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The current robotics narrative is heavily weighted toward artificial intelligence (AI). The prevailing assumption is that more parameters, larger models, and better reinforcement learning pipelines will eventually grant machines human like dexterity. This belief has shaped research agendas, funding priorities, and public expectations.

However, for engineers designing hardware that must survive millions of high-velocity cycles at companies like Amazon Robotics, a different truth is apparent. In the lab, the focus is on the brain, but on the production floor, robots fail for mechanical reasons far more often than algorithmic ones.

In high duty cycle environments, the primary drivers of unplanned downtime are wear, compliance, thermal drift, misalignment, and mechanical fatigue. These are not failures of perception or planning. No amount of neural network tuning can compensate for a linkage that deflects under load or an end effector that cannot maintain repeatability. As the industry continues to chase AI-centric solutions, it risks overlooking the fundamental engineering disciplines that determine whether a robot succeeds in the physical world.

The robotics community is at a crossroads. The last decade has delivered extraordinary advances in machine learning, but the physical reliability of robotic systems has not kept pace. The result is a widening gap between what robots can demonstrate in controlled environments and what they can sustain in real production settings.

Closing this gap requires a shift in mindset. The next leap in robotics will not come from larger models or more training data. It will come from better mechanisms, better actuation, and better physical architectures.

The reliability gap

The industry has spent a decade optimizing the brain while neglecting the body. This imbalance has created what can be described as the reliability gap. As a technical judge for MassChallenge and for university capstone programs at Worcester Polytechnic Institute and Boston University, I have observed a recurring pattern.

Startups and student teams often present systems that segment objects perfectly in simulation, classify scenes with remarkable accuracy, and demonstrate impressive reinforcement learning policies. Yet when these systems are deployed in the physical world, they fail after only a few hours of operation.

The reason is straightforward. AI amplifies a robot’s capability, but the mechanism defines the physical boundary. If a kinematic chain introduces unpredictable hysteresis, software cannot compensate its way to a reliable solution. If a transmission loses stiffness under load, no amount of perception accuracy will restore positional integrity. If an end effector cannot generate stable contact forces, even the most advanced grasping model will fail.

The robotics industry must acknowledge a practical reality. Software and AI are essential, but they cannot overcome fundamental mechanical limitations. The most successful robotic systems in history have not been those with the most advanced algorithms, but those with the most deterministic mechanical behavior. Reliability is not an emergent property of software. It’s engineered into the physical system from the beginning.

Determinism and the voyager philosophy

True industrial progress requires a return to mechanical rigor, specifically a focus on what can be called deterministic mechatronics. This philosophy suggests that the most successful robotic systems are those engineered for passive stability, predictable behavior, and graceful failure. A useful analogy comes from deep space engineering.

Voyager 1, launched nearly half a century ago, remains operational in one of the harshest environments imaginable. NASA has occasionally uploaded new command sequences, performed resets, and adjusted subsystems to extend its life. These interventions succeed because the underlying mechanical and electrical systems were engineered for extreme reliability. The spacecraft’s longevity is not the result of software alone or hardware alone, but the synergy between robust physical design and intelligent control.

Industrial robotics should adopt this same mindset. The next leap in automation will come from kinematic architectures that reduce inertia, precision transmissions that maintain sub-millimeter accuracy under load, and actuation strategies that prioritize physical determinism. The goal is not to diminish the role of AI, but to ensure that AI is built on a stable mechanical foundation.

A deterministic mechanism reduces the burden on perception and control. It narrows the solution space. It transforms a difficult control problem into a manageable one. When the physical system behaves predictably, the software becomes simpler, more robust, and more efficient.

Case study: The apparel challenge

The manipulation of non-rigid materials, such as apparel, provides a clear example of this principle. Handling folded fabric is traditionally viewed as an AI problem. The common assumption is that complex pose estimation, dense depth reconstruction, and advanced vision models are required to manage the noise introduced by folds and wrinkles.

However, breakthroughs in this field, including those protected under U.S. Patents 11268223 and 11939714, demonstrate that the solution is primarily mechanical. By designing a compliant yet deterministic gripping architecture, the physics of the material can be used to the machine’s advantage.

When the kinematic chain is engineered to minimize shear forces, the physical interaction becomes predictable. When the mechanism constrains the degrees of freedom in a way that aligns with the material’s natural behavior, the need for complex perception is reduced.

In these systems, AI still plays a meaningful role. It identifies features, guides sequencing, and handles variability. But it succeeds because the underlying mechanism provides a stable substrate. The machine does the heavy lifting so the software can remain efficient. This balanced approach is what the industry needs. Instead of using software to compensate for mechanical unpredictability, the mechanism is engineered to reduce the burden on software.

This approach scales. It is robust. It is repeatable. And it is the foundation on which industrial grade automation must be built.

A new hierarchy of design

To unlock the next stage of automation, the engineering community must rebalance its priorities. The hierarchy of design must shift.

First, the industry must invest in mechanism research and development with the same intensity it brings to AI. For every dollar spent on perception, equal resources should be allocated to transmissions, linkages, and end effectors. Mechanisms are not a solved problem. They are the frontier that will determine the next decade of progress.

Second, the industry must build reliability-first architectures. Robots should be engineered with the longevity of aerospace systems, not the lifecycle of consumer electronics. This requires a shift in mindset. Reliability is not a feature. It’s a design philosophy.

Third, the industry must foster a new breed of roboticists. The next generation of engineers must be equally proficient in kinematics and PyTorch, equally comfortable with finite element analysis and neural network training and equally invested in mechanical determinism and algorithmic efficiency. The future belongs to engineers who can bridge the physical and digital domains.

Finally, the industry must resist the temptation to chase demos. The goal is not to produce systems that perform well in controlled environments, but systems that operate reliably in the real world. The measure of success is not a viral video, but a robot that performs millions of cycles without failure.

The next decade of robotics

Artificial intelligence is an extraordinary amplifier, but it’s not the foundation of robotics. Intelligence can only be as effective as the physical vessel through which it acts. The next decade of robotics will be defined by the engineers who recognize that mechanisms, transmissions, and physical architectures are not secondary considerations. They are the core of the system.

The future of robotics does not belong to the AI-first approach or the mechanism-first approach. It belongs to the integration of both into a single, reliable, and deterministic system. When the body and the brain evolve together, automation will finally achieve the scale, reliability, and capability that the industry has been pursuing for years.

This is the mechanism-centric future of robotics. And it’s long overdue.

Santosh Yadav is senior mechanical engineer and robotics researcher at ASME MBE Standards Committee.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories
• Tale of 3 sensors operating in smart factory environments
• From edge AI to physical AI in smart factories: A shift in how machines perceive and act

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The evolution of automotive safety is moving from the exterior to the interior, opening a new frontier: in-cabin sensing. Its emergence marks a shift from passive vehicle shells to active systems capable of detecting and safeguarding occupants. However, implementing radar-based in-cabin sensing presents multifaceted engineering challenges, including privacy considerations, real-time data processing, and functional safety, all under strict regulatory umbrella.

Radar has become the preferred modality for in-cabin applications, offering privacy by design, effectiveness through interior materials, and immunity to lighting conditions. Crucially, it detects micro-motions such as breathing and heartbeat.

Why in-cabin sensing Is becoming mandatory

In-cabin sensing includes systems that monitor driver behavior, track occupant presence, detect vital signs, and recognize gestures within the vehicle. With the push for in-cabin sensing in response to global demand for higher safety standards, in-cabin sensing is moving from a “nice-to-have” to a “must-have” feature set.

Figure 1 In-cabin sensing is increasingly becoming a must-have feature in modern vehicles. Source: Cadence Design Systems

Tragic incidents involving children left in hot cars and drowsy driving have prompted regulators and safety organizations to act, making in-cabin sensing essential for top safety ratings.

Regulatory bodies are shifting focus from external crash prevention to interior safety measures. Programs like Euro NCAP’s Child Presence Detection (CPD), effective in 2025, and the U.S. Hot Cars Act highlight the importance of interior monitoring to prevent child fatalities and assess driver alertness. While traditional camera systems face privacy and lighting challenges, radar technology, especially 60 GHz frequency-modulated continuous wave (FMCW) radar, offers a superior, privacy-preserving solution for next-generation intelligent cockpits.

Why radar is emerging as a preferred modality

Radar technology offers a unique set of capabilities that make it the optimal choice for the complex environment of a vehicle cabin. Unlike cameras, which can be obstructed by poor lighting or raise privacy concerns, radar provides robust, non-intrusive sensing and offers many benefits.

Privacy by design

In an era where data privacy is paramount, radar offers a distinct advantage. It does not capture detailed visual images of faces or bodies. Instead, it detects presence and movement through point clouds. This allows the system to monitor occupants effectively without recording sensitive personal visual data, making it far more acceptable to privacy-conscious consumers.

Seeing the unseen (non-line-of-sight)

One of the most profound advantages of radar is its ability to penetrate materials. A camera cannot see a child covered by a blanket or sleeping in a rear-facing car seat obstructed by the driver’s seat. Radar, however, can detect the micro-movements of breathing or a heartbeat through clothing, blankets, and even seat materials (excluding steel). This non-line-of-sight (NLOS) capability is crucial for reliable CPD.

Environmental robustness

Radar is immune to lighting conditions. It functions just as effectively in pitch-black darkness as it does in blinding sunlight, ensuring continuous protection day or night. Furthermore, its performance remains robust despite temperature fluctuations, humidity, or vibrations—common factors in the automotive environment.

Why 60-GHz FMCW radar specifically?

As OEMs and Tier 1 manufacturers evaluate their platform choices, the FMCW-versus-ultra-wideband (UWB) debate often arises. While UWB has had success in consumer electronics and certain automotive access systems, FMCW radar aligns more naturally with the requirements of high-volume automotive in-cabin sensing deployments.

FMCW offers a lower cost structure, simpler integration path, and superior feature scalability. It supports multi-use sensing—from occupant monitoring and CPD to vital signs and gesture recognition—all within a unified signal-processing pipeline.

FMCW also avoids security challenges such as relay or “man-in-the-middle” vulnerabilities sometimes associated with UWB applications. Taken together, these factors make FMCW at 60 GHz the “sweet spot” for OEMs targeting a multi-model rollout between 2026 and 2030.

Challenges in engineering the intelligent cabin

Implementing radar-based in-cabin sensing is not without its challenges. It represents a multifaceted engineering hurdle that requires the convergence of precision sensors, high-speed signal processing, and functional safety compliance.

The processing challenge

Detecting the subtle rise and fall of a sleeping infant’s chest amidst the noise of a moving vehicle requires immense computational precision. The radar processing pipeline involves complex stages, including the Range FFT (Fast Fourier Transform), the Doppler FFT, and sophisticated clutter-removal algorithms.

Statistics show 99.9% accuracy in CPD using radar. To achieve this high accuracy, engineers must employ advanced digital signal processing (DSP) technologies. Solutions like the Tensilica Vision 110 DSP are designed specifically for these high-performance, low-power requirements.

Figure 2 Here is a radar processing pipeline for a child presence detection use case. Source: Cadence Design Systems

By offloading complex mathematical operations such as 8-bit and 16-bit MACs to a dedicated DSP, automotive designers can achieve the required frame rates (around 50 FPS) while adhering to strict power and thermal constraints.

Integrating AI and machine learning

The future of in-cabin sensing lies in the fusion of traditional signal processing with machine learning (ML). While traditional algorithms excel at determining distance and speed, ML is essential for classification. Is the object a bag of groceries or a child? Is the driver blinking due to fatigue or just natural movement? Object segmentation is performed by running AI models on a radar dataset.

Advanced radar architectures now support AI-driven classification, allowing the system to learn and adapt. This capability enables features like gesture recognition for touchless control of infotainment systems, adding a layer of comfort and convenience alongside safety.

Applications beyond safety: Comfort and autonomy

While safety mandates are the primary driver, the potential of radar-based in-cabin sensing extends well beyond user experience and autonomous operation.

Health and wellbeing

The sensitivity of 60-GHz radar enables vital sign monitoring. Systems can continuously track heart and breathing rates without physical contact.

Figure 3 This radar processing pipeline serves vital signs monitoring (HR/BR). Source: Cadence Design Systems

In the event of a medical emergency, the vehicle could detect the driver’s distress and autonomously pull over or alert emergency services.

Enhancing autonomy

As we progress toward L3 and L4 autonomy, the vehicle needs to know not just where it is, but also how its occupants are doing. In a handover scenario where the car needs the driver to take control, the in-cabin sensing system must verify that the driver is alert, present, and ready. Radar provides this verification reliably, acting as a core intelligence layer that builds trust in machine-driven environments.

Operational efficiency

For emerging mobility models like robotaxis, radar offers practical benefits. It can detect the number of passengers for billing purposes, ensure no objects are left behind, and even automatically manage trunk operation.

The silicon imperative: Efficient DSPs and AI at the edge

In-cabin radar workloads demand a unique blend of high-throughput DSP operations and compact neural-inference capabilities. Traditional MCUs lack the parallelism required for FFT-heavy pipelines, while dedicated NPUs often exceed cost and power envelopes for cabin modules. A new category of radar-optimized DSPs has emerged as the right balance—programmable, efficient, and capable of supporting both classical signal processing and radar-trained neural networks.

These processors must deliver high MAC throughput, robust SIMD capabilities, and efficient memory architecture while operating within tight thermal constraints. Their flexibility enables quick algorithmic iteration, which is essential in a domain where radar datasets continue to expand across body sizes, seating layouts, and vehicle architectures.

The road ahead

As vehicles advance toward autonomous operation, in-cabin sensing will become a core intelligence layer that predicts occupant needs, safeguards their well-being, and builds trust in machine-driven environments. The integration of radar into the vehicle cabin is redefining what it means to be safe on the road.

For automotive OEMs and Tier 1 suppliers, mastering scalable, radar-based sensing architecture is no longer optional, but is a determinant of future leadership. By leveraging powerful DSP platforms and embracing the unique capabilities of FMCW radar, engineers are not just meeting regulations; they are designing a safer, more intuitive driving experience.

The guardians are no longer just on the bumper; they are inside, ensuring that every journey ends as safely as it began.

Amit Kumar is director of Automotive Product Management and Marketing for Tensilica DSPs at Cadence. He has more than 20 years of design experience in the semiconductor and IP segments. Amit has held product marketing, application engineering, business development, and key strategic management roles with a specialization in automotive ADAS/AD and robotics applications.

Related Content

• Automotive: The latest on in-cabin sensing designs
• Partnering to Advance Automotive In-Cabin Sensing Tech
• In-Cabin Monitoring: Time-of-Flight and Radar Take the Wheel
• How In-Cabin Monitoring Solutions Contribute to Overall Vehicle Safety
• Advancements in radar technology and the evolution of in-cabin sensing

The post The guardians inside: How radar is redefining in-cabin sensing appeared first on EDN.

SmartClamp DrMOS power devices from AOS are designed for the demanding power requirements of AI servers and high-end GPUs. Each device is a synchronous buck power stage with two asymmetrically optimized high-side and low-side MOSFETs and an integrated driver. They provide precise 100-A positive and 50-A negative current limiting during high di/dt transients. The flagship AOZ53228QI extends protection to multiphase voltage regulators, helping prevent failures during frequent high peak-current events.

In AI applications, fast load transients can drive current beyond the limits of standard inductors and power stages. Conventional overcurrent protection schemes may introduce response delays that allow short current overshoot events, which can stress the high-side MOSFET, particularly under inductor saturation conditions.

The SmartClamp family mitigates this risk by implementing current limiting directly within the power stage rather than relying solely on the controller, improving response to load transients that occur in tens of nanoseconds. An internal ramp-based sensing method continuously monitors inductor current in real time, enabling cycle-by-cycle current clamping instead of reacting after fault conditions develop. Cycle-by-cycle control reduces the likelihood of inductor saturation and MOSFET overstress during AI-style burst loads.

SmartClamp devices, including the AOZ53228QI, AOZ53262QI, and AOZ53263QI, are available in production quantities with a 12-week lead time. The AOZ53228QI is priced at $1.40 each in lots of 1000 units.

Alpha & Omega Semiconductor 

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SiTime’s Elite 2 Super-TCXO family of oscillators delivers sub-nanosecond synchronization, increasing GPU utilization in AI clusters. By minimizing timing errors between GPUs, the devices boost throughput and performance per watt.

“Industry reports show GPU utilization in AI clusters can be as low as 20 to 40 percent—a large and largely hidden tax on AI infrastructure,” said Piyush Sevalia, chief business officer at SiTime. “AI workloads are distributed across GPUs in tightly orchestrated time slots. Even small timing errors force wait cycles to avoid data corruption, and in extreme cases can trigger GPU timeouts and system restarts. Poor synchronization directly caps GPU utilization.”

Emerging AI cluster requirements call for reducing timing errors to 10 ns, down from 1 µs today. The Elite 2 Super-TCXO achieves 1-ns synchronization accuracy—exceeding this target—with frequency slope as low as ±2 ppb/°C.

The series comprises four variants: SiT5234 and SiT5434, operating from 1 MHz to 60 MHz, and SiT5235 and SiT5435, operating from 60 MHz to 105 MHz. The SiT5234 and SiT5235 offer Allan Deviation (ADEV) of 1E-11, while the SiT5434 and SiT5435 achieve 6E-12. All oscillators are available in 3.2×2.5-mm plastic and 5.0×3.2-mm ceramic packages.

Elite 2 Super-TCXOs are sampling now, with commercial production expected in Q3 2026.

SiTime

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TVS diodes in the TPSMC, TPSMD, and TP5.0SMDJ series from Littelfuse provide standoff voltage ratings of up to 400 V in a single device. Compared to low- and mid-voltage TVS diodes that require multiple devices in series for adequate protection, this single-device approach reduces BOM costs and component count.

The TPSMC, TPSMD, and TP5.0SMDJ series deliver peak pulse power ratings of 1.5 kW, 3.0 kW, and 5.0 kW (10/1000 µs), respectively, with peak surge currents up to 300 A. Designed for automotive power electronics, the devices protect GaN/SiC MOSFETs and IGBTs in battery disconnect units, high-voltage HVAC systems, and PTC heaters from severe transients such as load dumps and other high-energy events.

These devices combine fast response times (typically <1 ps) for effective transient clamping with IEC-61000-4-2 ESD compliance up to 30 kV for robust system-level protection. AEC-Q101 qualification and PPAP capability support automotive reliability requirements, while the SMC (DO-214AB) surface-mount package minimizes PCB footprint and simplifies layout.

The TPSMC, TPSMD, and TP5.0SMDJ series are available in tape-and-reel format in quantities of 3000. Samples can be requested through authorized Littelfuse distributors worldwide.

Littelfuse

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R&S has extended its BBA300 family of broadband amplifiers with single-band models delivering 500 W and 1000 W P1dB RF output power. The BBA300-DE500 and BBA300-DE1000 cover 1 GHz to 6 GHz without band switching, improving efficiency in automated test environments. Optional BBA-PK1 software for the 500-W model enables bias point adjustment to optimize either linearity for complex signals or pulse fidelity, while providing a tradeoff between output power and mismatch tolerance.

Well-suited for automotive, aerospace, and defense applications, the solid-state amplifiers offer high availability and robust operation under mismatch conditions. They generate high field strengths for component and full-vehicle testing, as well as high-intensity radiated field (HIRF) testing. The amplifiers support a wide range of modulation types, from standard amplitude and pulse modulation to complex OFDM signals.

To achieve high power density, the compact modular amplifiers integrate into 30U racks preconfigured for direct horn antenna mounting. To reduce RF losses at high frequencies, the RF output is positioned centrally within the rack, minimizing cable length to the antenna and improving overall link budget.

Learn more about the BBA-300 family of broadband amplifiers here.

Rohde & Schwarz 

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The TS1800 platform root of trust controller and TS50x secure boot controller expand Microchip’s TrustShield portfolio of post-quantum cryptography (PQC)-ready devices. These ICs address emerging cybersecurity mandates, including the European Cyber Resilience Act (CRA) and Commercial National Security Algorithm Suite 2.0 (CNSA 2.0), across data center, compute, defense, and infrastructure systems.

Designed for external platform root of trust in multi-component systems, the TS1800 provides secure boot, secure firmware updates, attestation, and certificate handling using hardware-accelerated PQC. An Arm Cortex-M4F processor operating at up to 192 MHz provides up to 2× the processing power of previous generations to support the increased computational demands of PQC workloads. The controller also supports Open Compute Project (OCP)-compliant implementations, enabling firmware integrity validation and lifecycle management.

The TS50x series provides PQC-based secure boot for systems that do not require the full OCP-based platform root of trust feature set offered by the TS1800. With a simpler architecture, it focuses on signature verification using both PQC and classical cryptography for firmware stored in SPI flash. The controller holds the main chipset in reset until verification completes. This hybrid approach enables retrofitting existing ECC-based designs with PQC.

TS1800 and TS50x controllers and evaluation boards are available as part of Microchip’s early adopter program. 

TS1800/TS50x product page

Microchip Technology 

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This engineer no longer has a bulbous monitoring device attached to his chest. He’s transitioned to a svelte successor, in the same location but this time placed subcutaneously.

Thanks to all of you who wrote in expressing concern and well wishes subsequent to the publication of my previous two posts in this series, focusing on my recent cardiac issues. I’m happy to report that I successfully made it through the 30-day regimen with a function-tailored smartphone in my pocket and a monitor stuck to my chest . I’m also happy to report that my cardiologist’s analysis of the collected data revealed no serious ongoing concerns. That said, I’m not yet completely “off the hook”, therefore the topic of today’s follow-up writeup.

What the 30-day results did reveal were a few brief episodes of tachycardia, i.e., elevated heart rate and intensity sequences, albeit with a still-regular cadence:

As my cardiologist explained (and I now paraphrase), my heart seemed to be trying to go back into irregular rhythm but (thankfully) didn’t succeed. As such, he was of the opinion that I still should proactively have a cardiac ablation, but I’ve declined that option, at least for now.

During my mid-November episode, while the bulk of my arrythmia rhythm was classified as atrial flutter, which has a near-100% success rate even after only a single ablation procedure:

my heart also occasionally transitioned into atrial fibrillation (AFib), whose single-procedure success rate is lower, due in part to the larger number of impulse sites that typically need to be severed (subsequent repeat procedures bolster the chances of a successful eventual outcome):

Instead, what I proposed (and he eventually agreed to) was a more conservative approach, at least initially. I’d remain on rhythm-stabilizing beta blockers. And he’d embed a miniature leadless cardiac monitor, with three-year operating life, subcutaneously in my chest to enable ongoing logging of any further heart rate abnormalities. He’d then automatically receive a report from the service provider each month. If there was no further detected AFib or atrial flutter after the monitor’s integrated battery eventually died, I could declare an “all clear”, with the now-inert monitor potentially remaining in me for the rest of my life. And if any recurrence of irregular arrythmia did occur, we could revisit the potential ablation scenario.

Tiny but mighty

The system I’m now artificially augmented with—just call me Steve Austin—is from Medtronic. Specifically, it’s the first-generation Reveal LINQ, which has been in widespread use for more than a decade at this point. At its nexus is the model LNQ11 ICM (insertable cardiac monitor), now in residence in my chest, which required only a local anesthetic (lidocaine) and sub-1 cm incision for installation, along with a couple of internal dissolvable stitches and some glue to temporarily hold the incision flaps together for the first two weeks while it healed.

The ICM has dimensions of approx. 44.8 x 7.2 x 4 mm, translating to (at ~1.3 cubic cm) roughly 1/3 the volume of a AAA battery, and weighs around 2.5 grams. Here are some stock shots:

Wireless diversity

The ICM communicates with a standalone AC-powered patient monitor which receives transmissions from the ICM and passes them along to a “cloud” server over a cellular data link:

Here are the meaningful perspectives of the outer packaging I received post-ICM installation:

Opening up the box, there was (obviously) no longer an ICM inside; it had already been relocated to my skin’s underside, at the left pectoral region of my chest, to be precise:

The patient monitor is variously described as needing to be no further than either 2 or 3 meters away (depending on the literature piece being referenced) from the ICM-toting patient in order to ensure reliable data transfers:

The system manual (PDF) accessible (along with other useful info) via the patient portal provides detailed information on the divers spectrum swaths used for various ICM-to-patient monitor and patient monitor-to-cloud functions, along with their associated modulation schemes. The companion ICM manual (PDF) translates these technical specifications into “for the masses” cautions and broader recommendations for cardiac monitor operation in EMI-rich environments (motors, arc welders, radio transmitters, etc.) along with the information you should share beforehand with MRI scanner operators as well as airport and other security personnel (I carry a Medtronic-supplied info card in my wallet for situations such as these).

Speaking of spectrum swaths, the FCC certification ID for the ICM is LF5MEDSIMPLANT1; I encourage you to check out the FCC site for more interesting information on the device, including a set of teardown images. Even more interesting info can be accessed by punching other FCC IDs, found on product labels both above and below this point in the writeup, into the independently developed and maintained FCC certification website search engine.  And further to the spectrum swath topic, I’ll note that Medtronic has subsequently introduced the LINQ II ICM, similar in size (45.1 x 8 x 4.2 mm) and per my online research making several notable enhancements to the first-gen implementation:

• Like the 30-day cardiac monitor I described in my previous writeup, it communicates with the data receiver device over Bluetooth low energy (BLE), not the proprietary protocols leveraged with the first-generation ICM. As such, again as with the 30-day monitor I previously used, it can connect to a conventional smartphone versus requiring my dedicated bedside patient monitor device.
• Its BLE and smartphone intermediary foundations also enable it to be remotely reprogrammed by the cardiologist for settings fine-tuning purposes, versus necessitating an office visit for the patient.
• Estimated battery life is now 4.5 years.
• And the LINQ II is FDA-cleared for pediatric use with patients 2 years and older.

Selective storage and transmission

My previous cardiac monitoring device was bulky and required recharge every five days or so. How on earth, then, does this comparatively tiny ICM run for 3 years on a much smaller and non-rechargeable cell? Selectivity is one key differentiator; while the prior cardiac monitor was constantly logging heartbeat information, the ICM (automatically, at least; keep reading) only captures a data sequence when it senses there’s a potential arrhythmia event occurring, and cloud-based AI algorithms further weed out “false positives” before passing the information on to the cardiologist.

The ICM only houses enough onboard storage for 27 minutes’ worth of this auto-logged information. It’s what’s known as a “loop recorder”, overwriting old data with new, operating under the assumption that the old data has already been transferred to the patient monitor. Yes, this means that, as with my CPAP machine, I also need to travel with the patient monitor and its AC power adapter.

What happens if I’m symptomatic, suggestive of an in-process cardiac event; palpitations, dizziness, light-headedness, etc.? The answer to that question depends on whether my patient monitor is nearby. You may have already noticed in the earlier set of photos that the patient monitor appears to consist of two pieces, with the smaller portion sitting atop the larger base unit. Kudos on your insight: you’re right:

If the patient monitor is nearby when you find yourself in distress, you can detach the “reader” portion (which, perhaps obviously, contains an embedded rechargeable battery), place it on your chest directly above the implant area, and transfer the captured and “flagged” data for analysis by the cardiologist (who can also proactively reach out to you for an ad-hoc transmission of this same way, by the way, if he or she sees something awry in the auto-captured monthly report data).

And if you’re away from your patient monitor? That’s where the pocketable “patient assistant”, accompanied in the following photos by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes, comes into the picture:

Place it on your chest atop the ICM, punch the “record” button, LED light-confirm that the two devices are communicating and, later, that a successful sample has been captured, and the next time you’re nearby the patient monitor it’ll be priority-tagged and transmitted. The ICM contains additional storage sufficient for 30 minutes total (variously segmented) of patient-activated recordings, beyond the earlier-mentioned 27 minutes of auto-logged data.

I’ll pass along any other notable aspects of my “bionic augmentation” experience via this blog if/as I encounter them in the coming months (and years). For now, I welcome your thoughts in the comments on what I’ve shared so far!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• Wearables for health analysis: A gratefulness-inducing personal experience
• Cardiac monitors: Inconspicuous, robust data collectors
• Adventures with a remote heart monitor
• Heart rate monitor using a programmable SoC

The post Cardiac flutter(ing): Long-term monitoring appeared first on EDN.

Ultra-wideband (UWB) has moved well beyond research labs. Driven by IEEE 802.15.4z standardization and integration into smartphones from Apple, Samsung, and Xiaomi, UWB now underpins industrial real-time locating systems (RTLS), consumer keyless entry, and asset management platforms across multiple verticals.

For most of this adoption, time-of-flight (ToF) ranging has been sufficient, delivering approximately 10 cm accuracy in line-of-sight environments by measuring signal round-trip time. But system architects are increasingly moving to angle-of-arrival (AoA) techniques, which resolve the angular direction of a tag without requiring additional anchor nodes. AoA unlocks more efficient infrastructure layouts and opens new use cases in worker safety, autonomous robotics, and automotive access.

The shift exposes a hardware bottleneck that no amount of signal processing can fully compensate for: antenna isolation. AoA positioning relies on comparing the phase of a UWB pulse arriving at two closely spaced antennas.

If those antennas are mutually coupled—that is, insufficiently isolated—their signals contaminate each other. The resulting phase corruption introduces systematic angular errors that propagate directly into positioning accuracy.

Three design challenges facing UWB AoA antenna engineers

1. The –25 dB isolation threshold

Qorvo’s Application Note APH511—the widely referenced industry guide for AoA antenna integration—sets two non-negotiable requirements. Inter-antenna isolation must reach at least –25 dB across the full operating band, and physical antenna separation should be approximately 0.45 times the signal wavelength (λ).

For UWB Channel 9 (centred at ~7.987 GHz), that spacing equates to roughly 16.87 mm. Even at this theoretically optimal separation, raw isolation without dedicated decoupling structures typically falls short. The shortfall allows mutual coupling to corrupt the phase difference of arrival (PDoA) measurement on which AoA computation depends—and angular errors compound with distance.

2. Broadband impedance matching and pulse fidelity

UWB systems transmit sub-nanosecond pulses spanning hundreds of megahertz of bandwidth. An antenna that appears well-matched at a spot frequency can still distort pulse shape if its phase response is non-linear across the band.

Published time-domain evaluations indicate that group delay variation beyond approximately 1 ns degrades ranging accuracy even when return loss (S11) looks clean. Engineers must validate not just impedance matching, but pulse fidelity and group delay flatness—metrics that add complexity to an already demanding design process.

3. Size constraints vs. isolation performance

Industrial IoT tags, wearables, access cards, and consumer devices impose tight dimensional budgets. Conventional approaches to achieving strong inter-antenna isolation rely on enlarged ground planes or external RF filtering networks; both of which are incompatible with compact form factors. The result has been a persistent trade-off: high isolation or small size, but rarely both.

Chip antenna purpose-built for AoA

LK1820201 is an SMD chip antenna engineered specifically to address these barriers. Key specifications are summarized below.

Source: Leankon

Proprietary decoupling architecture

The central innovation is a proprietary decoupling structure that achieves inter-antenna isolation better than –25 dB between two co-located UWB antennas. In practical validation, a dual-antenna AoA array using the LK1820201 and its decoupling element measures –26 dB of isolation across the complete UWB Channel 9 band, confirming that performance holds across the full 6.0–8.5 GHz operating envelope, not just at a single center frequency.

This directly meets—and in practice exceeds—the Qorvo APH511 threshold, providing a solid electrical foundation for phase-coherent AoA computation.

• Ultra-low 0.5 mm profile

At 0.5 mm in height, LK1820201 is among the lowest-profile UWB antennas available in SMD chip format. This enables integration into slim wearables, access badges, compact industrial tags, and consumer devices without compromising mechanical design. Standard SMD reflow mounting eliminates the need for bespoke assembly tooling, reducing manufacturing entry barriers.

• Radiation pattern and power efficiency

Counter-intuitively for positioning applications, a lower peak gain paired with high radiation efficiency is generally preferred over a high-gain directional pattern. High efficiency distributes signal energy across a wide spatial angle, improving coverage at anchor installations and reducing dead zones for tags moving through complex indoor environments.

The antenna’s efficient radiation characteristic also reduces the transmit power burden on the UWB chipset—extending battery life in tags and wearables that must operate over weeks or months between charges.

Application areas

Centimetre-accurate UWB AoA positioning, enabled by high-isolation antenna pairs, is opening deployments across several industries.

• Industrial RTLS and worker safety: In manufacturing plants, logistics hubs, and construction sites, AoA allows a single anchor to resolve not just distance but the angular direction of a tag. This reduces the anchor infrastructure required for full coverage, lowering deployment cost for geofencing, collision avoidance, and emergency mustering systems.
• Healthcare asset tracking: Hospitals require continuous visibility into the location of mobile medical equipment—from infusion pumps to crash carts. UWB delivers the accuracy to track assets to the correct bay or room, without the ambiguity of Bluetooth RSSI-based systems.
• Automotive keyless access: Digital car key implementations use PDoA and AoA to determine whether a smartphone is inside or outside a vehicle—a security-critical distinction that RSSI cannot reliably make. Multi-channel support and high isolation performance are prerequisites for meeting the phase measurement accuracy demands of these deployments.
• Autonomous mobile robots: UWB AoA enables infrastructure-light follow-me navigation on autonomous mobile robot (AMR) platforms. By resolving both range and angle to a worker’s tag from a single onboard antenna pair, a robot can track a target in real time without requiring a fixed anchor network.

Design enablement and engineering support

Selecting a datasheet-compliant antenna is only the starting point. PCB stack-up decisions, ground plane geometry, feed trace routing, and antenna placement relative to metallic enclosures all interact with measured RF performance. Leankon supports the LK1820201 chip antenna with a design enablement program that covers:

• PCB layout recommendations optimized for isolation performance
• Antenna performance simulation services for pre-layout validation
• Mechanical design assistance for antenna placement within enclosures
• Fast prototyping services to accelerate design verification cycles
• Pre-test support for FCC, CE, and regional certification processes

This end-to-end support model reduces the engineering risk of adopting a high-performance UWB antenna and shortens the path from concept to production-qualified hardware.

Why AoA now

UWB angle-of-arrival positioning is a technically compelling evolution from range-only systems, but its precision depends fundamentally on solving the antenna isolation problem. For years, that barrier has limited AoA adoption to designs with generous PCB real estate or expensive external RF filtering.

Chip antenna changes the equation. By achieving better than –25 dB isolation from a 0.5-mm SMD package, supporting all major UWB frequency allocations from a single component, and simplifying BOM complexity for global deployments, it removes the principal hardware barrier to AoA in compact, cost-sensitive devices.

For IoT hardware engineers, RTLS platform developers, and device makers targeting precise indoor positioning, this antenna represents a technically meaningful step toward aligning hardware capability with the precision that modern UWB applications demand.

Chris Zhong, engineering manager at Leankon, leads the global antenna R&D team, overseeing both RF and mechanical design. With over 15 years of antenna design expertise, he specializes in 4G LTE, Bluetooth, 5G and mm-Wave, UWB, NFC, LoRa, and Wi-Fi technologies.

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• Ultra-wideband antenna arrays–The basics
• Ultra-Wideband Radar in Healthcare: A New Era of Non-Contact Sensing and Monitoring

The post UWB: Why angle-of-arrival positioning hinges on antenna isolation appeared first on EDN.

Op amps tend to make analog design easy. Maybe sometimes too easy?

Don’t get me wrong.  I like operational amplifiers.  Some of my best friends are op amps.  They embrace such a wide range of varied capabilities, including low noise, high power, micropower, zero-drift, RRIO, high speed, etc., that they’re easy to love.  They tend to make analog design easy.  Maybe sometimes too easy?

Wow the engineering world with your unique design: Design Ideas Submission Guide

This design idea applies the ΔVbe temperature measurement principle to make any cheap 3¾ digit digital multimeter with a 300mV range into an accurate, linear, 0.1°C resolution digital thermometer.  As a (hopefully) entertaining exercise, this time it does it without incorporating any op amps.  Here’s how it works.

ΔVbe temperature measurement is described and applied in an app note written by the famed analog design guru Jim Williams. See page 7 (PDF). Williams explains that the ΔVbe/°C effect depends solely on the ratio of applied currents, independent of their absolute magnitudes, and has an amplitude of 198μV per °C per current decade.  198uV=1V/5050, so 198μV/°C per current decade works out to ΔVbe/°C = Log10(Current-ratio)/5050.

Therefore, for any chosen ΔVbe/°C, the required Current-ratio = 10^(5050 Vbe/°C). So if we want ΔVbe/°C = 1mV, the solution couldn’t be simpler.  We “only” need to set Current-ratio = 10^(5050 * 1mV) = 10^(5.050) = 316,228:1.

Yikes!

The challenge, of course, is to achieve such an extreme current ratio. If the high side current were 1mA, then the low side would have to be very (very!) low indeed…like 1mA/316,228 = 3.2nA low.  This would involve Gohm current-setting resistors and circuit impedances in the multi-Mohm range.  So it’s not so simple after all and in fact is very likely impractical—without op amps, that is.

But consider this.  If it’s impractical to get enough ΔVbe signal from a single junction, why not wire N junctions in series and let their signals add up?  For example, if N = 5, then to get the required 1mV/5 = 0.2mV, we only need Current-ratio = 10^(5050 * 200uV) = 10^(1.01) = 10.23.  That ratio is highly practical.  It’s exactly what Figure 1’s circuit does, in fact:

Figure 1 Switch U1a and current mirror Q2Q3 apply an excitation current ratio of 10.23:1 to the 5 sensor transistor series array.  This creates a 5 x 200uV/°C = 1mV/°C AC signal synchronously rectified by U1c.

Circuit details include the D1R6 dummy load that serves to balance the currents passed by the two sides of the U1a switch, thus equalizing Ron voltage losses.  Current mirror aficionados (I’m looking at you, Ashu) will probably wonder how the Q2Q3 mirror, consisting of unmatched transistors with no emitter degeneration, can possibly have an accurate gain ratio?  The answer, of course, is: it doesn’t.  But that’s okay. It doesn’t need one.

Remember that Jim Williams said that the ΔVbe/°C effect depends solely on the ratio of applied currents, independent of their absolute magnitudes.  So the mirror’s gain can vary as it pleases without significantly affecting temperature measurement accuracy. Multivibrator U1b provides ~7kHz timing for synchronous sensor excitation and rectification with a ~33% duty factor.  This takes advantage of the 10x lower sensor array impedance at the high-current side of the excitation square wave.

If a more usual temperature readout in Celsius rather than Kelvin is desired, just plug the minus lead of the DMM into Figure 2 instead of ground, to offset 273K to 0°C:

Figure 2 This precision voltage reference converts Kelvin to Celsius.

Speaking of variations that don’t spoil accuracy, the V+ supply, for example, can vary from 5 to 6 volts without affecting accuracy.  Output impedance is roughly 2k, so variation of output loading by a typical 10M DMM input won’t impact accuracy, either. Who needs op amps, anyway?  (Not a serious question!)

Thanks, Jim!

Stephen Woodward‘s relationship with EDN’s DI column goes back quite a long way. Over 200 submissions have been accepted since his first contribution back in 1974.  They have included best Design Idea of the year in 1974 and 2001.

Related Content 

• ΔVbe + DMM = Celsius, Kelvin, Fahrenheit, and Rankine thermometer
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• Temperature compensation with a simple resistance temperature detector
• A temperature-compensated, calibration-free anti-log amplifier

The post ΔVbe thermometer outputs 1mV/°C without calibration or op amps appeared first on EDN.

Misbehaving buffer pointers, whose effects threatened to create a fatal project setback, were identified via a clever software subdivision technique.

In the 1990s, I was working as a motion control engineer for the Giant Meter Wave Radio Telescope Project (GMRT). The radio telescope consists of 30 giant meter wave antennas, each a parabolic dish 45 meters in diameter. The motion control electronics (i.e., the control computer and power electronics) were located inside a control room within the supporting tower below each antenna. The servo computer received motion control coordinates from a master computer situated in a central building via an optical fiber link.

Do you have a memorable experience solving an engineering problem at work or in your spare time? Tell us your Tale

After the first two prototype antennae were commissioned, the radio astronomers started using them for their observations. Whenever a celestial object is to be observed, the antenna has to move in opposition to the earth’s motion in order to remain focused on the object under observation. After a few weeks I received a phone call from the security guard manning one of the antennas. “The antenna was dancing madly,” the guard said. “I had to shut off the power supply! Please come down and investigate.” I reached the project site only to discover that the problem could not be reproduced.

This story repeated itself every few days, for both antennae in turn. The control system development team blamed the “dancing behavior” on erratic fluctuation with the rural electricity grid, suggesting that “if your grid bus voltage dances madly, the antenna will do the same.” However, I couldn’t “buy” this explanation. If it had been true, a repeated power on/off sequence could have reproduced the problem. But it didn’t.

The developers then handed me a 2,500 page printout of the source code, which was written in Turbo Pascal. Since I instead suspected a control software bug as the culprit, I was tasked with finding it. But how could anyone debug such a voluminous amount of software, written by multiple development team members, none of them myself? And what debugging tools could I use to track down an issue that occurs only once a few weeks? The situation appeared hopeless.

I decided to make use of the three LEDs located on the front panel of the servo computer, Each LED can have three states: on, off and blink. So we have cube of three combinations, 27 possible combinations in total. I divided the program into 27 different parts. A specific combination out of the 27 was therefore illuminated on the LEDs each time the associated code portion was being executed. I then asked the security guard to record the LED pattern being displayed every time the antenna was “dancing”, before he shut down power.

After only two or three iterations of the “dancing antenna event”, the culprit area of the program was identified, located within a two-page portion of the original 2,500-page source code printout. I was admittedly thrilled at the seeming magic of my debugging technique. The culprit program segment implemented a 128 byte circular communication buffer. When the master computer was issuing commands, the buffer would store them until the servo computer could execute them. Occasionally, however, the motion trajectory was so fast that the buffer would also rapidly begin to fill up.

In the worst-case scenario, the entire 128-byte buffer would become full. The buffer management routine maintained two pointers: a read pointer to the next command to be executed and a write pointer to the last location written. The pointers normally circularly wrapped around after reaching the 128th location. However, in this particular situation the read pointer was erroneously advancing to an invalid 129th location instead. No wonder it would then read a junk motion control command, resulting in the antenna “dancing” erratically!

I corrected the bug, to the delight of the other team members. The antennae had been running the risk of falling down during the “dancing”, leading to a fatal setback for our project. After more than three decades of development work, I have accumulated enough experience (and experiences) to come up with “life-saving” countermeasures for bugs such as these:

• Motion control software needs to carry out a “sanity check” before executing any motion command. Such a huge amount of inertia cannot be given a violent added acceleration beyond a reasonable threshold. Any command breaking this rule can be safely ignored, with an error subsequently flagged.
• A simple checksum for every command bit stream could have identified a “junk motion command” situation such as the one described here.

Our project received a prestigious IEEE Milestone Award a few years ago. Needless to say, if this difficult-to-find bug had not been identified and rectified, the project would not have even seen the light of the day, far from basking in the global good reputation it has achieved over the years among the international radio-astronomer research fraternity.

Vishwas Vaidya is a graduate of the Indian Institute of Technology in Delhi, India. Currently, he is self-employed as an engineering consultant and industry faculty member in the field of embedded systems for global automotive clients and high-repute academic institutions. Vishwas’ articles and research reports have appeared in many worldwide engineering publications.

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The post The curious case of the dancing antennae appeared first on EDN.

The concept of the “smart factory” has evolved significantly over the past decade. Early industrial AI deployments, often categorized as Industry 4.0, focused on centralized analytics. This typically involved collecting data from machines, transmitting it to the cloud, and generating insights for later action.

While useful for optimization and reporting, that model is no longer sufficient. What’s changing now is not just where AI runs, but how it operates—shifting from centralized analysis to systems that can perceive, decide, and act in real time within the physical environment.

Today’s factories demand intelligence that operates in real time, directly at the point of action. Whether detecting defects on a production line, coordinating robotic motion, or identifying safety hazards, AI is increasingly expected to function as an always-on, embedded capability within industrial systems.

This shift marks a broader transition in smart factories, from traditional edge AI toward more contextual awareness and autonomous operation: systems that not only analyze data, but perceive, decide, and act within the physical world. While the promise is substantial, realizing it introduces a new set of technical challenges that require purpose-built solutions.

Why edge AI Is moving closer to the machine in smart factories

Several converging forces are pushing AI workloads out of centralized infrastructure and toward the factory floor, where real-time interaction with physical systems is required.

Latency is among the most critical. In applications such as robotics, inspection, and safety monitoring, even small delays can result in defects, downtime, or safety risks. Round-trip communication to the cloud is often incompatible with these requirements. This is further compounded by the fact that many industrial environments operate with constrained, segmented, or variable network connectivity, making consistent low-latency cloud access difficult to guarantee.

Data volume is another key driver. Modern industrial systems generate vast streams of multimodal data—high-resolution video, audio signatures, vibration patterns, and increasingly, tactile inputs. Transmitting all of this data offsite is not only expensive but also unnecessary. In most cases, only a small fraction of events—such as anomalies, defects, or threshold violations—require action, making local inference far more efficient.

Figure 1 The transition from centralized AI to edge AI represents a fundamental shift in industrial computing. Source: Synaptics

Security and data sovereignty further make this trend important. Manufacturing processes and operational data are highly sensitive, and many organizations prefer to keep raw data within controlled environments.

The emergence of physical AI

On top of those factors, as AI moves closer to machines, its role is expanding. Instead of simply classifying or predicting, systems are beginning to interact with their environments in more dynamic ways.

This is the essence of physical AI in industrial systems, where they can:

• Interpret complex, multimodal sensory input in real time
• Adapt to changing physical conditions
• Execute actions with precise timing and coordination

Figure 2 The edge AI-enabled systems are now interacting with their environments in more dynamic ways. Source: Synaptics

Consider robotics as a leading example. Advances in tactile sensing now allow robotic systems to “feel” objects, adjusting grip force based on material properties. In one recent deployment developed with our partner Grinn, a robotic hand integrates distributed touch sensing with embedded machine learning, enabling nuanced manipulation of objects ranging from fragile materials to rigid components.

Such capabilities represent a shift from scripted automation to adaptive, context-aware behavior, bringing machines closer to human-like interaction with the physical world.

Key challenges in deploying edge and physical AI

Despite the momentum, implementing AI at the edge, and especially physical AI, presents several challenges.

1. Balancing performance and power

Industrial AI systems must operate continuously, often in constrained thermal and power environments. Unlike data centers, where peak performance is the primary metric, factory deployments prioritize sustained performance per watt.

Always-on workloads, for instance, predictive maintenance or safety monitoring, require efficient architectures that can run continuously without excessive energy consumption.

2. Managing workload diversity

Industrial AI is inherently multimodal. A single system may combine:

• Vision for inspection
• Audio for anomaly detection
• Vibration analysis for predictive maintenance
• Sensor fusion for robotics and control

These workloads have different computational characteristics, making it difficult to rely on a single type of processor. Increasingly, heterogeneous architectures that combine CPUs, GPUs, NPUs, and specialized sensors are required to efficiently handle diverse tasks.

3. Ensuring long-term reliability

Industrial systems often remain in operation for years or even decades. This creates unique requirements around:

• Silicon longevity and availability
• Stable software ecosystems
• Predictable behavior across revisions

Frequent hardware changes or software incompatibilities can disrupt operations and increase lifecycle costs.

4. Addressing model drift and lifecycle management

Unlike controlled lab environments, factories are dynamic. Lighting conditions change, materials vary, and equipment degrades over time. These factors can lead to model drift, where AI performance degrades after deployment.

Addressing this requires:

• Continuous monitoring and validation
• Local recalibration capabilities
• Secure, manageable update mechanisms

AI in industrial environments must be treated not as a static feature, but as a lifecycle-managed subsystem.

5. Integrating compute and connectivity

As systems become more distributed, the interaction between compute and connectivity becomes critical. Many manufacturers still rely on separate vendors for processing and wireless communication, leading to integration challenges and fragmented support models.

In physical AI systems, high-bandwidth, low-latency data movement between sensors, processors, and actuators is essential for safe and reliable operation.

The role of Wi-Fi 7 and next-generation connectivity

Connectivity is often a critical enabler of physical AI in smart factories, where real-time coordination between distributed systems depends on low-latency, high-reliability communication. As industrial systems scale in complexity and device density, traditional wireless technologies struggle to meet performance requirements.

Advancements in Wi-Fi and Bluetooth are addressing this, but wireless connectivity can no longer be viewed as a standalone, discrete capability. Without this level of connectivity, many physical AI use cases, particularly those requiring coordination across multiple systems, are not feasible.

There is a growing need, and clear benefits, in integrating processing and connectivity. This helps reduce system complexity, improve reliability, strengthen security, and simplify development for design teams.

Bringing together connectivity and processing changes how design decisions are made early in the product lifecycle. When core system functions work together, teams can simplify architecture choices from the outset and reduce the number of variables that typically slow progress.

Integrating connectivity and compute has benefits beyond the engineering and manufacturing phase. Over the lifetime of a product, integration helps reduce power consumption, lower device weight, and decrease overall system cost. At scale, even small reductions in size, mass, and power can translate into meaningful savings across production, shipping, and years of deployment.

Of course, wireless performance, range, and reliability are still critical in their own right. While existing Wi-Fi and Bluetooth standards have advanced the state of wireless connectivity, the emergence of Wi-Fi 7 introduces capabilities that enable more scalable and deterministic edge AI, supporting higher device densities and more predictable low-latency communication in smart factory environments.

• Multi-link operation (MLO) allows devices to transmit data simultaneously across multiple frequency bands. This provides redundancy and helps maintain consistent, low-latency communication even in environments with interference or congestion.
• Wider channel bandwidth (up to 320 MHz) supports high-throughput applications such as machine vision, where large volumes of image data must be transmitted quickly and reliably.
• Higher spectral efficiency (via 4K QAM) enables more devices to share the same wireless spectrum without degrading performance, an essential feature as industrial systems scale.

Toward a new system architecture

The convergence of edge AI, physical AI, and advanced connectivity is reshaping how industrial systems are designed, requiring more integrated and system-level approaches.

Some guiding principles to consider in developing such intelligent deployments are:

1. Start with system constraints

Rather than beginning with AI models, successful deployments start with system-level requirements:

• Latency and timing constraints
• Power and thermal limits
• Reliability and safety considerations

These factors should guide architecture decisions, including silicon selection and model design.

2. Embrace distributed intelligence

Instead of centralizing all processing, intelligence should be distributed across the system:

• Sensor-level processing for early data reduction
• Edge inference for real-time decisions
• Connection to cloud-based training and optimization for continuous improvement

This layered approach balances performance, efficiency, and scalability.

3. Design for multimodal integration

Physical AI systems rely on combining multiple sensing modalities. Architectures must support efficient data fusion and coordination across these inputs.

4. Treat AI as a lifecycle capability

Deployment is only the beginning. Ongoing monitoring, updates, and optimization are essential to maintaining performance over time.

The path forward

The smart factory is no longer defined solely by automation, but by intelligence embedded throughout the system, enabling decision-making that operates in real time, it adapts to its environment, and interacts with the physical world.

This transition from centralized AI to edge AI represents a fundamental shift in industrial computing. Performance and accuracy are still important, but what matters most is whether AI can operate reliably under real-world constraints: continuously, efficiently, securely, and in close coordination with physical processes.

Advances in heterogeneous computing, integrated connectivity, and open software ecosystems—as evidenced by AI-native platforms such as the Synaptics Astra Platform—are enabling this shift.

As these elements come together, the factory floor is becoming not just automated, but perceptive and adaptive, comprised of increasingly autonomous systems that do more than execute tasks; they understand context and respond accordingly.

Neeta Shenoy is VP of marketing at Synaptics.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories
• Tale of 3 sensors operating in smart factory environments

The post From edge AI to physical AI in smart factories: A shift in how machines perceive and act appeared first on EDN.

This audio-capture computer peripheral contains an integrated-transistor pickup capsule and a hunk of metal.

Back in November 2022, EDN published my introductory tutorial on standalone microphones—single- vs. multi-element, electret condenser vs. dynamic (including the associated necessity-or-not of a separate preamp), and analog vs. digital interface (and variants of each)—along with a separate piece on system-integrated mics a couple of months later.

I followed up those conceptual pieces with a USB-interface mic teardown in October 2023. And in both standalone-mic coverage cases, I mentioned (among others) one other USB-interface product, Blue’s (now Logitech G’s) Snowball, two examples of which were in my possession.

The Snowball, which supports both omnidirectional and cardioid pickup patterns, remains on my teardown pile. Stay tuned; it’s supposedly based on dual 14-mm electret condenser capsules, although there’s some controversy here, which I hope to sort out by putting my own eyes on the situation.

What we’re taking apart today is its spherical “little brother”, the cardioid-only Snowball iCE, which comes in both black and white color variants. I’ll start with some stock shots of my black-color ones, one of which I’ll be disassembling (non-destructively, hopefully).

Mine were a $40 (post-20%-off promo discount) two-pack ($20 each) bought from Woot in early 2024. Woot’s posting included a few other stock images I thought you’d find interesting.

Having a ball

While the mics themselves were brand new, their blank-cardboard and scant bubble wrap on-arrival packaging was definitely not retail-grade.

This last shot, along with others that follow it, as usual includes a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes.

I’ll start with the “extras”; a modest-but-functional tripod stand that screws into the mic underside, along with a legacy USB-A, to mini-USB cable and a sliver of literature.

Now for our dissection patient. Front:

Left side:

Rear, showcasing the aforementioned mini-USB connector (when’s the last time you saw one of those?) leveraged for both power and digital audio transfer purposes:

Right side, completing the circle:

And, last but not least, the top:

And bottom, showcasing the “adjustable desktop stand” mentioned in one of the earlier stock images (and implemented via a swivel mount in the microphone, mind you, versus anything to do with the stand itself):

For those of you curious about what the sticker circumnavigating the mic says, here are four consecutive segment snapshots for you to verbiage-glue together in your mind.

Severing the sphere

And now to get ‘er apart. In the earlier rear view, you might have noticed what looked like four screw holes, one in each corner. Kudos: you were right. It took me a bit of wading through my screwdriver collection to find one that:

• Had the right screw bit tip type-and-size
• With a bit that was both narrow enough to fit within the hole and
• Long enough to reach the screw heads deeply embedded inside

At that point, I expected the two halves of the sphere to neatly detach. But no. The previously mentioned sticker was still holding them together. There were two stickers, actually, as it turns out; the smaller one communicated device-specific info such as the serial number.

While the larger one handled the two-halves adhesion duties:

After I peeled it off, I thought its underside looked nifty and decided to share it with you, too.

And now the two halves of the sphere neatly detached:

FETalistic

Let’s first look at the moveable mount that fell out when the halves separated.

I trust many of you have already guessed that the red-and-black cable harness still connecting the two halves, which I promptly detached, is for the red LED. It only references the presence (or absence) of power to the microphone, by the way; there’s no integrated mute switch or any other reason for the LED to blink or otherwise communicate status.

There’s a notch in the internal assembly’s PCB that normally slots into a bracket at the inside back half of the microphone. With the two halves detached, the PCB slides out straightaway.

Assembly front view first:

Blue-now-Logitech claims that the 14-mm element is a “custom cardioid condenser capsule designed to deliver clear audio for recording and streaming, providing a significant upgrade over standard built-in computer microphones”. Marketing blah blah blah. Admittedly, it does review well, particularly considering its economical price tag. But its notable (IMHO) aspect, which I came across in my research, courtesy of a blogger who upgraded his, is its silicon integration:

The capsule in the Snowball is a 14-mm electret with an in-built FET that bears a striking resemblance to a JLI-140A-T. It uses a three-wire connection to the mic’s PCB, one each for the FET’s drain and source, and one for gate/ground. This means any electret with an in-built FET with all three pads brought out should work just as well (emphasis on “should”).

The fundamental purpose of the FET (alternatively a vacuum tube in some designs) is for impedance conversion and associated signal gain, thereby rationalizing why one well-known external mic preamp line is branded the “FetHead”. This thread on the Electrical Engineering Stack Exchange site gives a nice summary, complete with schematics and a conceptual diagram.

Heavy metal

Now for the left-side perspective:

Normally, when I see a hunk of metal, I assume that at least one of its primary purposes is to act as a heatsink. Not in this case. It just adds “heft” to the Snowball iCE, holding it in place on the user’s desktop (in partnership with the rubber-tipped stand “feet”) and suppressing ambient vibrations from being picked up by the capsule (along with the flexible rubber mount that mates it with the rest of the assembly). Here’s a bottom-side view, further showcasing the “hunk of metal”:

Back to the side views, next of the back of the assembly (with the mini-USB connector obscured by the ever-present penny, apologetically):

And finally, the right side:

Now for the perspective you all care about, that of the assembly-including-PCB topside:

Zooming in on the PCB itself, and after disconnecting the capsule cable harness:

The dominant IC on the landscape, toward the center of the PCB, is (unsurprisingly, given the mic’s digital output) the audio ADC-plus-USB interface device, C-Media Electronics’ CM6327A. This chip also embeds an I2C interface, harnessed in communicating with the Fremont Micro Devices  FT24C02A 2 Kbit serial EEPROM in the lower left corner (presumably housing system firmware).

In the spirit of thoroughness, and in closing, let’s take a peek at the PCB underside:

There’s nothing there that I can discern, other than test points, solder blobs and traces. In the interest of hopefully preserving mic functionality subsequent to re-assembly, I won’t proceed further with the dis-assembly. Sound off (bad pun intended) with your thoughts in the comments, please!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• Microphones: An abundance of options for capturing tones
• Microphones: On-PCB options for catching tones
• Disassembling an in-line microphone preamp
• Checking out a USB microphone

The post The Blue (now Logitech) Snowball iCE: This mic sounds nice appeared first on EDN.

The science of pH measurement has progressed from the crude color changes of lichen-based litmus paper to the precision of modern electronic meters. What began as a qualitative test has become a cornerstone of quantitative analysis, enabled by advances in electrode chemistry, signal conditioning, and digital display technology.

Today’s pH meters—combining robust sensor design with microcontroller-driven accuracy—are indispensable not only in semiconductor fabrication and pharmaceuticals but also in agriculture, aquaculture, food safety, and even everyday aquarium care. This evolution from natural dyes to digital readouts highlights how engineering ingenuity transforms simple chemical principles into reliable, scalable instrumentation across diverse fields.

Figure 1 The demo shows an advanced pH meter in operation. Source: Labo Hub

Understanding pH meters and their components

So, pH meters are electronic devices designed to measure the acidity or alkalinity of an object by detecting the voltage produced by a specialized sensor. They offer greater precision than pH paper or visual indicators, providing digital or analog readings that represent the hydrogen ion concentration in a sample.

A complete pH measurement system generally includes three essential components: a pH measuring electrode, which features a glass bulb highly sensitive to hydrogen ions; a reference electrode, which maintains a stable, known voltage; and a high-impedance meter, which amplifies and interprets the millivolt signal.

In modern applications, these components are frequently integrated into a single “combination electrode” for convenience and to enable measurements in smaller sample volumes. The pH electrode behaves like a tiny, ion-sensitive battery, producing a voltage that varies with the hydrogen ion activity across the glass membrane, while the reference electrode remains constant and serves as a stable comparison point.

In other words, the glass-electrode method works by comparing the voltage generated between two electrodes: the glass electrode and the reference electrode. The known pH of an internal reference solution is established, and the difference in potential between the two electrodes is measured.

This potential arises because the thin glass membrane of the electrode allows hydrogen ions to interact with a hydrated gel layer on each side, creating an electromotive force proportional to the difference in pH between the internal solution and the external sample. This thin barrier is known as the electrode membrane.

Put simply, the glass electrode is designed to generate an accurate electromotive force that reflects pH differences through the surface ion exchange, while the reference electrode is engineered to remain stable and unaffected by pH, serving as a reliable comparison point.

Figure 2 here is an educational pH sensor suitable for laboratory experiments and demonstrations traditionally performed with a pH meter. Source: Vernier

It is worth noting at this point that a pH electrode, a pH sensor, and a pH meter are closely related yet distinct components of pH measurement systems. The electrode serves as the sensing element, the sensor is the complete assembly that incorporates the electrodes and housing, and the meter is the instrument that amplifies, interprets, and displays the measurement.

In some modern designs, pH sensors also include integrated electronics that provide signal conditioning or temperature compensation, making them more versatile and easier to interface with digital instruments.

A brief note on the rise of ion-sensitive field-effect transistor (ISFET) technology: Traditional glass electrodes rely on a delicate bulb, but ISFET technology replaces the glass membrane with a solid-state semiconductor. In an ISFET sensor, the gate of a transistor is exposed directly to the solution. As hydrogen ions accumulate on the gate surface, they alter the electrical current flowing through the transistor.

This “glass-free” design offers significant advantages for the food and beverage industry, as it removes the risk of glass fragments contaminating a production line. Moreover, because ISFET sensors are manufactured using silicon-based processes, they can be miniaturized into tiny, “lab-on-a-chip” devices for real-time medical monitoring.

Buffer solutions and electrode choices

Buffer solutions remain the backbone of accurate pH measurement, providing stable calibration points and resisting shifts when acids or bases are introduced. Electrode material selection is equally critical: glass electrodes deliver high precision but are fragile, plastic electrodes trade sensitivity for ruggedness in field or teaching labs, and PTFE electrodes excel in corrosive industrial environments with their chemical resistance.

Specialized designs such as the quick-response probe (QRP) extend performance with faster response times and robust construction, making them well suited for rapid testing scenarios.

Reference electrolytes and junctions

In any pH electrode system, the reference half-cell is just as critical as the sensing element. The reference electrolyte, commonly potassium chloride (KCl), provides a stable ionic environment that maintains electrical continuity with the solution being measured. The reference junction serves as the interface, allowing ions to flow between the reference electrolyte and the sample solution.

Junction design directly affects measurement stability: porous ceramic junctions are widely used for general laboratory work, polymer or plastic junctions offer durability in rugged applications, and PTFE junctions resist fouling in viscous or dirty samples. Advanced junctions, such as double junction designs, minimize contamination of the reference electrolyte and extend electrode life, making them especially valuable in industrial or biological environments.

Calibration and real-world pH values

Accurate pH measurement hinges on proper calibration, typically performed at 25°C using standard buffer solutions at pH 4.00, 7.00, and 10.00 to span the acidic, neutral, and basic ranges. These points anchor electrode performance across diverse applications.

In practice, pH values vary widely: drinking water sits near neutral (~7), milk is slightly acidic (~6.5), soft drinks fall between 2 and 4, seawater averages around 8, and soaps or detergents trend alkaline (9–11). Such examples underscore why calibration across multiple buffer points is essential; electrodes must remain accurate whether measuring beverages, biological samples, or industrial solutions.

Figure 3 Datasheet snippet presents the technical parameters of a pH electrode, a high-quality sensor for analyzing liquid solutions in industrial automation, with applications spanning chemical processing, petrochemicals, semiconductors, biotechnology, and wastewater treatment. Source: Supmea

Signal parameters: Understanding your pH probe

Whether you call it a probe, sensor, or electrode, your pH device relies on a measurable slope to convert electrical signals into pH values. At 25°C, a perfect electrode produces 59.16 mV per pH unit, but real-world sensors typically achieve about 98% of this efficiency.

As the glass ages or becomes contaminated, the electrode slope declines, signaling the need for cleaning or replacement. Moreover, the mV change per pH unit is temperature-dependent, varying with sample conditions.

Unlocking innovation: Crafting your own pH meter

Building a pH meter from scratch can be challenging, especially since manufacturers closely guard electrode designs. Yet, innovation thrives on resourcefulness. You do not need to master glassblowing to succeed—DIY kits provide the specialized components that make it possible to assemble an experimental meter and bring your project to life.

For those aiming to push their designs further, dedicated analog front-end (AFE) ICs open up exciting analytical-sensing applications. These chips streamline the process of handling delicate electrode signals, offering precision amplification, filtering, and conversion. By integrating AFEs, experimenters can transform a basic DIY setup into a robust instrument capable of reliable measurements across research, industrial, and educational contexts.

Figure 4 A legacy reference circuit from 2013 demonstrates a completely isolated low-power pH sensor signal conditioner and digitizer with automatic temperature compensation for high accuracy. Source: Analog Devices Inc.

Equally important are today’s temperature sensors, which ensure accurate compensation for thermal effects in pH readings, and solid-state pH sensors, which provide rugged, low-maintenance alternatives to traditional glass electrodes. Combined with the accessibility of general-purpose hobbyist microcontrollers and single-board computing platforms, makers now have a powerful ecosystem at their fingertips.

This synergy of specialized ICs, modern sensors, and affordable computing hardware empowers innovators to bridge the gap between DIY experimentation and professional-grade instrumentation.

Take the leap

Well, take the leap from experiment to innovation. The tools are here, the components are accessible, and the knowledge is within reach. Whether through DIY kits, AFEs, modern sensors, or hobbyist computing platforms, the path to building your own pH meter has never been more open.

Start experimenting today; turn curiosity into creation, and creation into innovation. Most importantly, embrace mistakes because they are the fuel for progress.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

Related Content

• Convert your DMM to a pH meter
• How IoT offers a transformative solution to water management
• Revolutionizing Precision Farming with Wireless Embedded Systems
• A portable health monitoring system using a PSoC Mixed Signal Array
• Reliable and power-aware architectures: Measurement and modelling

The post From lichens to digits: The evolution of electronic litmus paper appeared first on EDN.

Sensors—on the front lines of the technological revolution in factory automation—are embedding microprocessors, memory, and communication protocols within the ASIC to offer multiple functions in a single chip, facilitating a new generation of smart factory applications spanning from asset monitoring to industrial robots to manufacturing quality control.

Here is a sneak peek at three sensor designs that enable factory automation applications while ensuring productivity and safety in industrial environments. These sensor designs also incorporate a variety of interfaces to support a wide range of smart factory capabilities.

1. Depth sensor for automation and robotics

This real-time, indirect time-of-flight (iToF) sensor delivers high precision for long-distance measurements and 3D imaging of fast-moving objects. The Hyperlux ID family of depth sensors from onsemi can capture an entire scene and simultaneously process depth measurements in real time.

The sensor combines global shutter architecture with iToF technology to deliver precise, rapid depth sensing. The iToF technology enables it to measure depth by detecting the phase shift of the reflected light from one or multiple vertical-cavity surface-emitting lasers (VCSELs). And the global shutter technology aligns all sensor pixels with the VCSEL, significantly reducing ambient infrared noise from other lighting sources.

Figure 1 The iToF device further extends depth sensing under dynamic scene conditions while capturing fast-moving objects. Source: onsemi

The company claims that the device’s depth-sensing capability of up to 30 meters is 4 times that of standard iToF sensors. Moreover, the sensor can produce both monochrome (black-and-white) images and depth information simultaneously.

That’s vital in factory automation, where the ability to obtain highly accurate depth information quickly and efficiently is becoming critical to improve productivity and safety. So far, iToF sensors have been limited in their use due to minimal range, poor performance in harsh light, and inability to calculate depth on moving objects.

By providing precision measurements of moving objects and high-resolution images, the Hyperlux ID sensors can help reduce errors and downtime and optimize mission-critical processes in a smart factory. In factory automation and robotics, it facilitates object detection to improve navigation and collision avoidance, enhancing safety on factory floors.

Next, in manufacturing and quality control, this depth sensor can measure the volume and shape of objects, detect defects, and ensure that products meet quality standards. In logistics and material handling, the sensor can measure the positions, sizes, and content ratios of pallets and cargo to optimize storage and transportation processes.

2. The AI-enabled IMU

An inertial measurement unit (IMU) with two MEMS accelerometers and a gyroscope tunes this sensing device for activity tracking and high-g impact measurement in smart factory applications such as asset monitoring and event data recorders. The IMU also embeds AI processing—a machine learning core—to perform inference directly in the sensor, continuously registering movements and impacts.

STMicroelectronics’ LSM6DSV320X sensor module, available in a single package, comprises three MEMS sensors. One accelerometer, featuring a maximum range of ±16g, is optimized for robust resolution in activity tracking. The second accelerometer, measuring up to ±320g, quantifies severe shocks such as collisions or high-impact events. Then there is a gyroscope with a ±4000dps range.

Figure 2 The sensor module for industrial safety comprises two accelerometers and one gyroscope. Source: STMicroelectronics

The 3-mm x 2.5-mm sensor module enables smart factory applications—such as personal protection devices for workers in hazardous environments—to fully reconstruct events with high accuracy and assess the severity of factory-floor incidents. The inertial module with dual-sensing capability could also be used to accurately assess the health of factory equipment.

3. Sensor signal conditioner

This signal conditioning IC ensures high accuracy, sensitivity, and flexibility for sensor applications in industrial pressure transmitters, HVAC systems, weight scales, factory automation devices, and smart meters. The ZSSC3240 sensor IC’s flexible configuration makes it highly suitable for smart sensor-based devices for smart factory environments.

Figure 3 The signal conditioner provides higher flexibility for sensor adaptation in smart factory applications. Source: Renesas

Generally, micro-machined and silicon-based sensing elements produce mostly nonlinear, very small signals. And that calls for special technologies to convert the sensor signal into a linearized output.

Renesas’ ZSSC3240 signal conditioning IC facilitates both the design and production of sensor interfaces by providing programmable, highly accurate, wide-gain, and quantization functions, combined with powerful, high-order digital correction and linearization algorithms. So, with a flexible sensor front-end and a broad range of output interfaces, it allows design engineers to develop complete sensing platforms from a single signal conditioning chip.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories

The post Tale of 3 sensors operating in smart factory environments appeared first on EDN.

Power-supply inductors may be supplanted by piezoelectric energy-storage elements…maybe. And someday.

Today’s step-down DC/DC converters – often converting 48 V down to single-digit voltages — are highly refined topologies, offering efficiencies of 90 percent and higher. Designers can choose among many switched-mode power supply (SMPS) arrangements, each with various subtleties to maximize a desired attribute such as efficiency, transient response, line and load regulation, or output noise. One bill of materials (BOM) aspect that all of these designs share is consistent reliance on magnetics in the form of inductors, to store and release energy as needed during the various operating phases.

But it doesn’t have to be magnetics. A team at University of California at San Diego has developed what they call an “Always-Multi-Path Embedded Flying Capacitor Piezoelectric Resonator-based DC/DC Converter” (that’s a mouthful!) that adds hybrid, multi-path, output-power delivery features to reduce the internal charge-redistribution losses within a piezoelectric resonator.

Their integrated circuit modifies the optimal voltage conversion of the piezo network from 2:1 to 3:1, while adding a switched-capacitor output network and piezoelectric resonators (PRs) to enable continuous multi-path operation. The result is net optimal voltage conversion ratio of 9:1 for the converter. The chip, which is fabricated in a 180-nanometer high-voltage CMOS process, achieves a peak efficiency of 96.2% at a 48-to-4.8 V conversion ratio.

The “flying capacitor” concept itself is not a new development at all; they have been around since the “early days” of electronics. In a classic arrangement, a non-grounded, floating capacitor is first connected to an input source and charged, then it is disconnected for that input and switched to an output to discharge (Figure 1).

Figure 1 The flying capacitor scheme was originally used with electromechanical relays to isolate a signal or power source from the subsequent stage. (Image source: InsightCentral.net)

Also called a switched-capacitor arrangement, it was used for many years to galvanically isolate sensors with electromechanical relays for switching, while modern switching supplies use MOSFETs and other solid-state devices. The switching scheme has also been used in multistage step-up circuits which can deliver thousands of volts from a single-volt source (Reference 3).

What’s wrong with inductors, and why consider using piezoelectric resonators? Inductors are versatile and reliable, but converters using piezoelectric resonators — tiny devices that store and transfer energy using mechanical vibrations — could potentially be smaller, more energy dense, more efficient and easier to manufacture at scale (Figure 2). The UC-SD team claims that inductors have reached a limit in improvement with respect to size and storage density (I suspect inductor vendors would disagree with that assessment).

Figure 2 A piezoelectric resonator (white disk) used by the new chip to perform DC-DC step-down conversion. For comparison, an inductor that is typically used in traditional step-down converters is shown on the left. (Image source: University of California)

Unlike inductors, which store energy in magnetic fields, PRs store and transfer energy through mechanical deformation and piezoelectric effects. They offer several advantages over traditional magnetic devices, including reduced volume due to their thin planar form factors, superior volume-frequency scalability, the ability to be easily batch fabricated, and their potential for direct integration onto silicon chips in future work. The high coupling and quality factor (Q) of PRs makes them attractive when designing high-efficiency, high-performance power systems, especially in the context of next-generation power conversion technologies.

Not surprisingly, an off-the-shelf PR is not suitable for this application. Commercially available units are not optimized for power applications and cannot operate robustly at the high current demands of modern datacenters. Further, the maximum current-carrying capability of a PR is determined by its physical properties such as material, vibration mode, and geometrical design, as well as electrical excitation strategies. For these and other reasons, the team designed a custom PR unit (Figure 3).

Figure 3 The custom piezoelectric resonator (right) overcomes limitations of commercial ones; the resonator size (left) is shown compared to a penny. (Image source: University of California)

Final performance is characterized by many different parameters under different operating conditions, such as those in Figure 4:

Figure 4 Fabrication and measurement images in abundance augment your knowledge base: a) Silicon die photo of the proposed converter; b) Measured waveform of each side of the PR, its differential voltage (VCP), and output voltage under voltage conversion ratio (VCR) = 10 and VCR = 20; c) Efficiency curve versus load current with fixed VCR (=10); d) Efficiency curve versus VCR with fixed load current (=200mA); e) Output current versus operation frequency, where the frequency operates in the inductive region of the PR. (Image source: University of California)

The team does acknowledge some limitations. Because piezoelectric resonators physically vibrate, they cannot be soldered onto circuit boards using conventional approaches and will require different strategies to integrate them into electronic systems. Although the technology is still in its early stages, the researchers say it represents an important step toward overcoming the limitations of today’s power converters. Future work will focus on improving materials, circuit design and packaging

As project senior author Patrick Mercier, professor in the Department of Electrical and Computer Engineering at the UC San Diego Jacobs School of Engineering noted, “Piezoelectric-based converters aren’t quite ready to replace existing power converter technologies yet. But they offer a trajectory for improvement. We need to continue to improve on multiple areas — materials, circuits and packaging — to make this technology ready for data center applications.”

Will this new approach get some traction? I don’t know, nor does anyone. After all, when optimized magnetic-based converters already have efficiency in the 90-95% range along with other favorable attributes, the pain needed to get another point or fraction of a point of improvement may not be worth the gain. On the proverbial other hand, a reduction in size or cost, even at the same efficiency, may be worthwhile.

Their paper “A hybrid piezoelectric resonator-based DC-DC converter” was published in Nature Communications but is behind a paywall; however, the team has posted a preprint here.

References

1. Knowles, “What Are Flying Capacitors?”
2. Insight Central, “Flying Capacitor High Voltage Battery Monitor”
3. EE World Online,  “Generating really high voltages without a tesla coil”

Related Content

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• Piezo audio devices go micro, provide output in a big way
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Part 1 of this mini-series about PCIe 7.0 fundamentals explained ordering rules and the distinction between relaxed ordering and ID-based ordering. Part 2 elaborates why PCIe 7.0 bandwidth alone isn’t enough and how UIO addresses legacy ordering limitations in this version of high-speed serial interface specification.

As noted earlier, PCIe 7.0 doubles raw link bandwidth compared to PCIe 6.0, increasing full‑duplex throughput from 256 GB/s to 512 GB/s on an x16 link by raising the signaling rate to 128 GT/s in flit mode. However, raw bandwidth does not directly translate into sustained throughput in AI factories.

Large‑scale training and inference systems generate traffic patterns such as GPU collective operations, sharded parameter broadcasts, gradient reductions, and streaming access to disaggregated accelerator and memory resources. These patterns include many independent data streams that cross the PCIe fabric concurrently and continuously.

The legacy ordering model inherited from earlier PCIe generations, including strict ordering, relaxed ordering, and ID‑based ordering, was designed around a producer-consumer abstraction in which ordering conveys semantic meaning to software. Relaxed ordering and ID-based ordering loosen this model selectively.

Relaxed ordering allows certain transactions to bypass global ordering constraints, while still participating in fabric‑enforced ordering rules. ID-based ordering further scopes ordering guarantees to a requester or execution context, preserving program order within that scope. In both cases, the PCIe fabric requires tracking and enforcement of ordering relationships to ensure correctness.

However, fabric‑enforced ordering introduces head‑of‑the-line blocking, increases buffering pressure, and restricts the ability of switches and endpoints to exploit parallel paths. This is particularly the case for multi‑path and non‑tree topologies common in modern AI systems. These effects reduce effective link utilization even though physical bandwidth is available, making it difficult for highly parallel AI workloads to keep PCIe 7.0 links continuously busy.

Addressing legacy ordering limitations with UIO

The unordered I/O (UIO) engineering change notice (ECN) was introduced in the PCIe 6.1 specification and included in PCIe 7.0 to address the specific limitation noted above. UIO introduces a wire-level semantic that shifts producer-consumer ordering responsibility from the fabric to the endpoints. The UIO ECN declares that ordering may be irrelevant for certain traffic classes.

For AI factory workloads, where operations such as reductions, parameter streaming, and telemetry are independent or statistically aggregated and never consumed in program order, enforcing any form of ordering (even per‑ID ordering) adds overhead. UIO removes fabric‑enforced ordering, enabling true multi‑path parallelism and reducing buffering requirements.

This allows PCIe fabrics to sustain higher utilization for concurrent AI traffic. Since UIO enables independent transactions from different request originators to bypass one another safely, AI systems can optimize PCIe 7.0’s increased bandwidth to support rapidly growing model sizes and highly parallel GPU workloads.

UIO is especially effective at reducing read latency because multiple UIO read completions for a single UIO read request may be returned in any address order. This same flexibility applies to UIO write completions, with the additional capability that write completions for the same transaction ID may be coalesced. Since every UIO request has a corresponding completion, the request originator maintains the ordering of its own transactions. This allows the PCIe fabric to forward traffic along multiple paths without violating semantic correctness.

With its low latency, UIO transforms PCIe fabrics into high-throughput, highly parallel forwarding planes capable of accommodating modern AI workloads. Instead of relying on the fabric to manage per-flow sequencing, UIO shifts ordering control back to the source device that initiates the requests.

How UIO reduces latency and unlocks concurrency in AI applications

UIO’s command set and wire semantics reduce latency and boost performance for AI training and inference in several ways.

First, UIO mandates completions for all UIO requests. This gives GPU endpoints precise end-to-end flow control and prevents posted-write “fire and forget” bursts from clogging switch queues. It also cuts head-of-the-line blocking and shortens tail latency, speeding up requests by allowing different types of requests to bypass each other without applying any ordering rules within the PCIe fabric.

One of the classic head-of-the-line blocking examples in the baseline strict ordering rule is that current read requests are not permitted to bypass previous write requests. UIO eliminates this rule, allowing read and write requests to be processed in parallel and completed in any order, as shown in Figure 1.

Figure 1 UIO read and write requests are processed in parallel at the application layer. Source: Cadence Design Systems

In addition, UIO read requests reduce latency and buffering by allowing a completer to return read completions out of order. This enables data to be delivered as it becomes available, rather than delaying responses to preserve requests or address ordering. This improves overall efficiency by giving the device greater freedom to exploit internal data availability and minimizing completion queueing and reassembly overhead.

For example, Figure 2 and Figure 3 show the completion patterns for a single 512 MB MRD request for non-UIO (in-order) and UIO (out-of-order) cases, respectively.

Figure 2 Non-UIO completion responses must be in order for the same MRD request. Source: Cadence Design Systems

For non-UIO, Figure 2 illustrates that completions must arrive in order, starting at byte 0 and ending at byte 511. However, with UIO, the completion order can be random, as shown in Figure 3. The first two completions carry the last two chunks of MRD requests (256-383B and 384-511B) because they are already available in the local cache. After that, the application reads the remaining completion data from its local memory and sends the remaining two completions (0B-127B and 128B-255B).

Figure 3 UIO read and out-of-order completion responses are processed for the same request. Source: Cadence Design Systems

Second, because ordering is enforced at the source rather than at every intermediate hop, packets from unrelated GPU streams can be load-balanced across multiple parallel paths through the PCIe fabric without being serialized by switch-level producer-consumer rules. This increases effective throughput at a given link rate and stabilizes latency underload. In multi-path topologies, system architects often use a non-transparent bridge (NTB) to connect separate systems, enabling cross-system traffic within a larger fabric.

Third, UIO is available only in flit mode. Operating in fixed-size flits with UIO-specific VC3VC4 (via the streamlined virtual channel capability) isolates UIO traffic from legacy flows, minimizes delays, and improves switch buffer utilization.

Figure 4 The above diagram displays a multi-path application example. Source: Cadence Design Systems

Figure 4 shows two interconnected PCIe systems (System 0 and System 1), each with GPUs and local PCIe switches connected via multiple NTB links. The upper NTB link can operate with either UIO-enabled or non-UIO-enabled traffic, while the three diagonal and lower links operate with UIO-enabled NTB.

As a result, independent transactions can flow concurrently across switches SW0–SW3. This topology shows how UIO-based NTB paths improve GPU communication by enabling multipath routing, reducing latency, and increasing bandwidth in large-scale AI systems.

PCIe ordering: A traffic light analogy

A helpful way to think about PCIe ordering is traffic control in a city. Strict ordering is like running the entire city with a single traffic light, and every vehicle must wait its turn and proceed in sequence. While there is no ambiguity, congestion can quickly build up. Relaxed ordering allows certain vehicles to pass through intersections in specific emergency situations, provided it is safe to do so.

While this removes unnecessary traffic jams, it still assumes the traffic system is centrally managed. ID-based ordering further refines this model by assigning each neighborhood its own traffic lights. While cars within the same neighborhood must obey local ordering rules, traffic from different neighborhoods can flow independently. This improves parallelism without sacrificing local correctness.

UIO bypasses traffic light rules entirely. It is akin to routing traffic onto a freeway, where there are no intersections or signals at all, and vehicles move continuously as capacity allows. On a freeway, the infrastructure does not impose sequencing. Instead, the responsibility for safe merging and interpreting arrival order shifts to drivers.

Similarly, with UIO, the PCIe fabric no longer enforces producer‑consumer ordering or completion sequencing. The requester explicitly declares that ordering carries no semantic meaning, allowing the fabric and devices to deliver and complete transactions opportunistically. This maximizes parallelism while minimizing buffering and latency.

These four ordering schemes are a progression rather than a set of alternatives. Strict ordering prioritizes safety and simplicity, while relaxed ordering removes unnecessary global barriers. ID-based ordering preserves correctness within a context while enabling scale, and UIO explicitly abandons ordering when it has no value. This layered model allows PCIe to remain compatible with legacy software while scaling efficiently for modern accelerators, multi‑queue devices, and highly parallel workloads.

Turning PCIe bandwidth into system-level performance

Fully utilizing PCIe 7.0’s 128 GT/s link in today’s AI factories requires more than higher signaling rates. In an environment where thousands of GPUs, accelerators, and memory expanders operate as a single, distributed system, an ordering model that can scale with extreme parallelism is necessary.

Legacy relaxed ordering and ID-based ordering schemes retain implicit ordering constraints that limit their efficiency at PCIe 7.0 speeds, making them increasingly inadequate for AI factories operating at hyperscale.

UIO relaxes fabric‑enforced ordering and enables AI workloads to more effectively utilize multi‑path PCIe fabrics. By shifting ordering decisions to endpoints that already manage synchronization at the runtime and application levels, UIO reduces ordering-related head-of-the-line blocking issues.

Not only does this improve latency under bursty collective traffic, it also supports higher sustained link utilization across dense training and inference clusters. The result: Under AI workloads, PCIe 7.0 can be used more efficiently as a data plane, rather than simply serving as a peak‑bandwidth interconnect.

Vanessa Do is a senior product marketing manager for PCIe IP at Cadence with over 20 years of experience in PCIe design, system validation, and customer engagement. Her background spans PCIe protocol development, FPGA-based customer support, and leading cross‑functional teams to debug complex PCIe issues at the system level.

Editor’s Note

This is Part 2 of the article series about PCIe 7.0 fundamentals. Part 1 explained PCIe’s ordering rules and the distinction between relaxed ordering and ID-based ordering.

The post PCIe 7.0: Addressing legacy ordering limitations with UIO appeared first on EDN.

Portable power units have both instantaneous-output and run-time limits, of course, but this situation seems a bit ridiculous. Or, then again, maybe not. But how to tell?

Last December, a few hours after the “kickoff” of our high wind-induced multi-day power outage “adventure”, I had the bright (if I do say so myself) idea to try hooking up our portable power stations (plus extended batteries in two of the three cases):

to the refrigerator-plus-freezer combo in the kitchen, along with both its combo fridge-plus-freezer companion and a standalone chest freezer out in the garage. The weather outside, therefore also the temperature in the garage, was chilly, so I wasn’t terribly worried about anything spoiling in either of those latter two units. Then again, I didn’t know how long the outage would last, and I had three supplemental power solutions at my disposal, so…

A preparatory test-drive admittedly would have been wise

I started (and ended; keep reading) with the cooling combo in the kitchen, my highest priority for perhaps-obvious comparative ambient temperature reasons. It’s a Samsung model RF217ACBP/XAA; here are a couple of stock photos to start:

I dragged from the downstairs furnace room the EcoFlow DELTA 2-plus-Smart Extra Battery “stack”, enabled the former’s AC inverter outputs, and plugged the combo fridge-plus-freezer in. I heard the compressor start up (accompanied by a DELTA 2 front panel display-reported AC output spike)…try to start up is a more accurate description, because after a second or so, the setup seemingly overloaded and gave up trying. Next up, the DELTA 3 Plus and its Smart Extra Battery sibling. Same underwhelming outcome.

The wind was blowing, the outside light was dimming, and my spouse was understandably getting stressed, so I didn’t waste any more time messing around; I promptly bailed on the idea and focused my attention elsewhere. Since I’d already expended the effort to get both “stacks” upstairs, they ended up alternatively finding use in powering table lamps, recharging various battery-powered devices—lanterns, laptops, tablets, smartphones—and the like.

No, I didn’t bother trying to haul upstairs my even heavier SLA battery-based Phase2 Energy unit. And fortunately, save for the spoil-prone contents of our kitchen refrigerator (but not its combo freezer), we didn’t need to toss any food. Still, I was both disappointed and (more than a) bit surprised, because I’d seen success reports from other folks who’d successfully powered food-storage equipment (albeit of unknown capacity and for unknown duration) using EcoFlow and other suppliers’ similar systems in similar circumstances as mine.

Published data also would have been helpful

Given my background experiences with other startup-surge hardware, I was pretty sure I knew how the failure had happened, but not specifically why. So, after the electricity started flowing again, I did some research. First off, I realized I hadn’t enabled either EcoFlow base unit’s X-Boost Mode feature, which might have gotten them over the compressor-start initial-surge “hump”. Please take a moment to “enjoy” the following promo video clips :

As I wrote last February, X-Boost “doubles the output AC power (at a reduced voltage tradeoff that not all powered devices are guaranteed to accept, albeit obviously counterbalanced by higher current)”. Could it have helped? Dunno; I’ll have to try it sometime when I get a chance.

But how much surge current, and at what minimum voltage, does the Samsung RF217ACBP/XAA demand on compressor startup? Ay, there’s the rub. You won’t find it in the user manual, or even the service manual, only steady-state power draw specs. The labels on the side:

and rear of the Samsung RF217ACBP/XAA:

weren’t directly helpful either, although they at least revealed the compressor model number (MK162D-L1U SJ1). But my online browsing using that specific search term was equally fruitless.

Cue the hand-waving

What do online resources say in general? Here’s Google AI Mode’s take on the topic:

A refrigerator typically experiences a startup surge current 3–4 times higher than its normal running amperage, lasting only a few seconds. While running at 1–4 amps, it can spike to 15–30 amps during compressor startup. This inrush current is essential to overcome inertia, usually requiring a dedicated 15–20 amp circuit.

I just checked and confirmed that my kitchen refrigerator breaker is 20A. Feel free to contrast that with the “3.9 Maximum Amperes” claim in the above sticker closeup shot. Sigh.

Ballpark figures are better than nothing, I suppose, albeit still (quite) non-ideal. Am I just overlooking something obvious, or being pedantic, or is the startup surge draw:

• useful information that
• Samsung (at least) isn’t publishing

therefore, compelling consumers to potentially overshoot, buying portable power systems beefier and more expensive than they may actually need (and, apparently, than I bad-pun-intended “currently” own)? Reader thoughts are as-always welcomed in the comments!

My father (the King of Duct Tape) would have been impressed

p.s…while researching this post’s topic online, I came across a mind-blowing (at least to me) somewhat-related Reddit thread that I couldn’t resist sharing: “Fridge kept tripping circuit breaker until I added an extension cord. Why?”. Here’s my stab at the TL;DR summary:

The OP (original poster) eventually determined, in conjunction with his repair tech, that the refrigerator’s defrost heater was failing. But in initially attempting to debug the issue, originally assuming that the outlet wiring might be failing, he used an extension cord (beefy, I hope) to plug the fridge into another outlet, which worked fine. Turns out, the extension cord was still largely coiled and sitting on top of the fridge; the resulting added circuit induction sufficiently opposed the high frequency noise injection coming from the failing defrost heater such that the arc fault circuit interrupting (ACFI) breaker stopped tripping…temporarily, at least.

The entire thread is well worth your perusal if you have sufficient spare time and interest!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• Preemptive utilities shutdown oversight: Too much, too little, or just right?
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ADI’s ADAA245x series of A2B 2.0 Automotive Audio Bus transceivers delivers 4× higher bus bandwidth (98.3 Mbps full-duplex) than A2B 1.0 devices. Now in production, the transceivers handle up to 119 upstream and downstream audio channels for advanced automotive audio systems, enabling high-definition audio transport across ECU networks.

The ADAA2457 supports Ethernet data tunneling via an Open Alliance SPI (OASPI) interface. All ADAA245x devices are compatible with existing A2B 1.0 cable and connector infrastructure and enable A2B 1.0 branching via device-specific I2S, I2C, and SPI interfaces. The ADAA2455 operates as a sub-node transceiver, while the ADAA2456 and ADAA2457 can be configured as main or sub-nodes.

According to ADI, the transceivers achieve up to 30% system cost reduction through increased functional integration and reduced external circuitry and component count. They also provide low, deterministic latency of 62 µs and are built for straightforward integration.

Learn more about A2B 2.0 and individual transceivers here.

Analog Devices

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Rohm’s ML7670/ML7671 wireless charging chipset provides NFC charging for compact wearables such as smart rings and fitness trackers. Operating in the 13.56-MHz band, NFC charging enables antenna miniaturization for ultra-compact devices. Following the 1-W ML7660/ML7661 chipset, the ML7670/ML7671 is optimized for even smaller wearable designs.

The chipset comprises the ML7670 receiver and ML7671 transmitter and supports wireless power transfer up to 250 mW. Peripheral components, including switching MOSFETs used to power the charging IC, are integrated. ROHM states that the 2.28×2.56×0.48-mm receiver IC reaches 45% power-transfer efficiency at 250 mW output, where it is optimized for compact wearable designs.

Rohm says the 45% power-transfer efficiency is enabled by tailored coil matching, rectifier circuitry, and reduced switching losses. Firmware for wireless power delivery is embedded in the IC, eliminating the need for a host MCU and reducing board space.

The NFC Forum WLC 2.0-compliant chipset is in mass production and is used in the Soxai Ring 2.

ML7670 product page 

ML7671 product page

Rohm Semiconductor

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