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The rapid integration of artificial intelligence (AI) and machine learning (ML) into safety-critical systems is one of the most significant engineering challenges of our time. Whether it’s a medical device diagnosing an anomaly, an autonomous robot on a factory floor, or a train’s obstacle detection system, the question is no longer if we will use AI, but how can we guarantee its safe operation?

Enter ISO/PAS 8800, a new specification focused on the safety of AI applications in road vehicles. At first glance, the title implies that it’s solely for the automotive industry. However, for engineers in medical devices, industrial automation, rail, aerospace, and defense, dismissing this document as “just for cars” would be a missed opportunity.

Figure 1 ISO/PAS 8800 provides consensus-based framework for managing the unique risks of AI. Source: Parasoft

While ISO/PAS 8800 is tailored for the automotive V-cycle and references standards like ISO 26262, its core principles are fundamentally architecture- and domain-agnostic. It provides the most comprehensive, consensus-based framework to date for managing the unique risks of AI, such as nondeterministic behavior, data-driven bias, and performance degradation when systems encounter scenarios not represented in training data.

For example, in safety-critical systems, AI models used for perception or decision-making may behave unpredictably when exposed to rare or previously unseen conditions, potentially leading to incorrect or unsafe system responses if not properly validated and constrained. By understanding ISO/PAS 8800, engineers in other sectors can reinterpret its guidance to complement and enhance their existing safety standards, such as IEC 62304 (medical), IEC 61508 (industrial), EN 50716 (rail), and DO-178C (aerospace).

Here’s how the key principles of ISO/PAS 8800 can be adopted as a universal blueprint for AI safety.

The foundational shift: From “failure” to “insufficiency”

Traditional functional safety standards are built on a deterministic model: a component fails, and that failure must be managed. But AI/ML systems don’t “fail” in the traditional sense.

They can operate exactly as designed yet still be considered unsafe due to a lack of understanding the difference between a systematic fault (a bug in the C/C++ code) and a functional insufficiency (an AI model misclassifying a pedestrian because its training data lacked sufficient night-time examples). This is the single most important concept introduced in ISO/PAS 8800.

Figure 2 Here is how an AI model can misclassify a pedestrian because its training data lack sufficient night-time examples. Source: Parasoft

• For the medical device engineer (IEC 62304): This reframes how to validate diagnostic AI. The software units may be perfectly coded, but the model’s safety must be argued based on the sufficiency of its training data across diverse patient populations, not just its lack of software bugs.
• For the industrial robot integrator (IEC 61508): A collaborative robot’s safety function isn’t just about the hardware stopping in time. Its AI-based perception system might fail to detect a human in low light due to data insufficiency. ISO/PAS 8800 provides the language to specify and verify the “safety of the intended functionality” for AI, a concept that goes beyond traditional hardware/software failure rates.

AI is a system problem, not a model problem

The specification is adamant that an AI model is not a standalone “item.” It’s a component within a larger system. Clause 6 breaks down an AI system into three parts: pre-processing, the AI model, and post-processing. Safety, it argues, must be designed into the entire pipeline.

• For the aerospace engineer (DO-178C/DO-254): This aligns perfectly with the systems engineering approach of ARP4754A. AI-based object detection for a taxiing aircraft isn’t just the job of a neural network. It’s the image signal processor (pre-processing) and the voting logic that cross-checks the AI’s output with a LiDAR (post-processing). The “assurance argument” required by Clause 8 of ISO/PAS 8800 forces a look at the entire data and control path, not just the model’s inference accuracy.
• For the defense contractor (Def Stan 00-055): In a complex battlespace management system, the AI might propose courses of action. ISO/PAS 8800’s logic suggests that safety isn’t just about the AI’s recommendation, but about the “post-processing” layer, the human-machine interface and the rules of engagement that act as a final plausibility check before any action is taken.

The assurance argument: Moving beyond metrics

Clause 8 is the heart of the standard. It states that you cannot prove AI is “safe” simply by saying it is 99.9% accurate. Instead, you must build a structured assurance argument that combines quantitative data with qualitative reasoning.

An assurance argument must state a claim, provide evidence, and explain the reasoning that links them. For AI, the evidence requirement is multi-faceted:

• Data coverage: Is the dataset representative of the real world? (Clause 11)
• Robustness testing: How does the model perform under noisy or adversarial conditions? (Clause 12)
• Architectural mitigations: Are there redundant sensors, model monitors, or out-of-distribution detectors? (Clause 10)
• For the rail engineer (EN 50716 / CENELEC): Instead of just specifying an SIL rating for an AI-based track intrusion system, you would build an argument. The claim is “the system will detect an obstacle on the tracks.” The evidence includes: (1) traceability of the training data to a specification of the operational environment (for instance, all types of weather, debris, and times of day), (2) results from injection of anomalous sensor data to test robustness, and (3) the existence of a fallback to a traditional radar system if the AI’s confidence drops. This structured approach satisfies the rigorous traceability demands of rail safety.

Data as a safety-critical artifact

Clause 11 is revolutionary for its explicit treatment of data. In traditional software safety, the “code” is the master. In AI, the dataset is part of the specification. The standard mandates a full dataset lifecycle, from requirements definition to verification, validation, and maintenance.

• For the medical device engineer: This maps directly onto the need for diverse, high-quality clinical data. Clause 11 requires active management of datasets for gaps and biases. If an AI for tumor detection was trained only on specific age demographics, the standard mandates this be treated as a safety gap that must be mitigated, either by expanding the dataset or restricting the device’s intended use (Clause 9).

Confidence in tools and underlying code

Finally, Clause 15 reminds us that all AI systems are built on a software foundation, often C and C++. The most sophisticated AI model is useless if the C++ function that executes its safe-state monitor has a memory leak. The standard requires confidence in the development of the toolchain itself, from training pipelines to compilers.

This is where traditional software testing practices become the bedrock of AI safety. The “guardrails” that catch AI errors, the fallback logic, the monitors, and the plausibility checks must all be verified to the highest integrity levels using methods like static analysis, unit testing, and integration testing.

Figure 3 Robust software testing is critical in ISO/PAS 8800 implementation. Source: Parasoft

Just as ISO 26262 relies on robust software engineering, so too does ISO/PAS 8800. The principles of shift-left testing, automated unit testing, and CI/CD integration remain nonnegotiable, regardless of the final application domain.

A universal language for AI risk

ISO/PAS 8800 is more than an automotive standard—it’s a Rosetta Stone for translating the abstract risks of AI into the concrete language of safety engineering. It’s a vocabulary for discussing insufficiencies, a structure for building assurance arguments, and a lifecycle for managing data as a critical component.

For engineers in medical, industrial, rail, and aerospace sectors, the path to certifying AI-enabled systems will not require reinventing the wheel. It will require adopting and adapting the principles of ISO/PAS 8800 to a domain that complements existing standards like IEC 62304, IEC 61508, and DO-178C. By doing so, the navigation of AI complexities can be done with a proven framework, ensuring that as systems become smarter, they remain unshakably safe.

Ricardo Camacho is director of product strategy for embedded and safety critical compliance at Parasoft.

 

 

Related Content

• AI Safety Moves to the Forefront
• Specifying Objectives is Key to AI Safety
• Can We Trust AI in Safety Critical Systems?
• Safe Automated Driving Starts with Architecture
• The impact of AI/ML on qualifying safety-critical software

The post Why ISO/PAS 8800 is the new blueprint for AI safety in all critical industries appeared first on EDN.

Why does one battery (or a few) in a multi-battery pack always seem to drain faster than others, and how does this outcome affect both its siblings and the system they jointly power? Read on.

A recent teardown noted a surprisingly (at least to me) common occurrence that I’ve repeatedly experienced: a tendency for an Amazon Echo smart speaker (or other similarly powered device, for that matter) to functionally fail due to the demise (specifically: droop or other DC output voltage compromise under high load, I’m assuming) of an easily replaceable AC power adapter:

I’ve had a few second-generation “Dot” devices’ external power supplies fail in the past; the end result is either a flat-out refusal to start at all or a perpetual repetition of partial boots followed by abrupt restarts. In those cases, the consistent “fix” was straightforward and non-wasteful. Since the AC/DC converter with USB-A output was distinct from the USB-A to microUSB cable that fed the device, I could just swap in a replacement for the former and be up and running again in no time. Every time I did this, by the way, I wondered how many Echo Dots prematurely ended up in the landfill due to typical-consumer ignorance of both the exhibited issue’s root cause and simple resolution solution.

An oldie-but-goodie

In this post, I’ll cover another situation that I come across a higher-than-expected percentage of the time, whenever a multi-battery-powered device goes down for the count. To begin, I’d like to introduce you to a long-time, frequent-use friend of mine, my BT-168 battery tester:

I was happily surprised, while researching the BT-168 online just now while writing, to come across a link to a colleague’s review and teardown of it from a few years back:

Within his writeup, T.K also briefly mentioned a digital display-based successor, the BT-168D, whose existence I wasn’t aware of until now but which is apparently less accurate than my “old school” original analog version due to a comparative applied-load deficit:

I have no idea how long I’ve owned it, or for that matter, how it originally came into my possession. That said, it’s still available for sale (variously company-name branded by multiple retail sources) at Amazon and other distribution intermediaries, as is the follow-on BT-168D.

What’s this got to do with “single-battery failures in multi-battery arrangements”? Well, whenever a two-AA-powered remote control, for example, or a three-AAA-based bathroom scale:

or an LED flashlight, or even (an extreme example) the six-AAA-each (!!!) LED illumination-augmented automatic salt and pepper grinders we recently received as a gift:

functionally fades, I never reflexively slot all the batteries in a charger for refresh or toss ‘em all in the trash (depending, duh, on whether they’re rechargeable). Instead, I sequentially stick each of them in the BT-168 and see what remaining-charge level each reads. Invariably, one is significantly more “dead” than the other(s), even if they were all brand-new when originally installed. Replacing only the drained one more cost-effectively (for non-rechargeables) gets the gear going again, not to mention a reduced landfill payload…until the next one inevitably fails.

Organization determines compromise-outcome specifics

Why, though, does this operating-life inconsistency occur at all? I’d long been aware that batteries’ initial from-factory charges, therefore measured voltages, were predominantly-to-completely a function of their inherent chemical processes. To wit, so-called “precharged” rechargeable batteries are fundamentally just a marketing-driven relabel of low self-discharge, therefore longer-than-otherwise shelf life, battery chemistries and internal architectures.

But I admittedly didn’t fully realize until researching this writeup just how inconsistent battery-to-battery internal resistance can be, even within a common chemistry-and-architecture combination, both manufacturing batch-to-batch and even within a given batch. To be clear, Ohm’s Law, which I learned way back in my first semester of electrical engineering at university, has long informed me of the effects of higher-than-normal resistance: greater “waste” heat output, reduced current output and lower voltage, especially under load. And I also had some inkling of the fact that for a given battery, resistance also evolves over time and use, typically increasing (unless, of course, the battery develops an internal short). But notable battery-to-battery variability even fresh from the factory? That was, I confess, news to me, although in retrospect I shouldn’t have been surprised, especially for off brand, “cheap” battery options.

The resultant effects of internal resistance variability on multi-battery combos, as suggested by my research results along with another set of fundamental electronics laws, this time from Kirchhoff, are intriguing (IMHO, at least). For multiple batteries connected in series, as I’ve recently editorially inferred by analogy to solar panel connections, the outcomes of a higher-than-spec internal resistance for one of them are reduced aggregate output voltage along with bottlenecked peak current flow. Speaking of current, and on the other hand, batteries connected in parallel—where incremental peak current output potential is one key motivation for this organization, along with increased aggregate charge capacity—are hampered in both of these regards when one of the batteries is high resistance-compromised.

Multi-cell battery pack structures that connect their contents both in serial and parallel are increasingly common, both to boost the effective voltage (serial) and increase overall system runtime (parallel). As I was writing this post, for example, I came across editorial coverage of a YouTuber’s (modestly successful) project to power a (modestly equipped) desktop PC motherboard using only AA batteries:

You’ll see that he has four rows of 16 batteries each. Do the math and you’ll conclude, as I did (unless my methodology was flawed, which is always a possibility; if so, let me know in the comments) that each 16-battery bank is serially tethered (to generate ~25 V) and the four banks then connect in parallel (to boost capacity, therefore runtime). Battery degradation anywhere within the series/parallel cluster will thus result in both voltage and capacity compromises.

The YouTuber’s commentary, elementary as it may be, also makes important points about the importance of robust wiring and connectors, both topics which an excellent white paper (PDF) I came across in my research, published by Victron Energy, discusses at length. While both wiring and connectors, along with the batteries themselves, have resistances typically measured in dozens to hundreds of mΩ (that’s milli, not Mega), none is a perfect conductor. Use, for example, excessively thin wire, and you’ll end up with performance-degrading current flow constraints (along with maybe a fire). The same goes for a corroded battery contact, as anyone who’s dealt with a geriatric vehicle battery likely already knows. And each wiring run’s length is also a critical factor; if one span of a multi-battery parallel configuration is notably longer than the other(s), the resultant (slightly, but still) higher resistance will act akin to higher-than-average resistance in the battery itself.

More to say (but not today)

With brevity in mind, I’m only focusing here on the more common case of higher-than-average internal battery resistance (initially and, especially, over time). That said, as I already alluded to with my earlier “internal short” comments, resistance can also both inherently exist and evolve over time in the opposite (lower) direction. Such a situation is, perhaps obviously, particularly problematic in a multi-battery parallel configuration, both for the affected battery, the others in the parallel bank, and whatever they’re commonly powering.

For similar brevity reasons, I’m also covering today only situations where the installed batteries are either non-rechargeable or are removed for recharging. Multi-battery packs recharged in situ (while installed inside a portable power unit, for example) translate to an even more complicated scenario involving, among other factors, the critical importance (and difficulty) of balancing the various cells within the likely series/parallel cluster. The earlier-mentioned Vitron Energy white paper also explores this topic at length. More generally, I also found the various resources at Cadex Electronics’ Battery University quite helpful. And I’ll likely have more to say about these topics in future posts as well. Until then, and as always, I welcome your thoughts in the comments!

—Brian Dipert is the Principal at Sierra Media and a former technical editor at EDN Magazine, where he still regularly contributes as a freelancer.

Related Content

• Inside the battery: A quick look at internal resistance 
• Cell balancing maximizes the capacity of multi-cell batteries
• Active balancing: How it works and what are its advantages
• Internal Resistance Tester for Batteries

The post Single-battery failures in multi-battery arrangements: diagnosing selective cell derangements appeared first on EDN.

In a move that cements India’s transition from a consumer to a producer in the global silicon race, Prime Minister Narendra Modi officially inaugurated the Kaynes Semicon OSAT (Outsourced Semiconductor Assembly and Test) facility on March 31, 2026.

The ₹3,300 crore plant, located in the industrial heart of Sanand, marks the second major semiconductor unit to go operational in Gujarat within 900 days, following the earlier launch of the Micron facility. This rapid execution underscores the momentum of the India Semiconductor Mission (ISM) 2.0, as the country aggressively pursues a slice of the $110 billion global chip market.

A Global Export Hub

While domestic self-reliance is a key driver, the Kaynes plant is looking outward. During the ceremony, the first batch of Intelligent Power Modules (IPMs), sophisticated components that integrate 17 individual chips, was presented to Stephen Chang, CEO of Alpha & Omega Semiconductor, a California-based anchor customer.

“Today, a new bridge has been formed between Sanand and Silicon Valley,” the Prime Minister stated during his address. “The modules made here will reach American companies and, from there, power the world.”

Key Specifications of the Sanand Plant

The facility is designed for high-volume, high-precision manufacturing, focusing on sectors that are currently seeing explosive growth:

Feature	Details
Investment	₹3,300 Crore
Production Capacity	Approx. 6.3 Million chips per day
Primary Products	Intelligent Power Modules (IPMs), Multi-chip modules
Target Industries	Electric Vehicles (EVs), Industrial Automation, 5G Infrastructure
Timeline	From Cabinet approval to production in 14 months

The “Techade” Vision

The inauguration is more than just a corporate milestone; it is a strategic piece of the “India Techade” vision. Unlike traditional manufacturing, the Kaynes plant focuses on the back-end of the semiconductor value chain, like assembly, testing, and packaging, which has historically been a bottleneck for Indian electronics.

Union IT Minister Ashwini Vaishnaw highlighted the speed of the project, noting that the plant moved from foundation-laying to commercial production in record time. He also pointed to the growing “Sanand-Dholera” cluster, which is being modelled after global hubs like Hsinchu in Taiwan and Gyeonggi in South Korea.

Building the Talent Pipeline

To sustain this growth, Kaynes Semicon announced a memorandum of understanding with SVNIT Surat to develop a specialised workforce. This partnership aims to bridge the gap between academic theory and the rigorous standards of semiconductor cleanrooms, ensuring a steady stream of engineers for the 10 major chip projects currently approved across six Indian states.

As the ribbon was cut in Sanand, the message to the global tech community was clear: India is no longer just waiting for the future of hardware; it is assembling it.

The facility has already reported early execution success, having shipped approximately 900 multi-chip modules (IPM5) just days before the formal inauguration, signalling high operational readiness for its export commitments.

By: Shreya Bansal, Sub-Editor

The post Why Every EV & 5G Phone Could Soon Be Powered by Gujarat appeared first on ELE Times.

Intelligent power and sensing technology firm onsemi of Scottsdale, AZ, USA says that its hybrid power integrated modules (PIMs) will be featured in Sineng Electric’s next-generation 430kW liquid-cooled string energy storage systems (ESS) and 320kW utility-scale solar inverter. The design win builds upon the long-standing collaboration between onsemi and Sineng to deliver high-performance, future-ready solutions in the growing renewable energy and AI infrastructure markets...

Студенти ФСП відвідали місто Славутич Інформація КП чт, 04/02/2026 - 10:54

Alligator Automations India Pvt. Ltd., a manufacturer of end-of-line packaging automation systems, has reduced engineering effort in electrical design by around 50% by implementing WSCAD’s E-CAD solution.

The company’s ten-person electrical engineering team now uses WSCAD for creating electrical schematics, control cabinet design, and project documentation. Previously, tasks such as wire numbering, device grouping, and bill-of-materials generation had to be performed manually, resulting in project delays and an increased risk of errors. After switching to WSCAD, many of these steps are now automated, significantly improving both efficiency and design accuracy.

“Tasks that previously required manual work are now automated,” says Sagar Bhavsar, Control Engineering Manager at Alligator Automations. “Wire numbering alone now takes roughly half the time, allowing our team to focus more on complex design and optimisation tasks.”

Alligator Automations develops customised automation solutions, including robotic palletising systems, packaging automation, automatic loading systems, and intralogistics conveyor technology. Projects cover the entire value chain – from design and development to manufacturing, installation, commissioning, and long-term support for customers in industries such as food & beverage, paint & cement, fertiliser & petrochemicals, as well as tyre and agro industries.

“Automation projects are becoming increasingly complex while engineering timelines continue to shrink,” says Dr Axel Zein, CEO of WSCAD. “At the same time, AI is fundamentally changing how electrical engineering is performed. By automating documentation, verification, and knowledge retrieval, engineers can focus more on system design and optimisation instead of repetitive tasks. The Alligator Automations example demonstrates how standardising the E-CAD environment can significantly increase engineering efficiency.”

Beyond schematic creation, WSCAD supports precise 2D and 3D control cabinet layouts, automatic wire routing, and direct data transfer to cabinet manufacturing systems. This eliminates media discontinuities and reduces sources of error. AI-supported design, documentation, and multilingual translation capabilities further accelerate project delivery while ensuring compliance and data quality.

The post WSCAD ELECTRIX AI Cuts 50% Engineering Effort For Alligator Automations appeared first on ELE Times.

Vishay Intertechnology has introduced a new Automotive Grade photovoltaic MOSFET driver that is the first such device in the compact SMD-4 package to provide a creepage distance of 8 mm and mould compound with a comparative tracking index (CTI) of 600. Designed to increase safety and reliability in high voltage automotive applications — while simplifying designs and reducing costs — the Vishay Semiconductors VODA1275 features the industry’s fastest turn-on times and the highest open circuit voltage and short circuit current in its class.

Classified as providing reinforced isolation, the device delivers an open circuit voltage of 20 V typical, short circuit current of 20 μA, and turn-on time of 80 μs, which is three times faster than competing devices. These characteristics enable quicker and more reliable driving of MOSFETs and IGBTs in high-voltage systems. In addition, the device’s working isolation voltage of 1260 Vpeak and isolation test voltage of 5300 VRMS make it ideal for 800 V+ battery systems.

AEC-Q102 qualified, the VODA1275 is intended for use in pre-charge circuits, wall chargers, and battery management systems (BMS) for electric (EV) and hybrid electric (HEV) vehicles. While designers previously had to use two MOSFET drivers in series to generate the higher voltages required in these applications, the device’s high open-circuit output voltage allows them to use just one, saving space and lowering costs. In addition, the driver enables the creation of custom solid-state relays to replace legacy electromechanical relays in next-generation vehicles.

The optically isolated VODA1275 draws all the current required to drive its internal circuitry from an infrared emitter on the low-voltage side of the isolation barrier. This construction simplifies designs and lowers costs by eliminating the need for an external power supply. The MOSFET driver is RoHS-compliant, halogen-free, and Vishay Green.

The post Vishay Intertechnology Automotive Grade Photovoltaic MOSFET Driver Boosts Reliability and Lowers Costs in High Voltage Systems appeared first on ELE Times.

Two TVS diode series from Littelfuse provide DO-160 Waveform 5A Level 5 lightning protection for avionics, military, and other mission-critical systems. The SM15KPA-HR/HRA and SM30KPA-HR/HRA offer peak pulse power ratings of 15 kW and 30 kW (10/1000 µs), respectively, protecting I/O lines, power buses, and sensitive electronics from lightning-induced transients and high-energy surges.

Both families offer fast response times—typically less than 1 ps from 0 V to VBR minimum—30-kV ESD protection per IEC 61000-4-2 on data lines, and low incremental surge resistance. The devices remain stable across a junction temperature range of –55°C to +150°C. While all diodes undergo high-reliability 100% screening tests, the HR versions additionally pass MIL-STD-750 Group B tests for extra test rigor and extended reliability margins.

The TVS diodes come in compact SPD4-1 surface-mount packages compatible with automated assembly. These packages reduce weight and board space while eliminating the through-hole mounting typically required for high-energy TVS components.

The SM15KPA-HR, SM15KPA-HRA, SM30KPA-HR, and SM30KPA-HRA series are available in tape-and-reel format in quantities of 500. Samples can be requested through authorized Littelfuse distributors worldwide.

Littelfuse

The post Lightning-resistant TVS diodes safeguard avionics appeared first on EDN.

Infineon’s TDM24745T quad-phase power module with trans-inductor voltage regulator (TLVR) magnetics provides high current density for AI workloads. Integrating four power stages, proprietary magnetics, and decoupling capacitors in a compact 9×10×5-mm package, the module achieves 2 A/mm² and delivers up to 320 A peak.

The device optimizes transient response and supports the high-current core rails required by advanced GPU and AI processors in both lateral and vertical power delivery configurations. Powered by OptiMOS-6 MOSFETs, it offers enhanced efficiency and thermal performance in dense AI server designs. The TLVR architecture further improves transient performance while reducing required output capacitance by up to 50%.

The TDM24745T power module integrates with Infineon’s end-to-end AI server power delivery ecosystem. Availability was not disclosed at the time of this announcement. For more information about Infineon’s voltage regulation solutions for AI and data centers, click here.

Infineon Technologies 

The post TLVR power module supplies 320 A for AI processors appeared first on EDN.

Diodes has added a 100-V MOSFET to its lineup of 40-V to 80-V devices, all in 8×8-mm gullwing-leaded packages for automotive systems. With a maximum on-resistance of 1.5 mΩ, the DMTH10H1M7SPGWQ is well suited for 48-V BLDC motor drives used in power steering and braking systems. Like other family members, it minimizes conduction losses, reducing heat generation and maximizing overall efficiency.

The MOSFET’s PowerDI8080-5 package occupies a footprint of just 64 mm², approximately 40% less than the legacy TO-263 (D2PAK). It also offers a slim off-board profile of 1.7 mm. Copper clip die bonding reduces thermal resistance to as low as 0.3°C/W, enabling drain currents as high as 847 A without risk of damage. The gull-wing lead configuration supports automated optical inspection and enhances temperature-cycling reliability.

AEC-Q101 qualified, the DMTH10H1M7SPGWQ is rated to +175°C for high-ambient-temperature operation. One-hundred-percent unclamped inductive switching (UIS) testing during production ensures reliable, robust end applications.

The DMTH10H1M7SPGWQ is priced at $1.71 each in quantities of 5000. Contact sales via the product page for availability and purchase details.

Diodes

The post MOSFET ensures automotive thermal reliability appeared first on EDN.

The 40 LHE linear position sensor from Vishay measures strokes from 0 to 40 mm with ±1% full-stroke accuracy. Using Hall effect technology, it delivers 12-µm resolution and a lifespan exceeding 10 million cycles, making it well-suited for servo loop motion control systems that monitor small displacements. Typical applications include infrastructure integrity monitoring (crackmeters), in-line process measurement, industrial and medical robotic gripping, and throttle/pedal sensing in e-bikes and motorcycles.

Built for harsh environments, the 40 LHE operates from -40°C to +85°C and features IP67 sealing. It withstands high-frequency vibration up to 20 g and shock up to 50 g, with integrated input protections against reverse voltage (-10 VDC) and overvoltage (+20 VDC).

Its compact 35×14.5×28-mm design includes two face-mounting holes for horizontal or vertical installation. The sensor is available with or without a spring return and offers analog ratiometric or PWM output, with a recommended load resistance of 1 kΩ. It operates from a 5-VDC ±10% supply and draws less than 16 mA typically.

Samples and production quantities of the 40 LHE are available now, with 12-week lead times.

40 LHE product page 

Vishay Intertechnology 

The post High-res linear sensor tracks small displacements appeared first on EDN.

Leveraging IsoShield multichip packaging, TI’s isolated power modules deliver up to 3× the power density of discrete devices. The UCC34141-Q1 and UCC33420 reduce solution size by as much as 70% in isolated power designs for applications ranging from data centers to EVs.

IsoShield co-packages a planar transformer and isolated power stage to provide functional, basic, and reinforced isolation. This architecture supports distributed power and helps meet functional safety requirements by avoiding single-point failures.

The UCC34141-Q1 is an automotive-qualified 5-kVRMS DC/DC module for SiC and IGBT isolated gate drivers. It provides 1.5 W typical output at 85°C ambient, with dual outputs set by resistor dividers. A 5.5-V to 20-V input range and adjustable UVLO support EV battery voltages and regulated rails, while VIN transients up to 28 V are tolerated. 

The UCC33420 is an industrial 3-kVRMS DC/DC module offering up to 1.5 W of isolated output. It supports a 4.5-V to 5.5-V input and regulates a 5.0-V output with selectable 5.5-V headroom. Multiple protection features are integrated in its 4×5×1-mm package.

The UCC34141-Q1 and UCC33420 are available in preproduction and production quantities, respectively. Evaluation modules are available for both.

Texas Instruments  

The post Isolated DC/DC modules raise power density appeared first on EDN.

some time ago I bought this clock for few dollars at flew market. display was run out, so I decided to create new internals - based on ESP, WIFI and dot -matrix LED display. easiest way was to use bread board and some wires. I like to make some things with ESP modules - it helps to prolong life for unexpected things. My old fridge is next 😁 submitted by /u/Regular-Host-7738 [link] [comments]

EPC Space LLC of Andover, MA, USA (which provides high-reliability radiation-hardened enhancement-mode gallium nitride-on-silicon discrete transistors, ICs and modular devices for power management in space and other harsh environments) has announced two new additions to its family of demonstration and evaluation boards supporting applications including half-bridge connected point-of-load (POL) converter power stages, single- and multi-phase motor drivers, and Class-D single- and full-bridge stages...

NUBURU Inc of Centennial, CO, USA (a dual-use defense & security platform company focused on non-kinetic effects, directed-energy technologies, and software-orchestrated defense systems) says that its subsidiary Lyocon S.r.l. (an Italian laser-technology company specializing in the design, manufacturing and integration of high-power blue laser systems for industrial applications) has secured an initial deployment order for its portable directed-energy laser dazzler system for counter-drone (C-UAV) defense applications from a tier-one government-owned defense electronics organization operating within a centralized government procurement framework in a major Asia–Pacific defense market...

Silvaco Group Inc of Santa Clara, CA, USA — which provides AI-enabled technology computer-aided design (TCAD), electronic design automation (EDA) software and semiconductor intellectual property (SIP) for process and device development — has announced an expanded strategic partnership with Taiwan-based silicon and silicon carbide power device developer and and manufacturer Advanced Power Electronics Corp (APEC)...

If your favorite tool is a hammer, every problem will look like a nail. Abraham Maslow

Given how often I tinker with the LM555 and LMC555 analog timers, Maslow might have written that famous aphorism specifically for me. And it. Well, here I go again. Bang bang.

Figure 1’s circuit morphs the versatile 555 into a circuit that’s quite different from its usual role as an analog oscillator or timer. Here it’s combined with an NTC (negative tempco) thermistor, and one (or optionally two) resistors to make a resistor-programmed ON/OFF thermostat. It’s easily configured for heating or cooling.

Here’s how it works.

Figure 1 Basic bang bang heating configuration. Setpoint thermistor resistance = Rb/2. Optional Rh sets desired temperature hysteresis. Output rated at up to 15 volts and 300 mA = 4.5 W

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

One of the secrets (or at least scantily documented) features of the 555 is what happens if you tie Threshold (pin 6) to Vdd as shown in Figure 1. What happens is Trigger (pin 7) then becomes an inverting analog comparator input that drives Output (3) and Discharge (7) high if Trigger < Vdd/3, and low if Trigger > Vdd/3.

When you combine that action with an NTC thermistor and bias resistor Rb as shown, presto! You get a simple but practical thermostat. It turns power (and a substantial amount of it: up to 15 V and 300 mA) ON to the load (e.g., a resistive heater) if the thermistor’s temperature is cooler than the setpoint (thermistor resistance > Rb/2). Power goes OFF when the temperature is warmer (thermistor < Rb/2). 

But wait, there’s more. Because accurate thermostatic action depends only on resistor ratios rather than absolute voltages, V+ needn’t be regulated. In fact, if the load isn’t bothered by ripple (e.g., a resistor heater certainly won’t care), it doesn’t even need to be filtered!

Furthermore, if you swap the positions of the thermistor and resistor as shown in Figure 2, and connect a cooling fan (or perhaps a thermoelectric cooler), the temperature regulation inverts. It will now maintain a constant maximum instead of a minimum temperature. If the output load is inductive (e.g., a fan motor), don’t worry about possible inductive transients. The LM555 output pin includes its own kickback protection.

Figure 2 Cooling configuration: Setpoint thermistor resistance = 2Rb.

If hysteresis (dT) is required, for typical NTC tempcos (~4 %/oC), an easy (if approximate) rule of thumb value for Rh = 680k/dToC.

Figure 3 Typical configurations for 50oC setpoint.  Heating (left), Cooling (right) with ~1o hysteresis.

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.

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