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To strengthen its global competitiveness, deposition equipment maker Aixtron SE of Herzogenrath, near Aachen, Germany is to build a new manufacturing facility in Malaysia in order to tap into the fast growing semiconductor equipment ecosystem in South East Asia...

STMicroelectronics and Leopard Imaging have introduced an all-in-one multimodal vision module for humanoid and other advanced robotics systems. Combining ST imaging, 3D scene-mapping, and motion sensing with the NVIDIA Holoscan Sensor Bridge technology, the module integrates natively with NVIDIA Jetson and NVIDIA Isaac open robot development platform, simplifying and accelerating vision system design within the size, weight, and power constraints of humanoid robots.

“Humanoid robotics is moving beyond research projects and demonstrations to deliver powerful new machines for a wide range of roles in manufacturing and automotive factories, logistics and warehousing, as well as retail and customer service,” said Marco Angelici, Vice-President of Marketing and Application for Analogue Power MEMS and Sensors, at STMicroelectronics. “Our collaboration with Leopard Imaging brings market-leading ST sensors and actuators, seamlessly integrated into the NVIDIA robotics ecosystem, to accelerate the deployment of physical AI applications with human-like awareness.”

“Accessing ST sensors and actuators directly within the ecosystem has allowed us to standardise and streamline data acquisition and logging for humanoid robot vision across the HSB interface,” said Bill Pu, CEO of Leopard Imaging. “Robot builders can use our multi-sensing vision module with Isaac tools to accelerate learning and quickly bridge the ‘sim-to-real’ gap.”

Powered by the NVIDIA Holoscan Sensor Bridge, the new module integrates seamlessly with NVIDIA Jetson over Ethernet for real-time sensor data ingestion and NVIDIA Isaac open robot development platform, which offers open AI models, simulation frameworks and libraries for developers. The new module includes a build system and application programming interfaces (APIs), artificial intelligence (AI) algorithms curated for mobile robots, sample applications, domain randomisation, and a simulation environment containing sensor models.

ST continues to integrate its sensors, drivers, actuators, controllers, and development tools into the NVIDIA robotics ecosystem as a key NVIDIA robotics and edge AI partner, including high-fidelity models and proof-of-concept modules.

Technical information

The Leopard Imaging Systems vision module incorporates:

For vision-based sensing, the ST VB1940 automotive-grade RGB-IR 5.1-megapixel image sensor has combined rolling shutter and global shutter modes. ST has also released a mass market and industrial version V**943, part of the ST BrightSense product family, existing in monochrome or RGB-IR, in die or packaged sensor.

For motion sensing, the LSM6DSV16X 6-axis inertial measurement unit (IMU) embeds ST machine-learning core (MLC) for AI in the edge, sensor-fusion low-power (SFLP), and Qvar electrostatic sensing for user-interface detection.

For 3D depth sensing, the VL53L9CX dToF all-in-one LiDAR module, part of the ST FlightSense product family, provides 3D depth sensing with accurate ranging up to 9 meters. With its resolution of 54 x 42 zones (nearly 2,300 zones) combined with a wide 55°x42° FoV providing 1° angular resolution, short and long-distance measurements and small objects detection are achievable at up to 100 fps.

The post STMicroelectronics and Leopard Imaging use NVIDIA Jetson-ready multi-sensor module for robotics vision  appeared first on ELE Times.

At Auto EV TVS Summit, 2025, a panel of industry leaders—from semiconductor companies to automotive software firms—gathered to discuss one of the most transformative shifts underway in mobility: the rise of the Software-Defined Vehicles (SDVs). Moderated by Mohammed Saeed Mombasawala, CTO at Keysight Technologies, the discussion brought together voices from Bosch Global Software Technologies, Marelli India, NXP Semiconductors, Aumovio, and Auto Ascent to unpack how vehicles are evolving from mechanical machines into continuously upgradeable software platforms.

The consensus across the panel was clear: software is no longer an auxiliary component in the automotive stack—it is becoming the primary architecture around which the vehicle is built.

The Shift Toward “Intelligence on Wheels”

Software-defined vehicles represent a departure from the traditional automotive model, where functionality was fixed at the time of manufacturing. Instead, SDVs rely on software layers that can evolve through updates, new services, and data-driven improvements throughout a vehicle’s lifecycle. “SDV is essentially about delivering affordable intelligence on wheels,” explained Bosch’s Naved Narayan during the panel discussion.

The concept is already beginning to take shape in India. Features such as over-the-air updates, connected vehicle services, and Level-2 driver assistance systems are gradually entering the market. However, unlike Western markets where premium vehicles dominate adoption, India’s automotive ecosystem operates under a different constraint: cost sensitivity.

Industry participants emphasized that the success of SDVs in India will depend on achieving a delicate balance between technological sophistication and affordability.

India’s SDV Journey: Progress, But With Constraints

While the SDV revolution is global, India’s pathway has unique challenges. Panelists noted that the country is progressing toward connected and intelligent mobility, but several structural barriers remain.

Latha Chembrakalam, Founder and CEO of Auto Ascent, highlighted that the industry is driven by three key factors: safety, ease of use, and joy of driving. Software-defined architectures promise to enhance all three—but India must simultaneously contend with infrastructure gaps and regulatory limitations.

“India is a cost-sensitive market, and infrastructure readiness also plays a major role,” she noted. “While progress is visible, technological capabilities and regulatory frameworks still need to evolve to fully support SDV adoption.” Yet there are encouraging signs. Automakers such as Mahindra and MG have already begun introducing advanced connected features and driver assistance technologies in the Indian market, creating early momentum.

The New Architecture of the Vehicle

At the engineering level, SDVs are forcing a fundamental redesign of vehicle electronics. Traditional vehicles rely on dozens of electronic control units (ECUs), each responsible for a specific function. SDVs, however, are shifting toward centralized computing architectures, where domain controllers or zonal controllers manage multiple vehicle functions.

This transformation also introduces new challenges. Automotive companies must reconcile modern SDV architectures with legacy platforms and existing software stacks. “For new electric vehicle platforms, it is easier to design from scratch,” Chembrakalam explained. “But the real complexity lies in integrating SDV capabilities into existing vehicle platforms with legacy systems.”

In addition, the industry still lacks fully standardized architectures across OEMs, Tier-1 suppliers, and technology vendors. Without stronger coordination, experts warned, the ecosystem risks becoming fragmented.

Sensors, Connectivity, and the Edge Computing Challenge

Software-defined vehicles depend heavily on three technological pillars:

• Sensors
• Connectivity
• In-vehicle compute

Radar, cameras, LiDAR, and other sensing technologies collectively form the vehicle’s perception system. Rather than relying on a single sensor type, most companies now favor sensor fusion architectures that combine multiple sensing modalities. Rajkumar Anantharaman of NXP noted that radar remains one of the most critical sensing technologies due to its reliability across weather conditions. However, cameras provide complementary information, such as object classification and visual context.

“Radar alone cannot solve everything,” he said. “The industry is moving toward fusion architectures where radar and camera data are combined to improve environmental perception.” At the same time, sensing systems are also becoming increasingly intelligent. Modern radar chips now integrate edge processing capabilities, allowing them to detect and classify objects directly on the sensor rather than transmitting raw data to a central processor.

This shift toward edge AI processing helps reduce latency and bandwidth requirements.

Connectivity: Why Latency Matters

Connectivity plays another crucial role in the SDV ecosystem. While current vehicle platforms rely primarily on 4G networks, panelists believe 5G—and eventually 6G—will enable new levels of vehicle intelligence, particularly for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) communication.

The reason is latency. Even a delay of a few hundred milliseconds can significantly affect vehicle safety systems. In collision scenarios, a 200-millisecond delay could translate into several meters of additional braking distance. Future networks promise to reduce latency to just a few milliseconds, enabling faster information exchange between vehicles and surrounding infrastructure.

However, experts cautioned that India’s current telecommunications infrastructure still needs to mature before such capabilities can be widely deployed.

AI, Data, and the Digital Twin Revolution

Perhaps the most complex dimension of SDVs lies in software development itself. Unlike traditional automotive software, SDV platforms require continuous integration, machine learning models, and large-scale data pipelines.

Training autonomous and driver-assistance systems requires massive datasets capturing real-world driving conditions. Yet India presents a unique challenge in this area: driving environments that are far less structured than those in Western markets. Unpredictable traffic behavior, unmarked roads, and unusual scenarios—from animals crossing highways to dense urban congestion—make it difficult to collect sufficient real-world training data.

To overcome this limitation, companies are increasingly relying on digital twins, simulation environments, and synthetic data generation. These virtual environments allow engineers to simulate thousands of driving scenarios before deploying software in actual vehicles. By shifting validation earlier in the development process—known as “shift-left engineering”—companies can test and refine software models without relying entirely on expensive physical vehicle testing.

Vehicles That Improve Over Time

One of the most intriguing aspects of SDVs is the possibility that vehicles may increase in value over time. Traditionally, a car’s capabilities remained fixed after leaving the factory. With SDVs, however, software updates can introduce entirely new features years after purchase.

Bosch’s Narayan described this shift as “an upgrade without actually upgrading the vehicle.” Through over-the-air updates, automakers can introduce new driver assistance features, improved algorithms, or additional digital services long after the vehicle has been sold.

At the same time, consumer expectations are rapidly evolving. According to Yogesh Davangere Adevappa, rising awareness of global technology trends—driven by social media and digital exposure—is pushing buyers to expect more intelligent and feature-rich vehicles. “People are increasingly aware of the technologies available worldwide,” he noted during the panel discussion. “That awareness is driving demand for vehicles with more connected features, safety systems, and intelligent capabilities.”

Why the Software-Defined Vehicle Matters

Ultimately, the SDV transformation is about more than just technology. It represents a fundamental redefinition of what a vehicle is. For consumers, the appeal lies in enhanced safety, convenience, and personalized driving experiences. For automakers, SDVs open the door to entirely new business models built around software services and continuous updates.

There is also growing demand for customization in vehicles. Vikram Bhatt, Aumovio, pointed out that SDV architectures allow drivers to configure vehicle behavior dynamically—whether enabling features like park-assist modes, obstacle detection systems, or personalized driving configurations. These runtime adjustments represent a fundamental shift from static vehicle functions to software-enabled experiences.

Yet despite this progress, panelists acknowledged that India is still at an early stage of SDV deployment compared to global markets.

As one panelist summarized during the discussion, the future of mobility may not just be electric or autonomous—it will be software-driven at its core.

The post Decoding SDV Revolution: Sensors, AI, and the Future of Automotive Architecture appeared first on ELE Times.

As miniaturisation and increasingly complex design architectures continue to define modern technology, nanotechnology is emerging as a frontier discipline shaping the trajectory of innovation—from medical and electronic devices to energy infrastructure and beyond. Simply stated, as the focus of electronics development shifts towards the engineering and application of materials at the atomic and molecular scale—typically between 1 and 100 nanometres—certain physical, chemical, and electrical limitations begin to surface. When conventional materials such as silicon and copper are miniaturised to the nanoscale, they often encounter issues such as increased resistance, heat generation, and reduced performance. To address these limitations, Graphene, an sp²-hybridized two-dimensional honeycomb lattice, has emerged as one of the most promising materials for next-generation electronic systems.

Amid rapid advances across the nanotechnology landscape, graphene is increasingly regarded as a flagship material in nanoscale engineering, attracting significant attention, particularly in electronics.

While Graphene continues to attract significant research interest due to its exceptional properties, the transition from laboratory-scale breakthroughs to commercially viable semiconductor technologies remains a complex challenge. Industry players such as Weebit Nano emphasise that beyond material performance, factors such as manufacturability, process compatibility, and scalability are equally critical. This creates a dynamic balance in nanoelectronics—between exploring high-potential emerging materials and developing solutions that can be seamlessly integrated into existing semiconductor fabrication ecosystems.

Owing to its exceptional electrical conductivity and extremely high electron mobility, graphene is being explored for a wide range of electronic components, including high-speed transistors, flexible circuits, and highly sensitive biosensors. The material is both electrically and thermally efficient, enabling electronic devices to operate with improved performance while generating less heat. These properties have positioned graphene as a promising complement—and in some cases a potential alternative—to conventional materials such as silicon in applications including touchscreens, sensors, and next-generation electronic interfaces.

When nanotechnology converges with electronics, the field is commonly referred to as Nanoelectronics. Nanoelectronic systems require extremely high switching speeds and efficient charge transport while minimizing heat buildup in densely packed circuits. In this context, Graphene offers exceptional carrier mobility—reaching approximately 100,000 cm²/V·s under ideal conditions—making it an attractive material for high-frequency electronic applications. Additionally, as electronic components become increasingly dense in nanoelectronic architectures, thermal management becomes a critical challenge. Graphene’s remarkably high thermal conductivity enables efficient heat dissipation, thereby helping maintain the reliability and performance of nanoscale electronic systems.

Let’s look into some applications of Graphene in nanoelectronics: 

Graphene Field-Effect Transistors (GFETs)

Graphene Field Effect Transistors are advanced, ultra-sensitive electronic components comprising a channel made of a single-atom-thick layer of graphene, enabling modulation of current by an electric field.   

Structure: A GFET typically consists of three things: Source, drain & a gate (top or back).

• Channel: The space between the Source & the Drain makes up a channel where a 2D Sheet of Graphene is placed. 
• Gate Control: The gate voltage modifies the electric field, changing the charge carrier density in the graphene channel.

How does it work? 

It operates by controlling the flow of electrical current through the Graphene channel. When a voltage is applied between the source and drain, charge carriers in the graphene layer begin to move, creating a current. The gate electrode, separated from the graphene by an insulating dielectric layer, is used to control this current. By applying a positive or negative voltage to the gate, an electric field is generated that changes the concentration of electrons or holes in the graphene channel. 

A positive gate voltage increases electron concentration, while a negative gate voltage increases hole concentration, thereby modulating the conductivity of the channel and controlling the amount of current that flows between the source and drain. Because graphene has extremely high carrier mobility, electrons can move through the channel very quickly, allowing GFETs to operate at very high frequencies, which makes them particularly promising for radio-frequency and high-speed electronic applications.

Applications 

GFETs are used in various fields due to their high performance: 

1. Biosensors & Chemical Sensors: For detecting DNA, proteins, and gases at low concentrations.
2. Flexible Electronics: For wearable sensors and devices.
3. Radio Frequency (RF) Electronics: Due to high-speed charge transport. 

Nano-Electro- Mechanical Systems (NEMS)

Nano-Electro -Mechanical Systems are highly miniaturized devices that integrate electrical and mechanical functionality at the nanoscale, enabling the development of devices that are smaller, more sensitive, and more efficient as compared to the traditional silicon-based ones. 

Structure: The structure of a Graphene-based Nanoelectromechanical System (graphene NEMS) generally consists of a few key components integrated on a microfabricated substrate. It Includes: 

• Silicon Base: At the base is a Silicon or silicon-oxide substrate in which a small cavity or trench is created. 

• Electrodes: Metal source and drain electrodes are patterned on the surface to provide electrical contacts. A thin insulating layer may also be included to isolate different parts of the device. 

• Graphene Sheet: The central element is a suspended sheet of Graphene, which spans the cavity and connects the electrodes, forming a bridge-like membrane. 

• Gate Electrode: In some designs, a gate electrode is positioned beneath the graphene, separated by a dielectric layer.

How does it work? 

A Nanoelectromechanical System (NEMS) functions by converting mechanical motion at the nanoscale into electrical signals, or vice versa. These devices integrate mechanical structures—such as beams, membranes, or resonators—with electronic components on a very small scale.

When voltage is applied between electrodes (such as source, drain, or gate), electrostatic forces drive the mechanical motion of the nanoscale structure, and with this, the mechanical component begins to deflect, vibrate, or resonate. This mechanical movement changes certain electrical properties of the system—such as resistance, capacitance, or current flow—which can then be detected and measured by the electronic circuitry. As a result, NEMS devices operate as ultra-sensitive sensors, resonators, or switches, capable of detecting extremely small physical changes at the nanoscale.

Applications: 

NEMS are used in various applications, including: 

• Ultra-Sensitive Sensors: NEMS devices, such as AFM tips, detect forces, vibrations, and chemical signals at the atomic level. They are used as highly sensitive accelerometers for inertial navigation and motion detection.

• Bio-nanotechnology & Medical: NEMS enables lab-on-a-chip devices for diagnostics, biomolecule detection, and precise, targeted drug delivery systems. 
• Nano-switches and Relays: NEMS switches serve as mechanical, low-power alternatives to traditional semiconductor logic switches, offering near-zero leakage current.

Conclusion

As the electronics industry continues to push the boundaries of miniaturisation and performance, materials engineered at the nanoscale will play an increasingly central role in shaping the next phase of technological evolution. Among these, Graphene stands out for its exceptional electrical, thermal, and mechanical properties, offering solutions to several limitations faced by conventional semiconductor materials.

However, the path from material innovation to large-scale deployment remains complex. While graphene continues to demonstrate immense potential in nanoelectronic applications—from high-frequency transistors to ultra-sensitive nanoscale systems—its integration into mainstream semiconductor manufacturing is still an evolving challenge. In contrast, industry players such as Weebit Nano are focusing on developing technologies that align closely with existing fabrication ecosystems, underscoring the importance of manufacturability alongside performance.

As nanotechnology matures, the future of electronics will likely be shaped by a careful balance between breakthrough materials and practical implementation—where innovation is not only defined by what is possible at the nanoscale, but also by what can be reliably produced at scale.

The post Graphene in Focus: How Nanotechnology is Transforming Electronics? appeared first on ELE Times.

The 6G transition is no longer a distant theoretical exercise; it’s a commercial inevitability driven by fundamental requirements for cellular standards to keep moving forward. 5G penetration has already surpassed 75% and is on a trajectory to reach 95% within a few years. We are witnessing an appreciation for continued call quality and data throughput improvements despite an explosion in mobile traffic.

However, the wireless ecosystem projects that even this capacity will soon overload due to accelerating AI content, the integration of satellite communications (SATCOM) into the cellular fold, and the rise of physical AI. 6G is the industry’s response to keep pace with that exponential growth in data communication demand.

The 2030 countdown: Why 2026 is the crucial starting line

To understand the urgency, one must look at the decadal cycle of cellular evolution. History shows it takes about five years to finalize a standard and fold its requirements into a functional ecosystem. While 6G is anticipated to take off commercially by 2030, the work-back schedule reveals a tight timeline for product builders. By 2029, hardware must be ready for compliance testing, meaning component technologies must be finalized by 2028.

Consequently, underlying embedded systems must be built in 2027, necessitating that architectural definitions start as early as 2026. As an example of what is going on in the industry, Qualcomm’s CEO recently hinted at the Snapdragon Summit that 6G-capable devices could appear as early as 2028 for trials, making the 2028 Olympics a perfect arena for tech demos.

Unlocking the “Golden Band”: FR3 and the business of spectrum

Beyond architectural shifts, 6G introduces the Frequency Range 3 (FR3) spectrum, spanning 7.125 GHz to 24.25 GHz. Often called the “Golden Band for 6G,” FR3 offers the perfect balance between the wide coverage of lower bands and the massive capacity of mmWave.

This spectrum is expected to be a major business driver, enabling the 10x higher data rates targets (up to 200 Gbps) and supporting “massive MIMO evolution” to handle the projected 4x traffic growth by 2030 (going over 5.4 zettabytes as indicated by the GSMA Intelligence report).

Sustainable networks

Sustainability is a core pillar of 6G, with network operators seeking to reduce OpEx, as 25% of it is driven by power demand. 6G moves from an “always-on” to a “smart-on” philosophy, aiming for 30-50% increase in power efficiency. Key techniques include:

• Enhanced deep sleep modes: Enabling base stations to achieve near-zero power consumption when no active users are present, and reduction in periodic signaling (current 5G standard mandates high periodic signaling that in practice keeps a lot of the RF and power amplifier components active at all times).
• AI-driven beamforming: Using AI to direct signals precisely to users, reducing energy waste from broad, inefficient broadcasting.
• AI-driven resource management: Using AI at the higher protocol layers for effective radio resources management.

The AI-native revolution: Moving intelligence to the air interface

One of the most significant shifts in 6G is the move toward an AI-native air interface. Unlike 5G’s rigid mathematical models, 6G uses deep learning to dynamically adapt signal processing blocks. This enables “adaptive waveforms” that adjust modulation in real-time to environmental conditions.

It also facilitates integrated sensing and communication (ISAC), where RF reflections provide precise spatial awareness, allowing the network to proactively adjust beamforming based on user movement.

The coordination challenge: Managing two-sided AI

This transition introduces a complex challenge in how the transmitter (base station) and receiver (device) coordinate their intelligence. Unlike traditional algorithms, AI components must be synchronized through AI lifecycle management (LCM). The industry is weighing one-sided models (device-only optimization) against two-sided architectures (essential for tasks like CSI compression).

In two-sided designs, the device acts as a neural encoder and the base station as a decoder; these must be coordinated pairs to some extent. The level of coordination is still in study, as there are few optional schemes. Examples for those schemes are fully matched neural networks couples, or alternatively, independent at the NN architecture level but trained on the same dataset.

This raises critical questions on the protocol level: should the network use model ID-based selection (activating pre-loaded models) or model transfer (pushing new neural weights over the air) or weights transfer?

Programmable intelligence: Why DSPs are the preferred path

Because 3GPP specifications remain fluid, the need for flexibility through programmability has never been higher. Developing 6G on hard-wired logic is risky, as spec changes could render silicon obsolete. This is why digital signal processors (DSPs) are the preferred architecture. Modern DSPs are uniquely suited for the AI-native physical layer; they possess the massive number of MACs required for matrix operations and are highly efficient at the vector processing necessary for neural networks.

Leading technology vendors also offer dedicated AI ISA for accelerated NN activation functions. A fully programmable modem powered by AI-native DSP offers a “safe bet,” allowing developers to adapt as 6G settles while maintaining the performance needed to lead the market.

Elad Baram is director of product marketing for the Mobile Broadband Business Unit at Ceva.

Related Content

• Get ready for 6G
• 5G & 6G: Adoption, technologies and use cases
• 5G-Advanced to 6G: What’s next for wireless networks
• Making waves: Engineering a spectrum revolution for 6G
• The aspects of 6G that will matter to wireless design engineers

The post The 6G clock ticking: Why silicon architecture for 2030 must start in 2026 appeared first on EDN.

FormFactor and Rohde & Schwarz have announced a strategic co-marketing partnership as part of FormFactor’s MeasureOne partner program, a solution-integration initiative designed to deliver validated, turnkey on-wafer test systems. The collaboration combines advanced probing technology from FormFactor with industry-leading RF test instrumentation from Rohde & Schwarz, providing manufacturers with comprehensive solutions spanning early design verification through production. With RF device complexity and operating frequencies continuing to increase, this expanded collaboration formalises a tightly integrated on‑wafer test solution designed to lower integration effort and risk, reduce overall cost, and accelerate time-to-market for customers across development and production.

Reduced costs and faster time-to-market

On-wafer device characterisation of RF components such as 5G frontends or filters enables design validation during development, as well as product qualification and verification in production. Identifying faulty devices before packaging can significantly help reduce costs and improve yield. Through their integrated solutions, Rohde & Schwarz and FormFactor help manufacturers detect issues early in the process, which can result in faster time-to-market.

Seamlessly integrated test solutions

Rohde & Schwarz and FormFactor have been working together for several years to deliver powerful, seamlessly integrated solutions. Rohde & Schwarz provides instruments like the R&S ZNA, a versatile high-end VNA capable of measuring all key RF parameters, which can easily be combined with frequency converters extending frequencies up to the THz range. FormFactor complements this with a comprehensive portfolio of manual, semi-automated, and fully automated probe systems, including advanced thermal control, high-frequency probes, precision probe positioners, and robust calibration tools.

This combined approach allows manufacturers to validate product performance directly during wafer runs, leveraging the expertise of both companies. The tight integration of hardware and software components from both companies is designed to enable fast and reliable testing. The complete solution includes advanced instruments, reliable wafer and die fixuring, and high-precision probe positioning throughout the entire test cycle, strengthening confidence in product quality and performance.

Jens Klattenhoff, SVP and GM of the Systems Business Unit at FormFactor, said: “By expanding our collaboration with Rohde & Schwarz through the MeasureOne program, we are delivering integrated on‑wafer RF test solutions designed to help customers reduce risk, improve efficiency, and accelerate development. This partnership brings together advanced wafer probing and proven RF measurement technologies to address the growing complexity of next‑generation semiconductor devices.”

Michael Fischlein, Vice President, Spectrum & Network Analysers, EMC and Antenna Test at Rohde & Schwarz, stated: “We are delighted to be part of MeasureOne, a strategic Co-Marketing partner program that unites FormFactor – one of the world’s leading probe station providers – with Rohde & Schwarz, a global leader in test and measurement. Together, we are set up to deliver turnkey on-wafer solutions enabling crucial and demanding test capabilities for next-generation semiconductors.”

The MeasureOne partnership encompasses a wide range of Rohde & Schwarz instruments, including the R&S ZNA, R&S ZNB, R&S ZNBT, R&S ZVA and R&S ZNL VNA families, alongside signal and spectrum analysers such as the FSW, FSWX, R&S FSV3000 and R&S FSVA3000. Integration also extends to signal generators (R&S SMA100B, R&S SMB100B, R&S SGS100A, R&S SGU100A) and selected frontends and converters for advanced calibration workflows, all working seamlessly with FormFactor’s traditional plus speciality probe stations for cryogenic and vacuum applications.

The post FormFactor and Rohde & Schwarz Advance their Partnership for on-wafer RF Component Characterisation appeared first on ELE Times.

ANRITSU CORPORATION has jointly verified AI-based antenna optimisation technologies with SK Telecom, South Korea’s leading mobile network operator, Pohang University of Science and Technology (POSTECH), and Bluetest of Sweden.

In this verification, MIMO measurement data were acquired in a real user environment using Anritsu’s Radio Communication Test Station MT8000A and Universal Wireless Test Set MT8870A. Based on AI-based analysis and optimisation technologies, the effectiveness of antenna performance optimisation was confirmed.

Details of this joint verification were also presented at MWC Barcelona 2026 (MWC 2026), one of the world’s largest mobile communications exhibitions.

This verification analysed antenna performance based on throughput and ECC (Error Correlation Coefficient) data collected from real user environments. It reflected practical usage conditions, including free-space scenarios, handheld operation, and head-proximate usage scenarios. By incorporating a variety of user grip conditions, it quantitatively evaluated performance variations under time-varying RF conditions.

Through AI-based analysis, RF performance differences according to antenna tuner states were modelled, and the optimal antenna switching configuration was automatically identified. This enabled dynamic optimisation of antenna performance while maintaining communication quality in real user environments.

Based on measurement-driven evaluation results, significant throughput improvements were confirmed across various user scenarios in an 8Rx (eight-receive-antenna) configuration, while in a 4Tx (four-transmit-antenna) configuration, throughput improvement of up to more than two times was observed.

Verification Overview

This verification presented an AI-based antenna optimisation workflow built on real measurement data collected in an Over-the-Air (OTA) test environment.

The verification covered the following processes:

• Analysis of RF performance variations according to user scenarios and tuner state changes
• Comparison of power and performance characteristics for each antenna path
• Derivation of optimal switching states based on throughput and ECC data
• Verification of performance improvement through AI analysis based on measurement data

This approach goes beyond conventional static antenna design-centric evaluation, enabling data-driven optimisation verification that reflects real environmental conditions.

MT8000A (5G NR Test Platform)

MT8000A is an integrated RF and protocol-based test platform for 5G NR device validation. In this verification, it was used for MIMO performance analysis and throughput evaluation in an OTA environment.

Key features include:

• 4×4 / 8×8 MIMO signal generation and OTA data acquisition
• Multi-port synchronised measurement based on digital IQ capture
• Throughput evaluation under controlled 5G NR link conditions
• Provision of a repeatable and stable RF test environment

MT8000A provided high-precision signal generation and analysis capabilities for measurement-based performance verification, supporting the acquisition of highly reliable data required for AI modelling.

MT8870A (RF Measurement Platform)

MT8870A is a general-purpose wireless measurement platform supporting non-signalling RF measurements. In this verification, it was used for RF characteristic analysis by antenna path and comparative measurement of switching states.

Key features include:

• RF power measurement by antenna path under various tuner states
• TX/RX path control and RF characteristic measurement
• Collection of measurement data across antenna paths and switching states
• Support for multi-port RF measurement configurations

The path-specific RF characteristic data obtained through MT8870A is used as core input data for AI-based optimisation analysis.

The post Anritsu and SK Telecom Jointly Verify AI-Based Antenna Optimisation, POSTECH and Bluetest appeared first on ELE Times.

submitted by /u/1Davide
[link] [comments]

Measuring an 8 picofarad capacitor. submitted by /u/AwesomeAvocado [link] [comments]

Polar Light Technologies AB — which stems from research by founder professor Per-Olof Holtz and his team at Linköping University (with support from Sweden’s innovation agency Vinnova) — has been awarded a European Union (EU) Eurostars grant.With a total budget of €1.1m, the 18-month project ‘2ndGenMicroLED’ aims to deliver the first dual-color micro-LED micro-display prototype built on a bottom-up pyramidal LED architecture. Finetech GmbH & Co KG of Berlin, Germany (a supplier of sub-micron and high-accuracy die bonding solutions) joins the consortium, contributing its expertise in high-accuracy die attach and advanced packaging...

Taiwan Semiconductor Co Ltd (TSC) of New Taipei City, Taiwan — which supplies discrete power electronics devices, LED drivers, analog ICs, transient voltage suppressor (TVS), and electrostatic discharge (ESD) protection — has added to its growing series of automotive-grade 1200V-rated silicon carbide (SiC) Schottky diodes with 1A and 2A models. The TSCDFS01120G2H and TSCDFS02120G2H diodes, respectively, are claimed to offer exceptional performance in the industry’s only SOD-128 packages...

Guerrilla RF Inc (GRF) of Greensboro, NC, USA — which develops and manufactures radio-frequency integrated circuits (RFICs) and monolithic microwave integrated circuits (MMICs) for wireless applications — has released the GRF2118, an ultra-low-noise X-band amplifier delivering what is claimed to be best-in-class noise figure performance across the 6.0–8.5GHz band. Targeting the most demanding receive applications in satellite communications, defense electronics and space-borne platforms, the GRF2118 establishes a new performance benchmark for COTS X-band LNA solutions, it is claimed...

Well, I made a post awhile back about 3D printing a solder paist mask. I was finally able to mod/tune my 3d printer enough to get something usable. I outfitted my Ender3 V2 printer with a 0.2mm nozzle and gave my layer height a setting of 0.1mm. Please note that this was never for production boards as I am only doing 2 or 3 prototype boards with it. There is one or two glitchy areas with it which i attribute to not having dry filliment. Doing this will definitly save a lot of time manually putting paist on the boards. submitted by /u/Aggravating-Mistake1 [link] [comments]

🎓 STEM-стипендія для студентів НН ІПСА від фінтех-компанії Solidgate kpi ср, 03/25/2026 - 17:12

🉐Відкрито набір на довгостроковий курс японської для початківців! kpi ср, 03/25/2026 - 14:09

For decades, I’ve had a fascination with voltage-to-frequency converters and the 555 analog timer chip, and therefore a double obsession with VFCs based on the 555. In fact, my first Design Ideas (DI) submission (in 1974) was for a 555 VFC. It was not only published but also selected as the best DI of the year. That was it, I was thenceforth hooked forever.

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

The simple 555 VFC design to be presented here, so far as I know and as unlikely as it sounds for so *ahem* “mature” a part as the 555, is novel. It runs with good linearity and accuracy at 1 MHz, with even faster operation possible. That’s 100x faster than that 1974 555 frequency converter.

I hope you’ll find its details interesting.  Here’s how it works. The story begins with Figure 1.

Figure 1 Starred components are precision, including the +5 V supply, but something’s missing.

There’s nothing novel about the input current source comprising A1, Q1, and surrounding parts. It supplies 0 to 1 mA to the U1 current-to-frequency converter in response to its input voltage, as scaled and offset by R1 and R2.  The values shown set a 0 to +5 V input span.  R1 = 1.8M and R2 = 200k would make it -5 V to +5 V. 

A capacitor added in parallel with R2 will provide extra noise rejection. But the inherent noise immunity of the VFC analog-to-digital conversion is good, so you probably won’t need it.

Moving further into the circuit is when things do start to get weird, because the usual two resistors associated with 555 oscillators are missing. Also missing is the usual astable 555 1/3V+ peak-to-peak voltage swing.  This topology generates a 2/3V+ Vpp linear sawtooth waveform that resets, not to V+/3, but to zero. Unfortunately, while the sawtooth is nicely linear, due to U1’s internal switching delays Td, the frequency versus Q1 current Iq1 relationship is not very linear. Figure 2 shows how bad it is:

Frequency of oscillation (FOO) = 1.0/((VthC2/Ir3 + Td) = 1.0/(1.0ns/Ir3 + Td)

Figure 2 Nonlinear red curve versus ideal black shows ~20% linearity error from LMC555 internal delays.

Luckily, as derived in another recent DI: “Improve 555 frequency linearity.“

…it’s an easy fix. It consists of a single resistor, R4, connected between the Dch (discharge) and Thr (threshold) pins. R4 is used to linearize the current-versus-frequency function by biasing the Thr pin upward by IcR4. That cuts short the duration of the positive-going timing ramp and thereby the sawtooth period by the same amount that the delays lengthen it: IcR4/(Ic/C2) = R4C2 = Td.

Thus, if R4 is chosen so R4C2 = Td as shown in Figure 3, nonlinearity compensation will be (at least theoretically) complete over the full range of control current. The frequency of oscillator (FOO) for this circuit: 

FOO = 1/((VthC2)/Iq1 + 212ns – Td) = 1/(1.0ncb/Iq1 + 212ns – 212ns) = 1/(1.0ncb /Iq1) = 1000 Iq1 MHz = 1MHz(+5v – Vin)/+5v

Figure 3 R4C2 = Td = 212ns = nonlinearity compensation for 555 internal delays.

Now FOO will linearly track Iq1 and therefore Vin as shown in Figure 4.

Figure 4 Nonlinearity disappears if R4 = Td/C2 =212 ns/300 pF = 706 ohms.

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 the best Design Idea of the year in 1974 and 2001.

Related Content

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• Inverted MOSFET helps 555 oscillator ignore power supply and temp variations
• Gated 555 astable hits the ground running

 

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By 2030, performing inference on a large language model (LLM) with one trillion parameters will cost GenAI providers over 90% less than it did in 2025, according to Gartner, Inc., a business and technology insights company.

AI tokens are the units of data that GenAI models process. For the purposes of this analysis, a token is 3.5 bytes of data, or approximately 4 characters.

“These cost improvements will be driven by a combination of semiconductor and infrastructure efficiency improvements, model design innovations, higher chip utilisation, increased use of inference-specialised silicon, and application of edge devices for specific use cases,” said Will Sommer, Sr. Director Analyst at Gartner.

As a result of these trends, Gartner forecasts that LLMs in 2030 will be up to 100 times more cost-efficient than the earliest models of similar size developed in 2022.

The forecasted model results are split between two sets of semiconductor scenarios:

• Frontier scenarios: Model processing is based on a representation of cutting-edge chips.
• Legacy blend scenarios: Model processing is based on a representative blend of available semiconductors benchmarked to Gartner forecasts.

Modelled costs in the “blend” forecast scenarios are considerably higher than in the “frontier” scenarios, given lower computational power (see Figure 1).

Figure 1: Gartner GenAI Inference Cost Scenario Forecasts

Source: Gartner (March 2026)

Falling Token Costs will not Democratize Frontier Intelligence

However, falling GenAI provider token costs will not be fully passed on to enterprise customers. Moreover, frontier intelligence will demand significantly more tokens than current mainstream applications. Agentic models, for example, require between 5-30 times more tokens per task than a standard GenAI chatbot, and can perform many more tasks than a human using GenAI.

While lower token unit costs will enable more advanced GenAI capabilities, these advancements will drive disproportionately higher token demand. As token consumption rises faster than token costs fall, overall inference costs are expected to increase.

“Chief Product Officers (CPOs) should not confuse the deflation of commodity tokens with the democratisation of frontier reasoning,” said Sommer. “As commoditised intelligence trends toward near-zero cost, the compute and systems needed to support advanced reasoning remain scarce. CPOs who mask architectural inefficiencies with cheap tokens today will find agentic scale elusive tomorrow.”

Value will accrue to platforms that can orchestrate workloads across a diverse portfolio of models. Routine, high-frequency tasks must be routed to more efficient small and domain-specific language models, which perform better than generic solutions at a fraction of the cost when aligned to specialised workflows. Expensive inference of frontier-level models must be heavily gated and reserved exclusively for high-margin, complex reasoning tasks.

The post Gartner Predicts That by 2030, Performing Inference on an LLM With 1 Trillion Parameters Will Cost GenAI Providers Over 90% Less Than in 2025 appeared first on ELE Times.

At the Bharat Electricity Summit (BES 2026) in Yashobhoomi, New Delhi (19–21 March), Infineon Technologies AG of Munich, Germany announced a strategic technology partnership with Zenergize, an emerging Indian company designing and manufacturing AI-integrated power electronics hardware for the solar, EV charging, and renewable energy sectors. Infineon has had a presence in India for nearly 30 years, and currently has over 2500 staff across four sites...

Київський політехнік Ігор Ментюков на Всесвітніх іграх майстрів Інформація КП ср, 03/25/2026 - 12:15