Microelectronics world news

Keysight’s NA5305A and NA5307A mmWave modules extend PNA and PNA-X vector network analyzers (VNAs) to 170 GHz and 250 GHz, respectively. Paired with the 85065A 0.5-mm coaxial calibration kit and N5292A test set controller, the frequency extenders enable fully calibrated single-sweep broadband S-parameter measurements from 100 kHz (or 10 MHz) up to 250 GHz.

The broadband VNA accessories simplify test setups and enable engineers to characterize on-wafer or packaged components at sub-THz frequencies. They also help accelerate the design and validation of 1.6‑Tb/s and 3.2‑Tb/s components and next-generation semiconductors.

Test configurations provide a dynamic range of 105 dB at 170 GHz for passive components, high-rejection filters, and active devices. Differential measurements help validate active devices and high-speed interconnects, with maximum output power of 0 dBm at 170 GHz and –5 dBm at 220 GHz.

Existing 110‑GHz and 120‑GHz VNA users can easily upgrade their configuration to preserve their original investment.

NA5305A product page  

NA5307A product page 

Keysight Technologies 

The post Frequency extenders boost VNA range to 250 GHz appeared first on EDN.

SiFive’s Intelligence Gen 2 RISC-V IP portfolio combines scalar, vector, and matrix compute to accelerate AI workloads. The Gen 2 lineup includes the new X160 and X180, alongside the upgraded X280, X390, and XM series. All products feature enhanced scalar and vector processing, while the XM series adds a highly scalable matrix engine.

With up to four cores, the 32-bit X160 and 64-bit X180 target embedded IoT at the far edge. They deliver high efficiency in a compact footprint, extending AI to automotive, robotics, and industrial automation. Their vector engine boosts AI model performance with minimal power and area overhead.

Intelligence Gen 2 products span a wide range of performance, area, and power options within a single scalable Instruction Set Architecture (ISA). Features include a dual-issue, in-order eight-stage superscalar pipeline, narrow-to-wide vector engines, and the XM series’ scalable matrix engine for diverse AI workloads. The CPUs also support the SiFive Scalar Coprocessor Interface (SSCI) and Vector Coprocessor Interface eXtensions (VCIX) to link external AI accelerators and coprocessors.

All five Intelligence Gen 2 products are now available for licensing, with first silicon expected in Q2 2026.

Intelligence Gen 2 product page

SiFive 

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It was a classic stand-off. Vendor number one’s system wasn’t talking to vendor number two’s. What to do? Of course! Blame the customer’s network!

I worked for a TV station that was part of a group run by a common owner. One of the stations in the group used a system known as production automation, which allowed a single operator to control all of the equipment in the control room during newscasts. That would include the video switcher, audio console, camera robotics, video playback, lighting, and graphics generators. The computer system in the newsroom takes the scripts written by reporters and producers, generates a sequence called a rundown, and transmits and updates it in real-time to the automation system.

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

While performing a major update to one of the systems, communication stopped. Head scratching ensued for a while, and then the two vendors decided the problem must be something in the network that was blocking the IP packets. The station’s engineers pointed out that nothing had been changed in their network, and in any case, there was no internal routing or filtering going on. Not good enough, say the vendors. Prove to us it’s not your fault before we continue. Their advice was to install a copy of Wireshark, analyze the packets, and show us that the path between the systems is clear.

That’s reasonable as far as it goes, but Wireshark is a mighty powerful tool, and it is not for the faint of heart. At the local TV station level, the IT staff generally does not have the expertise needed to fire it up quickly and interpret its results. The station group’s central IT networking folks do, but getting them involved would have taken a good deal of time, and if they had to travel to the site, expense.

I was just a bystander to this. My own station was one of those with the same systems, so I was included in all of the emails flying back and forth. As it happens, not long before this incident, I had written a small one-trick pony Windows utility. All it did was send IP packets from one computer to another via a specific port. As seen in Figure 1, if the path is clear, the receiving computer replies, and the arrows move. Simple as that.

Figure 1 A demonstration of the Windows utility written by the author, sending IP packets from one computer to another via a specific port.

I sent the program to the station’s IT director, and in less than half an hour, he installed it on both systems, checked all of the ports the vendors specified, and found them all clear. With no more finger-pointing at the customer, the vendors had to get to work to find the actual cause of the problem, which turned out not to be network-related.

A few notes about the program. The image shown is just a demonstration, with both ends running on the same machine. In real life, one copy would be on each of two machines on the network, across the room, or across the world. Also, to be honest, I probably spent more time getting the ballistics of the arrow movement looking good than on the rest of the program.

Robert Yankowitz retired as Chief Engineer at a television station in Boston, Massachusetts, where he had worked for 23 years.  Prior to that, he worked for 15 years at a station in Providence, Rhode Island.

Related Content

• The mysterious MS-DOS reboot
• TV transmitter trouble
• A network-protocol primer
• IP Resilient Network Fundamentals–Part III

The post Between two vendors appeared first on EDN.

My 12S BMS (BQ76952) works and I can turn on the fets via I2C. Unfortunately I accidentally used a 6.3V tantalum on the 12V buck output which caused this catastrophic failure. submitted by /u/fishychair [link] [comments]

The 8-inch specialty foundry DB HiTek of Seoul, South Korea says it is in the final stages of development of its 650V E-mode gallium nitride (GaN) high-electron-mobility transistor (HEMT) process for power semiconductor applications. Also, at the end of October, the firm is offering a dedicated GaN multi-project wafer (MPW) program...

The rise of electric vehicles goes hand-in-hand with intelligent connectivity, and at AutoEV Bharat 2025, Telematics Technologies are positioned to be the cornerstone of India’s EV ecosystem. In essence, it refers to a comprehensive set of IT solutions, applications, and services designed to transform vehicles into connected platforms-enhancing safety, efficiency, and user experience.

At the center of it is the Telematics Development Environment and Tools, enabling manufacturers to design, simulate, and test connected vehicle systems before their deployment. These advanced development frameworks ensure reduced human error, increased system reliability, and faster innovation.

These also include In-Vehicle Operating Systems. AutoEV Bharat 2025 exhibits OS solutions that coordinate vehicle control, infotainment, and driver assistance systems for seamless operation across multiple vehicle platforms. Furthermore, these operating systems host AI and machine learning applications that manage driving behaviour and energy efficiency.

The HMI was introduced to enhance driver interaction with their respective electric vehicles. From smart dashboards to touch-sensitive panels, different HMI solutions exhibited at AutoEV Bharat 2025 offer intuitive control while minimizing driver distraction. Voice command, gesture recognition, and augmented reality overlays for navigation are considered the highest level of HMI design.

Communication Modules and Security Systems provide a real-time connectivity interface between vehicles, infrastructure, and cloud services. V2X communication allows coordination of traffic, accident avoidance, and predictive maintenance.

Telematics Services and Drive Recorders, and Digital Tachograph Systems provide actionable information to fleet operators. The systems keep track of speed, location, driving patterns, and vehicle health to efficiently reduce operational costs. The telematics technology backbone for autonomous driving provides real-time sensor fusion, path planning, and system diagnostics.

By demonstrating these technologies, AutoEV Bharat 2025 demonstrates that connected vehicles in India will be safer, smarter, and more efficient, paving the way for autonomous, data-driven mobility solutions.

The post Revolutionizing Electric Vehicle Intelligence through Telematics at AutoEV Bharat 2025 appeared first on ELE Times.

The semiconductor sector remains highly vulnerable to global uncertainty. The consumer electronic to automotive production industry can disrupt due to the single chip shortage. Conventional procurement methods, which depend on manual forecasting and historical trends, often fall short in responding to market volatility, resulting in prolonged lead times and abrupt supply chain breakdown.

Today, artificial intelligence (AI) is increasingly being adopted as a strategic procurement “Copilot”-enhancing rather than replacing human expertise by delivering augmented decision-making that improves agility and precision in decision making.

Why Procurement Needs AI Now

The semiconductor industry has a far more complex procurement function than that in other industries. Lead times for critical components often stretch from 12 to 52 weeks. This complexity stems from wafer fabrication facility(fab), which require months of advance scheduling, while demand can swing dramatically due to market shifts or geopolitical events.

Now, the emphasis is on how AI makes procurement a system that is intelligence-driven and predictive. “AI is moving procurement from hindsight to foresight, enabling leaders to anticipate disruptions before they occur,” according to Deloitte.

AI-driven procurement systems are replacing procurement methods like static supplier scorecards and spreadsheets with dynamic data-driven platform. They can integrate real-time data from wafer fabs, suppliers, logistics providers, and even macroeconomic indicators to provide predictive analytics. This enables procurement leaders to anticipate shortages, rebalance supplier portfolios, and minimize risks, which helps leaders prevent disruption and optimize sourcing strategies before they escalate into crises.

The Procurement Copilot in Action

1. Predictive Analytics for Lead Time Optimization

In order to generate extremely precise lead time projections, AI-driven systems can process thousands of variables, ranging from silicon wafer availability to equipment maintenance schedules. Procurement teams can use this information to proactively plan production cycles and secure crucial inventories, rather than depending just on supplier updates. According to industry case studies, leading companies have significantly reduced the risk associated with supply bottlenecks by using predictive models to minimize procurement cycle times by up to 20%. Jackie Sturm, Intel’s vice president of supply chain says, “predictive AI is helping us plan weeks ahead instead of reacting days late.”

1. Supply Chain Resilience Through Risk Mitigation

Supply chains for semiconductors are particularly susceptible to interruption. Global production lines can be stopped by a single sub-supplier. Dashboards with AI capabilities can identify possible hazards early. These include delays in logistics, geopolitical unrest in East Asia, and an excessive reliance on particular wafer fabs.  Procurement professionals may improve supply chain resilience and diversify their sourcing strategy by using AI to simulate “what-if” scenarios. According to McKinsey, “AI-driven procurement enables companies to respond to crises with greater agility than ever before.” It also reduces disruption-related losses by up to 40%.

1. Wafer Fab Scheduling and Production Alignment

Scheduling for wafer fabs entails thousands of interconnected process steps spanning extremely expensive machinery. AI can greatly improve this scheduling by identifying operational trends that minimize idle time and maximize overall throughput. Procurement leaders can better coordinate upstream suppliers and downstream manufacturing partners by using these data to align sourcing contracts with fab schedules.

1. Strategic Sourcing and ROI Impact

AI in procurement allows for more intelligent, data-driven investment decisions in addition to cost reduction. AI can find high-value supplier relationships by analyzing the total cost of ownership, which takes into account supplier performance, tariffs, and logistics. Within the first two years of implementing AI in procurement, early adopters have claimed ROI gains of 10–15% due to reduced inventory holding costs and more successful contract negotiations. As Gartner emphasized in its 2024 research, “AI-augmented sourcing is now a boardroom priority, driving measurable returns on resilience and efficiency.”

Global and Indian Context

 AI-enabled procurement systems and automation have been implemented by semiconductor industry leaders such as Taiwan Semiconductor Manufacturing Company (TSMC) and Intel in their wafer fab operations. In order to create a domestic semiconductor ecosystem, the Indian government has allocated around ₹76,000 crores under the Semicon India program, in which procurement would be crucial.  For Indian companies entering chip design, packaging, and fabrication, AI-driven procurement tools can enhance forecasting, supplier management and logistics optimization, helping to achieve bridge gaps in global competitiveness.

Take the proposed Vedanta semiconductor fab in Gujarat as an example. Success for such, s project depends upon on procurement systems capable of handling long lead times for fab equipment, fluctuating global wafer supply, and complex logistics.  An AI- driven procurement Copilot can provide the foresight and agility necessary to mitigate risk and ensure projects remain on schedule despite global uncertainties.

Challenges Ahead

The AI adoption in procurement is not as easy as it seems as it is encountering with several hurdles. In terms of the fragmented supplier network the data quality and availability remain among the major constraints.

For the purpose of smooth integration, many small and medium- sized suppliers lack the digital infrastructure required. Procurement leaders must carefully balance human judgment with AI -driven insights, especially when navigating geopolitical uncertainties or making long-term strategic sourcing choices.

Another significance obstacle is change management. Team in charge of procurement who are used to traditional negotiation methods could be hesitant to depend on    AI- generated insight. Transparent model outputs, explainable decision logic, and a clear demonstration of return on investment(ROI) are necessary to foster trust in AI Copilot.  As stated by Gartner “Responsible AI governance guarantees that AI stays an enabler, rather than a black box, keeping humans informed and accountable.

The Road Ahead

As semiconductor becomes the foundation of the digital economy, procurement is evolving from a cost-centric function to one focused on its ability to build resilience and agility. The procurement teams to move from reactive decision- making to proactive, data- driven strategies with the help of strategic procurement Copilot.  AI enables leader to make decisions with more accuracy and assurance by combining risk mitigation, strategic sourcing, and predictive analytics.

In India, where semiconductor manufacturing identified as a national priority, AI-driven procurement can translate policy goals into industrial capability. Early adopters of AI Copilot in procurement will enhance supply chain resilience and enhance their global competitiveness in the semiconductor value chain.

The post AI as the Procurement Copilot: The Next Leap in Semiconductor Supply Chains appeared first on ELE Times.

“Productizing an IP, to make it into a product, is where the money is,” remarks Raja Manickam, a semiconductor industry veteran with 4 decades of industry experience and Founder & CEO, iVP Semi, in an exclusive interaction with the ELE Times at Semicon India 2025. This is amidst the central government empowering the semiconductor industry through various schemes, including the DLI scheme with a capital outlay of around Rs 1,000 Crore.

Emphasizing his global outlook, Mr. Manickam draws parallels with chip giants to frame India’s semiconductor journey within a larger global vision. He asserts, “Creating IP is not the issue for us at all. The myth is that we need IP to make a product.” Challenging this notion, he stresses that IP alone does not define standards in the semiconductor industry. Instead, he urges India to focus on building stronger pathways to productization, which he believes is key to enabling a complete and sustainable ecosystem.

Focus On Substantial Value Addition

By drawing on examples of global chip brands, he reimagines India’s journey in semiconductors and electronics through a global lens.“Product companies make the most money out of the whole value chain and can build globally recognizable brands like NVIDIA or AMD,” he explains. Highlighting how every semiconductor crosses countless stages before becoming part of a final product, he points out that true value lies not just in designing chips but in building strong product companies that can scale globally.

He also refers to his company, iVP Semi, which emphasizes developing tangible products such as DC-DC inverters, relays, solid-state relays, power modules, and powertrains, instead of pursuing an IP-licensing model. iVP Semi reflects a deliberate and measured vision, shaped by Mr. Manickam’s long-standing commitment to fostering homegrown product companies.

With this perspective, he calls attention to the pressing need for a holistic semiconductor ecosystem—one that nurtures both talent and value creation, anchored in a long-term and reliable vision.

Figuring Out the Systems Approach

He says,” To make a chip, they need multiple IPs. They may have one IP or they may not even have an IP,” referring to the chip giants. “But they have figured out how to put all these IPs together and make a product,” he adds, further validating his stance.

In the conversation surrounding Design IPs, he seems to have a certainly different opinion that focuses on realigning India’s semiconductor ambitions towards realizing a systems approach that holds higher potential and can garner substantial and long-term value for the Indian talent and economy, both.

Focus on Startups

With this approach in mind and a quest to see India reach this potential, he urges big companies and corporations to adopt small companies and help them with capital and talent, both to realise this dream. He says,” So, my philosophy is to adopt these guys. But don’t look at it from an ROI,” as the conversation wraps up.

Raja Manickam, an IIT Kharagpur graduate, is a semiconductor veteran who founded Tessolve in 2003, growing it into a 1,000-crore global leader before its acquisition by Hero Electronix. He later served as the first CEO of TATA Electronics OSAT and founded Ponni Tech Consultants in 2023. In 2024, he launched iVP Semi to localize chip production and drive India’s semiconductor self-reliance. His vision is to build a robust ecosystem that attracts global partners to India.

 

The post Exclusive Insights: Design IPs Vs Productization? Raja Manickam at Semicon India 2025 Says Focus on Productization appeared first on ELE Times.

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has announced the commercial launch of its 200mm SiC materials products, marking a milestone in its mission to accelerate the industry’s transition from silicon to silicon carbide. After initially offering 200mm SiC to select customers, the firm says that the positive response and benefits warranted a commercial release to the market...

Automotive radar systems have become a cornerstone of advanced driver-assistance systems (ADAS), enabling object detection, collision avoidance, blind-spot monitoring, and adaptive cruise control. As vehicle autonomy advances toward higher SAE levels, radars are evolving with greater resolution, longer range, and multi-object tracking capabilities. But with this leap in performance comes the pressing challenge: how to test these increasingly complex systems with the accuracy and repeatability needed for safe deployment on public roads.

Technology Environment: 24 GHz to 77 GHz and Higher:

The environment of automotive radar is changing quickly. Due to bandwidth constraints and stricter spectrum laws, traditional 24 GHz radars once common for short-range applications like parking assistance and cross-traffic alerts are currently being phased out.

Radars operating at 77 GHz are replacing them as the new norm. They provide a greater bandwidth, longer detection ranges, better range resolution, and more robust interference resistance. For mid- to long-range ADAS features like adaptive cruise control, lane-change assistance, and automated emergency braking, they are therefore essential. However, there is a cost and design complexity trade-off.

At the same time, radar sensing has evolved from 2D to 4D imaging radar. Conventional 2D radars could measure distance and velocity but lacked elevation, limiting object classification in dense traffic. By contrast, 4D imaging radars measure distance, velocity, azimuth, and elevation simultaneously producing LiDAR-like point clouds enriched with Doppler data. This technology thrives in poor weather conditions like fog, rain, or snow, where optical sensors struggle, making it indispensable for L2+ through L4 autonomy.

Radar Test Architecture:

Radar Test Architecture for Automotive Applications: Phase-Coherent Multichannel Signal Generation, LO Distribution, and Parallel Receiver Testing with Automation Flow

This diagram illustrates a radar test setup optimized for automotive radar validation. It begins with multichannel vector signal generators that ensure phase coherence and support cascading for scalable configurations. The signals are routed through an LO distribution divider, feeding synchronized local oscillator signals to multiple vector signal analyzers for parallel receiver testing. At the base, an automation controller manages the test flow, enabling throughput optimization across channels.

Latest Trends in Radar Testing:

As radar performance expands, testing methodologies are transforming as well. Today’s radar testers are not only tasked with validation under ideal conditions but also with simulating real-world unpredictability before vehicles even hit the road.

1. 4D Radar Simulation

Virtual test environments can replicate rain, snow, fog, and multipath reflections that are impractical to test on real roads. These simulations are vital for developing next-gen 4D radars.

2. Hardware-in-the-Loop (HiL) Testing

HiL connects real radar hardware with a simulated driving environment. This allows engineers to test radar responses to cars, pedestrians, and traffic scenarios entirely in the lab—reducing cost and speeding up development.

3. AI-Enhanced Radar Validation

AI plays an increasing role by detecting subtle anomalies in radar signals, generating rare accident-like scenarios, and predicting radar degradation. This accelerates validation cycles compared to manual testing.

4. Sensor Fusion Testing

Since radars rarely operate alone, test systems now validate how radars integrate with cameras and LiDAR. Ensuring all sensors remain synchronized and error-free is critical to the safety of self-driving systems.

Industry Insights: Keysight Technologies at the Forefront

As automotive radar systems evolve to meet rising demands for higher resolution and precision, Keysight Technologies stands at the forefront of testing innovation. With chipset vendors adopting cascading architectures to boost transmit and receive channel counts, radar complexity is increasing alongside the need for more rigorous and extended test cycles. Natarajan Mahesh from Keysight’s Radar Testing Team highlights this shift as a key challenge in next-gen radar development.

“Automotive radar chipset vendors are looking to increase the transmit and receive channel count to cater to the increasing demand for better resolution using methods such as cascading radar chipsets. The higher channel count of receiver and transmitters will essentially mean more test time.” — Natarajan Mahesh, Radar Testing Team, Keysight Technologies

Keysight Technologies is addressing this challenge with specialized solutions that balance complexity with efficiency:

• Coherent Multichannel Signal Generators – providing compact, phase-aligned outputs with excellent phase noise.
• Local Oscillator Distribution – delivering stable, low-noise signals for cascading architectures.
• Simultaneous Multi-Channel Stimulus – enabling parallel receiver testing and cutting down test duration.
• Radar-Specific Test Automation – supporting MIMO radar, FMCW waveforms, and Doppler emulation.

Keysight also extends its scope into cybersecurity with its SA8710A Automotive Cybersecurity Test Platform, ensuring that radar systems in connected vehicles are validated not just for performance but also for resilience against digital threats.

“Keysight Technologies has solutions for the autonomous vehicle and in-vehicle communication systems, of which radar is one of the most critical sensors.”

Natarajan Mahesh, Radar Testing Team, Keysight Technologies

Future Outlook:

• Fully Virtualized Validation: AI and physics-based simulations work together to provide nearly comprehensive test coverage prior to in-person trials.
• 5G-Connected Testbeds: over-the-air (OTA) firmware optimization and cloud-based radar analytics.
• Automated Test Labs: these robotic devices simulate targets dynamically from various perspectives.
• 4D radar standardization: frameworks for industry-wide certification that establish consistent performance benchmarks.

Conclusion:

Automotive radar testers are critical enablers of the next wave of ADAS and autonomy. As radars evolve from basic range-speed sensors to high-resolution 4D imaging systems, test platforms must evolve as well becoming simulation-rich, AI-driven, and fusion-aware.

Companies like Keysight Technologies are leading this transformation, ensuring that radar-equipped vehicles perform safely, reliably, and securely under all conditions ultimately paving the way toward fully autonomous driving.

The post Why Cascading Chipsets and Fusion Testing Define the Next Era of Automotive Radar appeared first on ELE Times.

Electric vehicles (EVs) are no longer defined merely by battery size or driving range. At the very heart of their performance, efficiency, and intelligence lies the traction inverter a masterpiece of power electronics that converts DC from the battery into precise AC waveforms for motor drive.

What makes the inverter even more critical today is its evolution into a software-defined energy hub. Beyond simple power conversion, modern inverters integrate advanced semiconductors, AI-driven control, and bidirectional energy flow, turning EVs into smart, grid-ready assets.

Technologies Reshaping Inverter Design:

1. Wide-Bandgap Semiconductors: SiC and GaN

• The transition from traditional silicon to wide-bandgap (WBG) materials such as Silicon Carbide (SiC) and Gallium Nitride (GaN) is revolutionizing inverter efficiency and compactness.
• SiC MOSFETs support high-voltage (up to 1200 V) operation, offer lower switching losses, and provide high thermal endurance. This enables smaller form factors, decreases cooling system requirements, and facilitates ultra-fast charging.
• GaN HEMTs are known for their high-frequency switching, which makes e-axles and multilevel inverter topologies more compact. They’re emerging in light EVs and auxiliary systems where space is at a premium.

These devices can achieve switching frequencies above 500 kHz, unlocking higher power density and smaller passive components. While SiC has already become standard in 800 V platforms, GaN is set to complement it in next-gen EV systems.

2. AI-Based Predictive Control

In the realm of inverters, artificial intelligence is ushering in new operational paradigms. With Model Predictive Control (MPC) and machine learning at the helm, contemporary inverters:

• Mitigate torque ripple and switching losses
• Adapt in real-time to driving dynamics, component wear, and thermal conditions
• Support over-the-air (OTA) updates, ensuring inverter functionality is fine-tuned for the vehicle’s entire lifespan

Furthermore, AI-augmented control integrates perfectly with battery management and regenerative braking systems, facilitating enhanced, safer, energy-efficient driving.

3. 800 V Architectures: Faster, Cooler, Smarter

• The industry’s shift to 800 V platforms marks a significant leap in EV capability:
• Enables 200–350 kW ultra-fast charging with minimal I²R losses
• Reduces cable thickness and weight, improving efficiency
• Achieves 10–15 min charging to 80% capacity

In such high-voltage environments, SiC-based inverters thrive achieving >98% efficiency while maintaining robust thermal stability.

4. Bidirectional Energy Flow: Beyond Mobility

Modern traction inverters are designed for four-quadrant operation, unlocking multiple use cases:

• Vehicle-to-Grid (V2G): Supplying power back to the grid
• Vehicle-to-Home (V2H): Acting as an emergency or renewable energy backup
• Vehicle-to-Load (V2L): Powering tools or appliances on the go

These applications require adherence to global standards like IEEE 1547 and ISO 15118, alongside isolation and fault-tolerance mechanisms. In effect, EVs are becoming mobile energy storage units, supporting energy resilience and sustainability.

5. Integrated E-Axle Designs

OEMs are increasingly adopting integrated e-axle solutions that combine inverter, motor, and gearbox in a single compact package. Benefits include:

• Reduced parasitics and cabling losses
• Shared cooling and thermal management
• Lower manufacturing complexity and cost

This architecture improves torque density and space efficiency—ideal for both urban EVs and high-performance electric sports cars.

6. Modular Inverter Architectures

Scalability is key for automakers producing EVs across different segments. Modular inverter platforms allow:

• Power scaling from 75 kW to 300 kW
• Reuse of software, control logic, and digital stages
• Faster time-to-market and lower R&D costs

This flexibility helps OEMs deploy multi-platform strategies, from two-wheelers to heavy-duty trucks, with automotive-grade reliability.

EV Traction Inverter Architecture:

Block diagram of an EV traction inverter system showing torque command flow from VCU to traction motor via Safe MCU, SiC FETs, gate drivers, and resolver-based feedback.

This diagram illustrates how torque commands from the Vehicle Control Unit (VCU) are processed by a safety-optimized microcontroller (Safe MCU), which drives high-voltage SiC FETs through isolated gate drivers. These switches convert DC from the battery into 3-phase AC for the traction motor. Resolver and current sensing provide real-time feedback, enabling precise motor control and efficient bidirectional energy flow.

System-Level Trends:

Beyond materials, traction inverter innovation is increasingly system-driven:

• Bidirectional Charging & V2G: SiC and GaN enable energy flow back to the grid, turning EVs into mobile storage units
• Integrated Powertrains: OEMs are combining inverter, motor, and gearbox into unified modules for space and weight savings
• Cooling Innovations: Double-sided cooling and optimized thermal paths are reducing module size and improving reliability
• Software-Defined Inverters: Adaptive control algorithms are enhancing efficiency across driving conditions

Industry Spotlight: Infineon Technologies

To understand how traction inverter technology is evolving in the EV sector, Hans Adlkofer, Senior Vice President of Automotive Systems at Infineon Technologies AG, shares his perspective. He explains the technological shifts driving more efficient, compact, and bidirectional inverters, and how these advancements are shaping the future of electric powertrains.

“We can expect even more compact and efficient traction inverter designs. The shift from traditional IGBTs to Silicon Carbide (SiC) is driven by the need for higher performance, reduced size, and increased EV range. Fusion of IGBT and SiC technologies in a single module also optimizes cost-performance. Gallium Nitride (GaN) will further support advanced inverter topologies, including multi-level designs.”

“The transition to SiC and GaN opens the space for innovative module development, such as smaller or optimized cooled modules. Discrete solutions allow very compact inverter designs or integration directly into the motor. This contributes to higher efficiency, lower cost, and increased mileage.”

“Latest SiC and GaN products enable bidirectional charging, supporting intelligent V2G use cases. EVs can now act as mobile energy storage units, creating a more sustainable energy ecosystem and new business models for battery utilization.”

Hans Adlkofer, Senior Vice President Automotive Systems at Infineon Technologies AG

Conclusion:

Traction inverters are no longer functioning solely to change DC to AC traction inverters have effectively become the brain centre of an electric vehicle’s power train. They are changing electric vehicle performance and energy management with wide-band gap semiconductors, AI predictive control, modular system designs, and energy flow that is bidirectional.

As automakers focus on increasing charging speed, boosting range, and developing more intelligent energy systems, traction inverters will be instrumental in the renaissance of electric vehicles.

The post Next-Gen EVs Run on Smarter, Smaller, and Faster Traction Inverters appeared first on ELE Times.

Devices Reduce Power Losses in High Voltage Generators for Industrial and Medical Applications

Vishay Intertechnology, Inc. introduced a new series of Class 1 radial-leaded high voltage single layer ceramic disc capacitors that deliver a low dissipation factor (DF) and DC bias for industrial and medical applications.

Vishay Roederstein HVCC Class 1 series capacitors feature capacitance loss of < 25 % at 15 kV, which is half that of Class 2 devices. In addition, their < 1.0 % DF at 1 kHz is 0.5 % lower. The result is reduced power losses and high reliability in high voltage generators for baggage scanners, medical and industrial X-ray applications, air purifiers and ionizers, and pulsed lasers.

HVCC Class 1 series devices feature a capacitance range from 100 pF to 1 nF — with standard tolerances of ± 10 % — voltages of 15 kVDC, and an operating temperature range from -30 °C to +85 °C. The capacitors consist of a silver-plated ceramic disc with tinned copper-clad steel connection leads offering 0.65 mm and 0.80 mm diameters. The RoHS-compliant devices are available with straight leads with spacing of 9.5 mm and 12.5 mm, and feature an encapsulation made of flame-resistant epoxy resin in accordance with UL 94 V-0.

The post Vishay Intertechnology Class 1 Radial-Leaded High Voltage Single Layer Ceramic Disc Capacitors Feature Low DC Bias and DF appeared first on ELE Times.

From AI glasses to earbuds to long-range Bluetooth audio, three new product wins highlight how MEMS and RF front-end technologies are reshaping wireless sound.

Just finished the major components of my workbench. Me and my girlfriend build the desk from scratch and i put my electronics in the room. Still got some tidying up to do and run power to the 3d-printer and lab bench power supply to the far left submitted by /u/FluorescentApe [link] [comments]

ams OSRAM GmbH of Premstaetten, Austria and Munich, Germany has introduced the first product in its new Vegalas Power series of high-power laser diodes, targeting increasing popular projector applications such as immersive home movie experiences, automotive head-up displays or industrial machine vision. The firm now offers the full range of optoelectronic components required for projection: from light sources in various colors and power classes to sensors. In addition to projection, other application areas also benefit from the new laser diodes, including weed control or stage lighting...

The University of Massachusetts Lowell says that Electrical Engineering assistant professor Anhar Bhuiyan (who joined the faculty in fall 2023) is leading two US National Science Foundation (NSF) grants totaling $797,000 for research into next-generation power components for satellites and spacecraft — as well as for electronics on Earth...

From traditional fuses to eFuses, learn the advantages, limitations, and use cases of each technology to help you create reliable, space-efficient, and standards-compliant consumer products.

Nickel Cadmium (NiCd) batteries are widely used in consumer electronics due to their high energy density and long life. Constant current charging is often recommended by manufacturers. Several websites, including Wikipedia, suggest safely charging NiCd batteries at a 0.1C rate, meaning at 10% of their rated capacity, for 14 to 16 hours, instead of 10 hrs.

Slow charging does not cause a temperature rise, which may affect the life of the battery. More energy must be supplied to the battery than its actual capacity to account for energy loss during charging. Hence, 14 to 16 hours of charging instead of 10 hours.

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

The battery charger

Figure 1 gives the circuit for slow charging a NiCd battery pack with two AA-sized 1200-mAH cells. The battery is charged with 120 mA (10% of 1200 mA) constant current for about 15 hours.

Figure 1 The circuit for a low-cost NiCd (2 AA size 1200 mAH battery pack) slow charger with charge capacity indicator. Each segment in the U5 and U6 LED bar graphs indicates a charge capacity rise by 10%. As the charging current is constant, the time duration of charging indicates the charge capacity. After 10 hours, the battery should be fully charged. Charging for a few more hours than necessary supplies more energy to account for energy loss during charging. R2 and C1 are for the power ON reset of counters. The Vcc and ground pins of U2, U3, U7 and U8 are not shown here. They must be connected to 9-V DC and Vss, respectively. Time accuracy is not es. Each segment may glow for approximately 1 hour.

Every hour of charging is indicated by the glow of one LED bar graph segment (U5 and U6). After 15 hours, charging stops automatically. This is not a microcontroller-based circuit, so that even people without programming knowledge or a programmer device can build this circuit. A crystal-based timing circuit is not used here, as there is no necessity for time accuracy.

How it works

U1 is 555, configured as an astable multivibrator to generate a pulse train of width 0.88 seconds. R7 can be replaced by a 50K resistor and a 50K multiturn potentiometer in series for adjustment. LED D2 blinks at this rate. 

U7 divides this pulse train. Dividing by a 212 output at pin one yields a pulse train with a pulse width of 1 hour. U2A counts these pulses.

U3 is a 4- to 16-line decoder with an active LOW output. The selected output goes LOW, causing the corresponding bar graph segment to glow, while all other outputs remain HIGH. Since the 16th output at pin 15 of U3 remains HIGH, Q1 turns ON and the battery starts charging, and D1 begins glowing.

U4 is configured as a constant current generator. With R3 set as 10 Ω, the charging current is set at 100 mA, which is 10% of 1200 mA.

During the first hour, the output of U3 at pin 11 goes LOW, and the first segment of the LED bar graph U5 glows. After 1 hour, counter U2A increments once, and the output of U3 at its pin 9 goes LOW, which causes the second segment of the LED bar graph (U6) to glow.

This process goes on until the 15th segment glows to indicate the 15th hour of charging. When the 16th hour starts, the 16th output at pin 15 of U3 goes LOW, turning Q1 OFF.

Now charging stops, and the “Charging ON” LED D1 goes OFF. This LOW output, connected to U8B inverter input, outputs HIGH at the U2A clock input, which disables further counting. Hence, Q1 continues to be OFF. At this point, the battery is fully charged and becomes ready for usage.

Jayapal Ramalingam has over three decades of experience in designing electronics systems for power & process industries and is presently a freelance automation consultant.

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The post Low-cost NiCd battery charger with charge level indicator appeared first on EDN.

Decision tree learning algorithms are supervised machine learning algorithms that solve classification and regression problems. These models split up data through branches based on feature values until at the very end, a prediction is made; this setup closely aligns with human decision logic. Each internal node represents a decision based on a feature, whereas each branch represents results of that decision, and each leaf corresponds to a final prediction or class label. This intuitiveness makes them easily interpretable and graphical, hence their application in various fields.

Types of decision trees learning algorithms:

Decision tree algorithms are varied according to how splits are conceived, what types of data they handle, and how computationally efficient they are. ID3 is the basic algorithm which splits or bifurcates depending upon information gain and works well for classification, though it tends to overfit and exhibits problems with continuous attributes from the get-go. Based on ID3, C4.5 adds gain ratio for more effectively dealing with discrete and continuous data, though it can struggle in noisy environments. CART is a general-purpose algorithm applied to both classification and regression; it optimizes Gini impurity for classification and mean squared error (MSE) for regression, and includes pruning for diminishing overfitting. CHAID uses chi-square tests for split and is best suited for large categorical data, although it’s not best for continuous variables. CART is extended by Conditional Inference Trees use statistical hypothesis testing to perform unbiased splits with multiple types of data but are generally slower than standard tree algorithms because they have stringent testing mechanisms.

Decision tree learning algorithms examples:

Decision trees find their applications in real-world instances. They diagnose diseases based on the symptoms in the healthcare system. They assess loan eligibility by considering income and credit score in finance. They forecast a particular weather condition based on factors such as temperature and humidity in meteorology. They recommend products based on the analysis of user behavior in e-commerce. They are versatile due to their ability and flexibility to work with numerical as well as categorical data.

Top 10 decision tree learning algorithms:

1. ID3 (Iterative Dichotomiser 3)

ID3 is one of the earliest classes of decision tree algorithms, developed by Ross Quinlan. It uses the information gain to select the best feature on which to split the data at each instance of a node. The algorithm calculates entropy that signifies the impurity of a dataset and selects the feature that gives the largest decrease in entropy. ID3 is a very simple and elegant approach to classification problems. However, it suffers when dealing with continuous data. Also, ID3 does not work well in the presence of noise or when the training data is very small, as it tends to overfit the data.

2. C4.5

C4.5 is an extension of the ID3 algorithm and solves many of its shortcomings. Most importantly, it introduces the “gain ratio” as a splitting criterion, so that information gain is normalized and is not biased toward features with many values. It also includes support for continuous attributes, pruning, and handling missing values, ideal features to make it robust and applicable to real-life datasets. It is one of the most influential algorithms in decision tree learning.

3. CART (Classification and Regression Trees)

CART is an all-purpose medium for the classification and regression. They evaluate Gini impurity or sometimes called error, while regression uses Mean Squares Errors (MSE) to quantify the accuracy of splits. CART always grows binary trees; that is, each node can split exactly into two branches. It uses cost-complexity pruning to improve accuracy and avoid overfitting and hence, is widely used in modern ML.

4. CHAID (Chi-squared Automatic Interaction Detector)

The chi-square tests determine the best splits, so this is best for categorical data and multiway splits. Unlike CART, CHAID can create trees with more than two branches per node. It’s particularly effective in market research, survey analysis, and social science applications, where categorical variables dominate. However, it’s less effective with continuous data and may require discretization.

5. QUEST (Quick, Unbiased, Efficient Statistical Tree)

QUEST uses statistical tests to produce an unbiased and quick decision tree splitting. It can avoid the bias that some algorithms yield regarding the variable with many levels and is efficient in handling large datasets. QUEST accepts explanatory variables, either categorical or continuous, and provides pruning mechanisms. It is rarely used in preference to CART or C4.5 but is appreciated for its statistical rigor and for speed.

6. Random Forest

Random Forest is an ensemble learning method where many trees are constructed using bootstrap samples and random sampling of features, and then each tree votes for the final prediction. This leads to better accuracy and less overfitting. It works well for classification and regression problems and handles large data sets with higher dimensions. Being fast, robust, and scalable, Random Forest is often used as a benchmark in predictive modeling.

7. XGBoost (Extreme Gradient Boosting)

XGBoost works by sequentially building trees, with each one focusing on correcting the errors of the previous one by regularizing to avoid overfitting, and it is generally optimized for speed and performance. XGBoost has become a go-to algorithm in data science competitions due to its high accuracy and efficiency. It supports parallel processing and handles missing values gracefully.

8. LightGBM (Light Gradient Boosting Machine)

LightGBM stands for Light Gradient Boosting Machine and is a speed- and scale-oriented gradient boosting algorithm developed by Microsoft. Using a leaf-wise tree growth strategy, LightGBM basically results in deeper trees and better accuracy. It is helpful when working with large datasets and supports categorical features natively. It is widely used across industries for various applications like fraud detection, recommendation systems, and ranking problems.

9. Extra Trees (Extremely Randomized Trees)

The execution of Extra Trees resembles that of Random Forest, but more randomness is inducted as splitting thresholds are chosen at random and not optimized. This increases bias and reduces variance and may lead to faster training times. If your dataset is prone to overfitting, this method may be useful, and it is beneficial when dealing with high-dimensional data. In ensemble learning, Extra Trees are often employed to increase generalization.

10. HDDT (Hellinger Distance Decision Tree)

HDDT uses the Hellinger distance as a splitting criterion, making it effective for imbalanced datasets. It’s particularly useful in domains like fraud detection and rare event modeling, where traditional algorithms may falter.

The post Top 10 Decision Tree Learning Algorithms appeared first on ELE Times.

Infineon Technologies AG of Munich, Germany is introducing a 12kW reference design for high-performance power supply units (PSUs), specifically designed for AI data centers and server applications, and targeted at R&D engineers, hardware designers, and developers of power electronics systems. Leveraging all relevant semiconductor materials silicon (Si), silicon carbide (SiC) and gallium nitride (GaN), the reference design is said to offer high efficiency and high power density...