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The UK Semiconductor Centre (UKSC) has appointed its first chief executive officer to deliver on its vision of establishing the UK as a global leader in semiconductors by accelerating scale-up, investment and commercial growth across the sector...

На Факультеті інформатики та обчислювальної техніки КПІ відсвяткували Day F kpi вт, 05/05/2026 - 22:24

Power Integrations Inc of San Jose, CA, USA (which provides high-voltage integrated circuits for energy-efficient power conversion) has appointed Michael Balow as senior VP, worldwide sales. He joins the executive management team with responsibility for leading the global sales organization, channel strategy and growth initiatives...

КПІ отримав відзнаку Platinum Educational Partner від SoftServe kpi вт, 05/05/2026 - 13:19

For first quarter 2026, AXT Inc of Fremont, CA, USA — which makes gallium arsenide (GaAs), indium phosphide (InP) and germanium (Ge) substrates and raw materials at plants in China — has reported revenue of $26.9m, up 17% on $23m last quarter and 39% on $19.4m a year ago, and slightly above the forecasted $26m as export permits came in slightly better than guidance...

STMicroelectronics of Geneva, Switzerland has introduced two 100W VIPerGaN high-voltage converters, extending wide-bandgap energy savings throughout domestic appliances, building and home automation, smart lighting, and consumer products including televisions and device chargers...

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.

Gallium nitride (GaN) power IC and silicon carbide (SiC) technology firm Navitas Semiconductor Corp of Torrance, CA, USA has appointed semiconductor veteran Davin Lee to its board, serving on the Compensation Committee and the Governance and Sustainability Committee. He will stand for re-election in 2027 as a Class III director...

Confirming its preliminary figures released on 14 April, for first-quarter 2026 deposition equipment maker Aixtron SE of Herzogenrath, near Aachen, Germany has reported revenue of €59.4m, down 47% on €112.5m a year ago and down 68% on €187m last quarter, but within the guidance of €65m±€10m. Of total revenue, as much as 40% came from after-sales, and only 60% from equipment sales (€35.7m, down 52% on €87.7m, or 78% of total revenue, a year ago)...

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.

STMicroelectronics is enabling a shift from dedicated audio wiring to Audio over Ethernet in next-generation vehicles. The Stellar G6 automotive MCU integrates hardware-level Time-Sensitive Networking, Media Clock Recovery, and a dedicated communication engine to deliver high-fidelity, zero-jitter audio over the vehicle’s existing Ethernet backbone. The approach eliminates the need for proprietary A2B cables and transceivers, saving automakers approximately $70 per vehicle while enabling new capabilities such as real-time Active Noise Cancellation at the zonal level. A joint solution with AutoCore has already demonstrated end-to-end latency under two milliseconds, and ST is showcasing the technology live at Embedded World 2026 in Nuremberg.

Bringing high-fidelity audio to the software-defined vehicle
In a car, sound is personal. Listeners sit in fixed, asymmetrical positions surrounded by dozens of speakers, and their brains are ruthlessly precise about timing. A delay of just five milliseconds between two speakers is enough for the Haas Effect to kick in, tricking the listener into “pinning” the sound to whichever speaker fired first. A delta of two milliseconds can pull the entire soundstage to one side of the cabin, destroying the “phantom center” that makes a singer feel like they’re standing on the dashboard. When speakers fall slightly out of sync, sound waves collide destructively, creating nulls in the frequency response that make audio sound hollow or metallic. This is comb filtering, and it’s the acoustic signature of a timing problem.

These are not edge cases. They are the everyday reality of in-cabin audio, and they explain why the automotive industry has relied on dedicated wiring like A2B (Automotive Audio Bus) for so long. A2B is effective, but it demands its own cabling and transceivers, adding weight, complexity, and cost to the vehicle harness. Now that the industry is shifting toward Software-Defined Vehicles and zonal architectures, a new question is taking center stage: can a single Ethernet backbone carry diagnostics, control signals, and high-fidelity audio at the same time, without compromising the millisecond precision that human hearing demands?

With the Stellar G6 automotive MCU, we set out to prove that it can.

Latency is a number; jitter is the real enemy
Engineers often focus on latency, the constant delay between source and speaker. However, in automotive audio, jitter is far more destructive. Jitter is the variation in that delay. On a standard Ethernet network, an audio packet can get stuck behind a burst of sensor data. If the delivery time “jitters” by even a few microseconds, it introduces phase distortion that smears the music. For applications like Active Noise Cancellation, where a microphone signal must be inverted and played back through a speaker in near real-time, jitter doesn’t just degrade quality. It breaks the physics entirely.

Solving this requires more than a fast processor. It requires determinism, meaning the guarantee that a packet arrives exactly when it’s supposed to, and clock coherency, ensuring every node in the vehicle shares the same nanosecond. These are hardware problems, and they need hardware answers.

What Stellar G6 brings to audio over Ethernet
The Stellar G6 was engineered to treat audio as a time-critical stream, not as generic data. Three hardware-level capabilities make this possible. First, the Stellar G6 features a built-in L2+ Ethernet Switch supporting the full suite of Time-Sensitive Networking (TSN) standards. IEEE 802.1AS (gPTP) synchronizes every node in the vehicle to a sub-microsecond master clock. IEEE 802.1Qbv (scheduled traffic) creates protected time slots for audio and microphone data, ensuring they always get priority even on a congested network. IEEE 802.1CB enables seamless redundancy through Ethernet ring topologies, eliminating the single point of failure that plagues traditional star configurations.

Second, even with a perfectly synchronized network, the audio sample clock can still drift. The Stellar G6 includes specialized Media Clock Recovery hardware. Rather than relying on a software-based PLL, a dedicated digital hardware loop recovers the Audio Master Clock directly from the Ethernet stream, keeping speakers and microphones in perfect phase. The result: virtually zero jitter on the recovered clock, which is the critical enabler for professional-grade audio delivery.

Third, Stellar embeds a dedicated communication engine that offloads all data-moving and synchronization tasks from the main CPUs. This hardware isolation means that a processing spike in the vehicle’s body-control zone cannot cause a pop or a glitch in the audio. Communication runs at the lowest possible latency, completely decoupled from whatever else the host cores are doing.

From central processing to localized intelligence
Traditionally, all audio processing happened in a central head unit. Moving to an Ethernet-based zonal architecture changes this fundamentally. With a Stellar G6 acting as the Zonal Controller at each vehicle zone, significant compute now sits closer to every speaker and microphone.

This unlocks capabilities that were previously impractical. In-Cabin noise cancellation becomes possible by placing microphones near individual seats, identifying noise sources such as a loud conversation in the rear, and cancelling them locally. Road noise cancellation works on the same principle: the system captures vibration and road noise through zone-level microphones, generates an anti-noise signal, and plays it back through nearby speakers with near-zero latency. The processing happens at the edge, in the zone, rather than travelling back and forth to a central unit. For the passenger, the result is a cabin that can become a sanctuary, a workspace, or a private sound bubble, all updated over-the-air as easily as a smartphone app.

The cost equation: saving up to $70 per vehicle
Beyond acoustic performance, Audio over Ethernet carries a straightforward economic argument. By eliminating dedicated A2B cables and transceivers and reusing the vehicle’s existing Ethernet backbone, automakers can save approximately $70 per vehicle. In an industry where every cent on the bill of materials is scrutinized, consolidating audio onto a network that already exists for diagnostics and control is not just elegant engineering. It’s a significant cost reduction that scales across millions of units.

From proof-of-concept to production validation
In January 2026, we announced a collaboration with AutoCore on an Ethernet-based Zonal Controller distributed audio solution. By combining Stellar G6’s Media Clock Recovery with AutoCore’s TSN protocol stack, the joint solution achieved end-to-end audio latency of less than two milliseconds. That is fast enough to run high-performance Active Noise Cancellation over a standard Ethernet backbone.

At Embedded World 2026, we are taking this further with a live demonstration of Stellar G6’s native Audio-over-Ethernet capabilities. The demo features two Zonal Controller Units, each built around a Stellar G6, connected in a ring topology. Each ZCU streams four channels of 24-bit audio over Ethernet, for a total of eight high-fidelity streams running simultaneously. Visitors can witness the audio clock recovery in action, hear the zero-jitter playback quality firsthand, and see the resilience of the ring topology through live plug-and-unplug trials that demonstrate fault tolerance without audio interruption. It is a concrete, audible proof point: dedicated audio cables are no longer a requirement for premium in-cabin sound.

The Ethernet backbone is the nervous system of the SDV
We are moving toward a future where the vehicle’s Ethernet backbone becomes its nervous system, and Audio over Ethernet is one of the most visible and audible ways this transformation is taking hold. When a vehicle can use its Zonal Controllers to deliver immersive sound, suppress road noise, or create a private acoustic zone for every passenger, the concept of what a “car” offers fundamentally changes.

Stellar G6 is not just a processor in this journey. Solving one of the most demanding timing and synchronization problems in hardware, it allows automotive engineers to focus on the experience rather than the plumbing. As the industry embraces the zonal revolution, we are ready to help redefine what the drive actually sounds like.

The post Audio over Ethernet: How Stellar G6 is replacing dedicated audio cables with a single Ethernet backbone appeared first on ELE Times.

Славутицька філія КПІ: становлення і сучасність Інформація КП пн, 05/04/2026 - 13:54

As Keysight explores localization and diversification opportunities through its recently inaugurated manufacturing facility in Chennai, India, ELE Times sat down with Mr Sudhir Tangri, Vice President & General Manager Asia Pacific, Keysight Technologies, and Takuya Furata, Senior Director Global Marketing, Asia Pacific, Keysight Technologies, to understand the nuances of the initiative and what Keysight is looking for in India now that its manufacturing facility is operational! 

Keysight’s vision for its manufacturing facility and its integration within its global strategy are thoroughly examined. Sudhir Tangri highlights the significance of commencing manufacturing operations in India as a pivotal moment for Keysight’s presence in the region. The discussion delves into the comprehensive nature of the manufacturing process, from component sourcing to end-to-end production, underscoring the company’s commitment to quality and innovation. Moreover, the conversation emphasizes the value a manufacturing facility brings to Keysight India, aligning with the company’s core principle of prioritizing proximity to customers. This strategic move reflects Keysight’s enduring dedication to customer-centric practices, a principle that has guided the company’s trajectory over the past four to five decades.

Why A Manufacturing plant in India?

As the world witnessed the challenges of consolidated supply chains and consequential shortages following the COVID-19 pandemic, it was high time for companies to think about diversification across the supply chain, right from design to procurement and shipping. The case is the same with Keysight. “To diversify our global supply chains,” says Sudhir Tangri, referring to the reason for expansion.

Coming to India, Takuya Furata says, “India is not just leading, it is probably number one when it comes to a lot of indexes as well, so it’s growing, and our business is growing strongly in India as well.” Further, he adds, “So to be closer to the customers of the fastest-growing economy is a natural choice because we look into the future.” Tracing the history of Keysight’s plants in Japan and the USA, they were eventually moved to get closer to the customer as Keysight’s market expanded.

Global Impact of India Plant

Reflecting on the global impact of the newly inaugurated plant, Takuya Furata says, “Not just India, but within Asia, having two different manufacturing sites will benefit a lot of Asian customers for sure.” As demand picks up in Asia, Keysight is focused on derisking from one single manufacturing site in Asia to better support its customers across the continent.

“Looking over the entire Asia, this is such a happy moment because that’s going to add another value to the Asian customer,” adds Takuya Furata as he underlines the global impact of the facility, considering Asia’s geographical vicinity to India. This would largely mean smaller turnaround times, smaller manufacturing lifecycles, and easier procurement for the Keysight customers in Asia.

Challenges

While referring to the potential challenges of having a manufacturing facility in India, he says, “The priority right now is to stabilize and mature the manufacturing operations that we have started.” On the same lines, Takuya Furata talks about how worldwide companies must cross Day 01 to reach the next phase of production, Day 02, to make their efforts a success.  He says, “If you don’t clear Day 01, there is no Day Two, right? So, it’s imperative for the entire team, at Keysight here and the Keysight team, to make this successful.” 

One-of-a-Kind Dynamic

With the inauguration of the manufacturing facility in Chennai, Keysight India becomes the first T&M company in India to have its own manufacturing facility, which desirably puts Keysight into an entirely distinct segment of T&M companies in India. Reacting to the development along with a long-held dream of high-end manufacturing on Indian ground, Sudhir Tangri calls it a “surreal thing” unfolding before his eyes. 

The post Exploring The Surreality Of High-End Manufacturing On Indian Soil With Sudhir Tangri And Takuya Furata From Keysight appeared first on ELE Times.

I designed DC spot welder controller up to 4kA+ which can be powered from high current source or extrenal 12VDC source. I want to setup it with 4s or 2p2s LEV40 pack. If it passes the tests I will make it open-source. Functions: - Settable amount of energy into the weld in reasonable time with 50us control loop - Autopulse with settable delay for example pulse will be send after 300ms after shorting electrodes - Manual trigger (no delay) - Measuring source voltage - And maybe others submitted by /u/Short_circuit2 [link] [comments]

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.

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