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India Electronics Development Fund (EDF) strengthened the country’s deep-tech ecosystem with an accumulated investment of Rs 1,335.77 crore (approx. $139.23 million) supporting 128 start-ups and tech enterprises. This milestone further demonstrates the government’s commitment to developing Indian capabilities in innovation across electronics, semiconductors, nanoelectronics, and IT. This development reiterates their aim to foster an indigenous self-reliant ecosystem that can eventually enable the creation of a globally competitive technology economy leveraging high innovation, leading edge technology, high-quality human talent to create differentiated technology solutions.​

The Electronics Development Fund (EDF) set up by the Ministry of Electronics and Information Technology (MeitY) in February 2016 is an Umbrella-Fund of Funds concept. In this model, the EDF invests into other professionally managed venture capital and angel investment funds, called “Daughter Funds.” These Daughter Funds invest into the high- growth potential start ups that build future technologies. As of September 30, 2025, a sum of 257.77 crore of direct equity in eight Daughter Funds by EDF and additional 1,335.77 crore capital invested by Daughter Funds in total of 128 start-ups/ ventures were catalysed in India.

The electronics development fund manages eight SEBI regulated Daughter Funds like Unicorn India Ventures Trust, Aaruha Technology Fund-I, Endiya Seed Co-creation Fund, Karsemven Fund, pi Ventures Fund I, YourNest India VC Fund II, Ventureast Proactive Fund-II and Exfinity Technology Fund Series II. The daughter venture funds pick out startups that can use cutting edge technologies and have high commercial viability.

Having invested in 128 start-ups spanning various frontier technologies, the Electronics Development Fund has become a significant lever for nurturing deep-tech innovation in the country. Over the coming years, the programme will play an even larger role in scaling the nation’s electronics ecosystem, create high-skill, high-value jobs, and advance India’s vision to lead the world in areas like electronics design, semiconductor technologies, and next generation digital innovations.

The post Electronics Development Fund Backs 128 Start-ups with $139.23 Million in Investments appeared first on ELE Times.

Розширення партнерства із закладами вищої освіти Пакистану KPI4U-2 пт, 07/31/2026 - 12:02

For its fiscal first-quarter 2027 (ended 27 June 2026), Qorvo Inc of Greensboro, NC, USA (which provides core technologies and RF solutions for mobile, infrastructure and defense applications) has reported revenue of $784.8m, down 2.9% on last quarter’s $808.3m and 4.2% on $818.8m a year ago...

The Supervisory Board of ams OSRAM AG of Premstaetten, Austria, and Munich, Germany has approved the reappointment of CEO Aldo Kamper and agreed to a new contract effective 1 October, and running through 30 September 2031. His previous term was set to expire on 31 March 2027. Kamper has been CEO since April 2023...

Finland-based laser developer and manufacturer Vexlum — which was spun off from Tampere University of Technology’s Optoelectronics Research Centre in 2017 and develops and manufactures high-power semiconductor vertical-external-cavity surface-emitting laser (VECSEL) technology systems — has announced the appointment of Dr Stefan Truppe as managing director of Vexlum UK, and the establishment of a dedicated laboratory in London, positioning the firm at the center of the UK’s rapidly growing quantum ecosystem...

India is stepping up investments in key EV technologies. The South Asian country is speeding up its investments in key electric vehicle (EV) technologies while planning to boost a self-dependent and globally relevant EV manufacturing ecosystem. Driven by various central government initiatives, state government programs, and private sector investments, India attracted commitments for around US$25.6 billion worth of investments across battery manufacturing, advanced battery chemistries, powertrains, charging infrastructure, and technologies for battery and electric powertrains. These investments will help scale up domestic manufacturing and reduce dependence on imported components.

The Government of India launched multiple schemes to boost domestic manufacturing of the Electric Vehicles (EV) ecosystem through production-linked Incentive (PLI) Schemes for Advanced Chemistry Cell (ACC) and battery storage, as well as the PM E-Drive program to promote charging. The PM E-Drive program aims to promote charging for automobiles and auto components. Along with manufacturers both domestically and overseas, these schemes lead to localization of EVs’ key technologies.

The post India Accelerates Core EV Technology Investments to Strengthen Domestic Manufacturing Ecosystem appeared first on ELE Times.

High-voltage batteries for e-mobility have become highly complex systems consisting of numerous components. The market for these batteries is becoming increasingly dynamic, and technologies are constantly evolving: innovative battery cells with new chemical compositions are being introduced. Automotive suppliers and manufacturers therefore require a tailored and flexible testing strategy for battery cell quality assurance, both in development and in production. Only in this way can suppliers compete in the market with more powerful batteries, higher levels of automation, and optimized workflows.

To address these challenges, GÖPEL electronic has developed a high-performance and quickly configurable battery cell tester that features a modular design and high flexibility. This allows for the quick and transparent determination of quality, charging efficiency, and reliability in up to five battery cells simultaneously. The tester complements the product portfolio, which includes, among other things, GÖPEL electronic’s EOL battery test system, which performs comprehensive test routines for the entire battery pack.

The new battery cell tester covers standard safety tests that evaluate the condition of the cells: The OCV (Open Circuit Voltage) value indicates the voltage of a battery without a load (open circuit) and serves as an indicator of its state of charge. For the ACIR (Alternating Current Internal Resistance) measurement, the tester uses a test method to determine the internal resistance of the battery cells. This internal resistance under alternating current provides information about the performance, state of aging, and quality of the battery cell.

The main advantage of the new, smaller tester from GÖPEL electronic lies in its fast and reproducible test results: By interfacing with automated equipment, highly efficient batch testing can be performed in a matter of seconds. This is ideal, for example, for incoming inspection or cell grouping.

The post Fast and Flexible Test Device for Safety and Functional Testing of Battery Cells appeared first on ELE Times.

Ather Energy has started rolling out Pothole+ Alerts for customers with Gen 2 and above scooters, including the Ather 450 Apex, 450X and Rizta Z. The feature leverages data generated by Ather’s connected scooter fleet to warn riders about potholes, broken road sections, uneven surfaces and speed breakers ahead while navigating. It also goes a step further, suggesting the smoothest route before a ride begins, beyond just the fastest one.

Announcing the rollout on X, Ather Co-founder and CEO Tarun Mehta said the feature had been nearly nine years in the making. “The challenge was never the idea. It was the data,” Mehta wrote. He explained that building the feature required a large connected fleet travelling the same roads over time, allowing Ather to generate the scale of fleet intelligence needed to accurately identify road conditions.

Every Ather scooter comes equipped with onboard compute and connectivity, enabling the company to continuously gather real-world riding data and deliver new capabilities through over-the-air software updates. According to Mehta, Pothole+ Alerts represents one of the first large-scale applications of this connected ecosystem, transforming millions of kilometres of ride data into a feature that benefits riders every day.

Integrated into the scooter’s navigation experience, Pothole+ Alerts provides advance notifications for potholes, broken road sections, uneven surfaces and speed breakers. Alerts are displayed on the dashboard and can also be delivered through the scooter’s speakers, the Ather Halo smart helmet or any compatible Bluetooth headset.

The rollout reflects Ather’s continued focus on building connected features powered by real-world riding data. Mehta added that Pothole+ Alerts is just the beginning, with the company planning to build more rider experiences on top of its growing road intelligence dataset.

The post Ather Energy Rolls Out Pothole+ Alerts, Built on Connected Fleet Intelligence appeared first on ELE Times.

Aeluma Inc of Goleta, CA, USA has signed a letter of intent (LOI) for up to $30m of proposed funding under the CHIPS and Science Act, which is administered by the US Department of Commerce. The award will support R&D on the firm’s scalable, non-InP (indium phosphide) semiconductor manufacturing platform for photonics...

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 entered into a definitive agreement with Lumentum Operations LLC of San Jose, CA, USA (which designs and makes photonic products for optical networks and lasers for industrial and consumer markets) for the supply and capacity reservation of indium phosphide wafer substrates...

Стипендія КМУ за видатні заслуги у сфері вищої освіти Віталію Дідковському KPI4U-2 чт, 07/30/2026 - 10:00

Here’s a question your grandfather could have answered: Would you rather have a million dollars today or a penny that doubles every day for a month? Most people take the million. The penny reaches $5 million by day 30.

Human intuition is simply bad at exponential math. We think in straight lines, and compounding curves surprise us every time. Now run that intuition in reverse.

An AI coding agent that’s right 98% of the time sounds impressive. And 98% is a generous assumption, probably better than reality for most tasks. But apply that accuracy across 34 steps with no verification in the loop, and you’ve crossed the coin-flip line. More likely wrong than right. The math is 0.98^34 = 0.505.

The surprise is the same one your grandfather felt. And in a safety-critical development environment, the consequences are not a missed investment opportunity.

Sophisticated agentic systems don’t run open-loop. They compile, test, lint, and self-verify at each step, and the public record shows it works.

Andreas Kling ported Ladybird’s LibJS engine from C++ to Rust using AI agents across hundreds of human-directed prompts, producing 25,000 lines of Rust with zero regressions across 65,359 tests and byte-for-byte identical bytecode output. The human was in the loop at every decision point, which is precisely why it worked.

The Bun JavaScript runtime went further. AI Weekly highlighted that Claude agents rewrote roughly one million lines of Zig to Rust autonomously across 6,755 commits, passing 99.8% of its existing test suite. It also left 13,044 unsafe Rust blocks, where a comparable handwritten project would have 73. A passing test suite doesn’t surface this safety debt—it stops a safety-critical certification in its tracks.

Both of these projects succeeded because verification ran inside the loop at every step. They also illustrate exactly where the limits are. In most software development contexts, the floor is an efficiency problem. Verification catches it, the agent retries, and the process converges. Expensive in tokens and time, but recoverable.

In safety-critical development, the calculus is different. This is where functional correctness testing and safety-critical qualification part ways. Bun passed its own test suite. Ladybird produced byte-for-byte identical bytecode. Those are impressive results. But they are not safety cases. ISO 26262, DO-178C, and IEC 62304 don’t recognize self-generated test results as qualified verification evidence.

Your braking system software doesn’t get partial credit for passing tests it wrote for itself. Your insulin pump firmware isn’t certified on a curve. The standards assume deterministic tools producing verifiable evidence—qualified tools, documented configurations, and traceable outputs. An agentic workflow that self-verifies is better than one that doesn’t. But in safety-critical development, it still isn’t enough.

What safety-critical compliance actually requires isn’t vague.

ISO 26262 mandates a documented safety plan, requirements with bidirectional traceability from hazard analysis through to verified implementation, and evidence that coding guidelines—typically MISRA C or CERT C—were enforced by a qualified tool using a qualified configuration.

DO-178C adds structural coverage requirements. At the highest criticality levels, every statement, every branch, and every condition and its complement must be exercised by tests that are themselves traced to requirements.

IEC 62304 requires a software development lifecycle with documented verification activities at each phase. In every case, the evidence must be generated as the work happens rather than reconstructed afterward—and not self-certified by the tool that produced the artifact being evaluated.

The open-loop pipeline isn’t an edge case; it’s what every team promises to fix after the next release. A requirements review is handed to a code generator, a documentation tool, and a traceability updater with testing saved for the end. That’s not an agentic worst case. That’s a pipeline. At 98% per-step accuracy across 34 stages, you’ve crossed the coin-flip line before you’ve run a single test.

The answer isn’t a better model. It’s the same answer safety-critical engineers have always given to unreliable processes. You don’t improve your way to acceptable; you gate your way there.

Static analysis enforces expected coding patterns and flags dangerous anti-patterns like uninitialized memory, undefined behavior, and violations of MISRA or CERT rules that exist precisely because they’ve caused failures before.

Unit tests verify that individual components behave as specified under known conditions. And coverage in safety-critical development isn’t a spot-checking exercise. DO-178C requires 100% MC/DC coverage at DAL A, and ISO 26262 requires the same at ASIL D. Every line. Every branch. Every condition.

Each gate resets the accumulated uncertainty back toward zero before the next stage compounds it further. That’s not a new idea. It’s how you build software that people’s lives depend on.

The question AI raises isn’t whether to use gates. It’s whether the gates you already have are positioned to catch what an AI agent introduces and whether you’ve thought carefully about where in the workflow the uncertainty is actually accumulating.

The gates were designed for a world where code has an author who made deliberate choices. A human developer who writes an uninitialized variable made a mistake. A human developer who skips a boundary check made a tradeoff. Static analysis flags both—the developer understands the finding in context, and the correction is made by someone who knows what the code is supposed to do. The evidence trail is intact. The intent is recoverable.

An AI agent doesn’t make mistakes in that sense. It produces outputs that are statistically consistent with its training: plausible, often correct, and occasionally wrong in ways that look right.

The static analysis tool will still flag the MISRA violation. The unit test will still fail on the boundary condition. But the developer reviewing the finding is now one step removed from the original intent because there wasn’t original intent in the human sense. There was a probability distribution. And when you ask the agent why it made that choice, the answer is not recoverable in any form a certification auditor can use.

The gates catch the artifact. They don’t reconstruct the argument. In a safety case, you need both, and one of them must have been generated as the decisions were made, not reverse engineered from the output afterward.

The consumer technology press calls it “hallucination,” which means the AI confidently states something wrong. This term captures the symptom, but not the mechanism.

In safety-critical engineering the mechanism is what matters. ISO/PAS 8800, the emerging automotive standard for AI safety that the broader embedded industry is watching closely as a template, uses the term “functional insufficiency”: an unexpected error under specific conditions not adequately represented during development. As EDN noted, for engineers building software for medical devices, industrial automation, rail, aerospace, and defense, dismissing this document as “just for cars” would be a missed opportunity.

The distinction matters. Hallucination implies the system invented something from nothing. Functional insufficiency describes something more precise. The system performed exactly as its training data suggested it should, and the training data didn’t cover this case.

You can’t fix a hallucination by improving the model. You can’t fix a functional insufficiency that way either. What you can do is bound it, monitor it, and build an architecture that prevents it from propagating into a safety-critical decision unchecked.

None of this is an argument against AI in safety-critical development. These industries already have the architectural foundations to manage it responsibly. That argument is already lost, and it should be. AI tools are accelerating development, surfacing defects earlier, and handling the kind of repetitive verification work that exhausts engineers and introduces its own error rate.

The question was never whether AI would enter these industries. It’s here. The question is whether the engineering discipline surrounding it will keep pace.

Compound uncertainty doesn’t care about your intentions or your vendor’s benchmark scores. A 98% accurate agent in a 34-step open-loop workflow has already crossed the coin-flip line. Those numbers don’t improve because the use case is important or the schedule is tight.

Compound uncertainty in multi-step workflows. Even with 95% per-step accuracy, overall success rate declines sharply as the number of workflow steps (N) increases—not because model performance degrades, but because the workflow itself compounds error. Source: Parasoft

What does improve the outcome is treating AI in safety-critical development the way these industries have always treated unreliable components: with gates, evidence, and documented reasoning that survives an audit.

The standards that govern medical devices, aviation software, and automotive systems were written for a deterministic world. But the principles they encode—rigorous verification, traceable decisions, complete coverage, and structured safety arguments—turn out to be exactly the right response to a world where probabilistic behavior slipped into the development process before anyone checked its credentials.

ISO/PAS 8800 is the automotive industry’s first formal attempt to extend those principles into AI-specific territory. Other domains are watching. The framework outlined in the embedded world—manage uncertainty, bound it, argue it, and monitor it—applies whether you’re building firmware for a ventilator or a flight control system or an autonomous vehicle.

You will never eliminate functional insufficiency from an AI system. However, you can build an architecture that catches it before it becomes a safety event. That’s not a limitation of technology. It’s just engineering.

Arthur Hicken is a senior software evangelist at Parasoft.

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 Compound uncertainty: AI’s hidden risk in safety-critical development appeared first on EDN.

submitted by /u/Quietgoer [link] [comments]

Multi-national industrial and energy company METLEN Energy & Metals S.A. of Athens, Greece — which operates the only vertically integrated bauxite, alumina and primary aluminium production unit in the European Union (EU) with privately owned port facilities — has announced a long-term commercial agreement for the supply of about 25% of the annual gallium production from its planned production facility in Greece...

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has appointed Andy W. Mattes to its board of directors...

Wide-bandgap (WBG) materials, particularly silicon carbide (SiC) and gallium nitride (GaN), are no longer considered exotic semiconductors. The automotive industry has chosen them for their ability to switch faster, tolerate higher blocking voltages and temperatures, and dissipate less energy than conventional silicon.

The physical and electrical characteristics of SiC and GaN enable improved efficiency in electric and hybrid drivetrains, higher power density, and better thermal management. This article examines the current state and next steps for both technologies across different key automotive areas, including traction inverters, on-board chargers (OBCs), DC/DC converters, and auxiliary power systems.

Traction inverters: the realm of SiC, but GaN makes headway

The transition from a 400-V to 800-V bus architecture, a common trend in electric-vehicle design, has halved the current required to deliver the same power. This reduction, in turn, enables ultra-fast charging, as EVs can absorb more power without incurring hazardous current levels or excessive heating. Moreover, the 800-V bus power delivery significantly lowers internal heat loss and allows for lighter and thinner vehicle wiring.

This architecture shift is a key factor for WBG adoption. At 800 V, the traction inverter operates with bus voltages that are very close, or even higher, than the rating limits of conventional silicon power devices, such as IGBTs.

SiC MOSFETs, rated at 1,200 V or higher with low on-resistance (RDS(on)) and fast-switching behavior, are suited for this application. In 2021, SiC inverters had a market share of less than 8% in global EV production, reaching 24% by 2026, according to market research firm Market Intelo. Analysts estimate that by 2030, SiC inverters will reach a 55% market share across EV production.

In January 2025, Wolfspeed Inc. introduced its Gen 4 SiC MOSFET platform, covering 750-V, 1,200-V, and 2,300-V voltage classes in discrete, module, and bare-die form. Compared with the previous version, Gen 4 technology reduces the specific on-resistance (Ron,sp) at high temperatures by up to 21% (with an even higher reduction at low temperatures) and provides improved turn-on performance with reduced ringing.

Built on this platform are the 1,200-V six-pack power modules (Figure 1), part of the automotive-qualified YM Six-Pack module line. These modules integrate a direct-cooled pin-fin baseplate and use sintered die-attach layers, copper-clip interconnects, and epoxy encapsulation to improve power-cycling capability. According to Wolfspeed, they deliver a 3× higher power-cycling capability at rated operating temperature than comparable competitor modules. The YM package size is compatible with existing IGBT inverter housings, simplifying platform migration.

Figure 1: Based on Wolfspeed’s Gen 4 SiC MOSFET technology, the six-pack YM power modules comply with the AQG-324 automotive standard. (Source: Wolfspeed Inc.)

Wolfspeed recently introduced its Gen 5 SiC MOSFET planar technology. Manufactured in Wolfspeed’s 200-mm fabs, the latest generation further reduces Ron,sp by up to 27% for 1,200-V devices, raising continuous junction temperature to 200°C for improved reliability.

STMicroelectronics provides 750-V and 1,200-V, automotive-qualified devices for 400-V and 800-V EV traction inverters, thanks to the introduction of the company’s Gen 4 SiC MOSFET technology. Gen 4 devices offer reduced RDS(on) (8.2 mΩ and 10.2–10.9 mΩ for the 750-V and 1,200-V classes, respectively) to cut conduction losses.

ST also announced the ongoing development of a higher-temperature-capable architecture to further reduce RDS(on) at high junction temperatures, meeting the requirements of air-cooled or passively cooled traction inverter designs.

Rohm Semiconductor introduced the TRCDRIVE pack, designed for xEV traction inverters. Built on Gen 4 SiC MOSFET technology, these 750-V and 1,200-V, two-in-one SiC molded modules feature higher power density, a compact layout that optimizes heat dissipation, and signal terminals supporting press-fit mounting (Figure 2).

Figure 2: Rohm’s TRCDRIVE pack modules offer high power density and simplify assembly through press-fit pins. (Source: Rohm Semiconductor)

Rohm also partnered with Schaeffler on a high-voltage inverter brick in mass production for a leading Chinese automaker. The brick integrates Rohm’s Gen 4 SiC MOSFET bare chips with a DC-link capacitor, a cooling solution, and a DC boost function. The design allows 800-V EVs to use a 400-V charging infrastructure. More recently, Rohm announced the availability of its Gen 5 SiC MOSFETs, targeting xEV traction inverters with a further reduction of RDS(on) at high temperatures.

Cambridge GaN Devices (CGD) has developed a 650-V ICeGaN device for automotive applications, including xEV traction inverters. While the device offers a lower blocking voltage than SiC counterparts, according to CGD, it can be paralleled without suffering imbalance or requiring a careful selection of components with similar characteristics.

The ICeGaN device integrates protection features, offering low RDS(on) (9 mΩ), reduced losses, and improved thermal management. CGD has also showcased a multilevel, 800-V inverter based on this device that can power electric motors to over 100-kW peak, 75-kW continuous power.

OBCs: SiC and GaN share the space

The OBC converts the AC grid power to the DC required by the main battery. It operates at lower power (typically 11 kW to 22 kW for passenger cars) than the traction inverter. SiC MOSFETs are currently the most common choice for OBCs, particularly for 800-V battery systems in which 1,200-V SiC devices offer wide operating margins and high reliability.

Rohm released a family of 750-V and 1,200-V SiC molded modules in the HSDIP20 package for xEV OBCs. The package integrates components for power-factor correction (PFC) and LLC conversion, including SiC MOSFETs, an insulating substrate, and decoupling elements. Available in four-in-one and six-in-one configurations, the modules reduce chip temperature by up to 38°C under a 25-W output load compared with discrete SiC MOSFETs.

Navitas Semiconductor announced the automotive qualification of its high-power GaNSafe Gen 4 ICs to both AEC-Q100 (integrated circuit) and AEC-Q101 (discrete transistor) standards (Figure 3). The GaNSafe family integrates control, gate drive, and sensing. It also offers short-circuit protection with a maximum latency of 350 ns, 2-kV ESD protection on all pins, programmable slew rate control, and elimination of the negative gate drive requirement. The devices are suited for OBCs and HV-LV DC/DC converters.

Navitas also introduced the first production-ready, 650-V bidirectional GaNFast ICs with IsoFast isolated gate drivers. This solution enables OBCs with a single-stage AC/DC topology that eliminates the conventional two-stage approach (with a separate PFC and DC/DC).

Figure 3: For the automotive-qualification process, Navitas prepared a reliability report with over seven years of production and field-data analysis. (Source: Navitas Semiconductor)

DC/DC converters and 48-V systems: GaN gains ground

GaN power devices are being adopted in DC/DC converters for EV applications. In EVs, DC/DC converters step down the main 400-V or 800-V battery bus to 12 V or 48 V for vehicle auxiliary loads. For example, Vitesco Technologies selected Infineon Technologies AG’s CoolGaN 650-V devices for its Generation 5+ DC/DC converter. The fast-switching capability of GaN at 650 V enables higher converter switching frequencies, which reduces the size of the magnetics and filtering capacitors.

Figure 4: Infineon’s 100-V CoolGaN transistors target applications such as zone control and main DC/DC converters, auxiliary systems, and Class D audio amplifiers. (Source: Infineon Technologies AG)

Also, auxiliary vehicle systems, such as electric power steering pumps, cooling fans, HVAC compressors, and 48-V mild hybrid motor generators, operate at voltage levels at which 100-V- to 200-V-rated GaN devices are fully suitable today.

One example is Infineon’s automotive-grade GaN transistor family that is suitable for low-voltage subsystems, such as 48-V and auxiliary power rails in hybrid and full-electric vehicles. These CoolGaN 100-V G1 devices (Figure 4) are qualified to AEC-Q101.

SiC is also used in DC/DC converters, particularly for bidirectional designs operating from 800-V battery systems. The Rohm HSDIP20 molded modules mentioned in the OBC section are specified for both PFC and LLC DC/DC stages, making them suitable for OBC and DC/DC converter applications.

Final considerations

After reaching mature status as a semiconductor technology, the primary challenge for SiC has shifted to scaling up production volume and reducing costs. The wide adoption of SiC in the automotive industry has forced the transition from older, 150-mm to 200-mm wafers, with the setup of dedicated fabs. This shift significantly reduces the cost of single chips and increases the yield per wafer. Wolfspeed has advanced production by developing the industry’s first 300-mm, single-crystal SiC wafer.

In the past, GaN faced issues with automotive adoption due to a lack of long-term field-reliability data. As mentioned, several GaN devices have successfully achieved AEC-Q101 and AEC-Q100 qualifications. Moreover, several manufacturers, including Texas Instruments Inc., are claiming a failure-in-time rate for GaN devices below 1 (fewer than one failure per billion device-hours) over a targeted 10- or 15-year lifecycle.

Interestingly, GaN is also experiencing a 300-mm trend. Because GaN is typically grown on top of standard silicon substrates (GaN-on-Si), companies such as Infineon have scaled 300-mm GaN power wafer technology by using existing silicon manufacturing lines, lowering the device costs.

Imec also announced plans to manufacture 300-mm GaN wafers with several partners, including Aixtron, GlobalFoundries, KLA Corporation, Synopsys, and Veeco. The GaN program will focus first on using 300-mm Si(111) as a substrate for low-power applications, followed by high-power applications (650 V and above) using 300-mm semi-spec and CMOS-compatible QST­ engineered substrates from Qromis Inc.

The post How SiC and GaN are reshaping automotive power electronics appeared first on EDN.

This is hackxpansion, it is powered by the RP2354B, has a 2" 240x320 LCD, and a ≈6h battery life while in use. It has two modules slots by default, and I'm currently working on a back attachment (last photo) which add two more slots, but I think the postal company lost one of my packages that had the parts for it😭 The modules connect with a 2x7 2.54mm header, this way you don't even need to make a pcb to create new modules, just use a pref board, and you can just plug in dupont cables directly into the device. Each module has two resistors, which when connected each become the top resistor of a voltage divider, a 12bit ADC measures the resulting voltages, and loads the correct driver for that module. The firmware is written in rust, in a way so that writing new drivers and apps is really easy, and can be done in external crates. Each app can depend on drivers, and only be loaded into the app list, if all their requirements are met. I tried porting a NES emulator, but there doesn't exist an emulator written in rust currently that actually performs well on this hardware, I got an average of 12FPS in Super Mario Bros. I've collabed with Hack Club, and I will be running a program where if you are a teen 13-18(inclusive) and design 4 modules, you get funding to make these modules and get one of these consoles for free. The program will start on Aug 3 and end on Aug 31. If you want to participate [RSVP] now! (https://meko.fillout.com/hackxpansion). Repo Website(docs WIP) submitted by /u/K0eg [link] [comments]

Based on BrainChip’s Akida neuromorphic engine, the AKD1500 edge AI coprocessor is now available in a compact M.2 2230 (22×30 mm) form factor. The card features a B+M key edge connector for use with Raspberry Pi 5 and compatible host systems, enabling fanless, plug-and-play AI acceleration without redesigning existing power or cooling systems.

Operating from a 3.3-V supply, the AKD1500 M.2 card connects through a two-lane PCIe Gen2 host interface. The coprocessor integrates 32 neural processing units (NPUs) and delivers up to 800 effective GOPS (INT4) for edge AI inference. It also includes 1 MB of dual-port on-chip memory and consumes a typical 250 mW at 400 MHz.

The event-based digital architecture delivers ultra-low-power acceleration for standard neural network models and supports on-device learning, enabling applications to adapt without a cloud connection or full model retraining. Models are developed and optimized using BrainChip’s MetaTF software flow with TensorFlow/Keras and PyTorch front ends before deployment to the card.

The AKD1500 M.2 card with a B+M key connector is priced at $129 and is available for purchase directly from BrainChip.

AKD1500 M.2 product page 

BrainChip Holdings 

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Advantech is launching a portfolio of servers based on AMD EPYC 9006 SP8 processors to support next-generation AI infrastructure. Servers, such as the SKY-924E5F, provide the scalability and reliability required for AI, high-performance computing (HPC), networking, and mission-critical industrial workloads.

EPYC 9006 series server CPUs feature up to 128 Zen 6 or Zen 6c cores, 256 threads, and 2-nm process technology, delivering up to a 20% average performance improvement over the previous generation and up to a 20% performance-per-watt improvement. These processors enable more virtual machines, higher throughput, and improved system efficiency. With up to 128 PCIe Gen6 lanes per CPU, CXL 3.1 memory expansion, and support for DDR5-8000MHz and MRDIMM-12800MHz, the EPYC-powered servers provide balanced compute, memory, and I/O performance.

The edge servers support GPU-accelerated AI workloads and AFA-ready high-density E1.S/E3.S NVMe SSD storage. The lineup includes the following models:

• SKY-642E5, 4U MGX GPU server for large-scale AI acceleration
• SKY-722E5, 2U DC-MHS server with DC-SCM capability for modular data center and edge AI deployments
• SKY-712E5, 1U DC-MHS server with HHHL and FH-3/4L expansion card compatibility for high-density enterprise edge and cloud workloads
• SKY-822E5, 2U short-depth DC-SCM modular server accommodating 2–3 dual-slot GPU cards for space-constrained edge data centers
• SKY-924E5F, 2U 4-node front-access server for distributed edge computing
• ASMB-982 and ASMB-832, server boards for flexible, expandable system designs

A timeline for server availability was not provided at the time of this announcement. Learn more about the Advantech + AMD EPYC 9006 platform here.

Advantech

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Optimized for physical AI, AMD’s Ryzen AI Embedded X100 series processors combine CPU, GPU, and NPU resources on a single embedded SoC. With up to 16 AMD Zen 5 CPU cores, an integrated GPU, power-efficient NPU, and unified memory, the processors handle perception, reasoning, and real-time control workloads in robotics, industrial automation, aerospace and defense, and other embedded systems.

AMD says the series delivers up to 2.1× higher multithreaded CPU performance, 1.7× higher graphics performance, and 3.5× higher AI token generation with 1.4× faster time-to-first-token than Intel Core Ultra Series 3 processors. The company also says the processors provide up to 3× higher peak FP32 performance than the NVIDIA Jetson T5000 and an average 1.7× faster beamforming for cardiac ultrasound than the NVIDIA RTX 4000 Ada.

The Ryzen AI Embedded X100 series supports an open software stack with Linux, the AMD ROCm GPU software stack, the Xen Hypervisor, and AI frameworks including PyTorch, ONNX, and TensorFlow. AMD also provides tools to migrate CUDA codebases to ROCm.

Production availability of the Ryzen AI Embedded X100 series is expected in Q4 2026. Learn more about the processors on the product page or in AMD’s technical blog.

Advanced Micro Devices 

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