ELE Times

India’s electronics manufacturing landscape is set for a major transformation under the newly launched Electronics Component Manufacturing Scheme (ECMS). The scheme, aimed at increasing domestic production of non-semiconductor components, has seen an industry-wide runaway response with proposals for investment totaling ₹1.15 lakh crore, well over twice the scheme’s initial aim of ₹59,000 crore.

Based on industry estimates, the rise in participation under ECMS can assist in doubling domestic value addition in the manufacture of finished electronic products from the present 15–20% to 35–40% in the next five years. This is a significant improvement towards diminishing dependence on imports and consolidating India as an international manufacturing powerhouse.

The program has received proposals from 249 firms, including major component segments like flexible printed circuit boards, electro-mechanical components, multi-layer PCBs, sub-assemblies, display modules, camera modules, and lithium-ion cells. These proposals are to be soon assessed by a committee for approval.

Amongst the largest investment proposals, enclosures for mobile phones, IT hardware, and other associated devices represent ₹35,813 crore. Other prominent segments comprise flexible PCBs (₹16,542 crore), electro-mechanical components (₹14,362 crore), multi-layer PCBs (₹14,150 crore), and display module sub-assemblies (₹8,642 crore). Cumulatively, more than 100 companies have offered investments of over ₹65,000 crore in merely three important segments electro-mechanical components, enclosures, and PCBs.

Industry specialists perceive the ECMS as a game changer in the electronics value chain that has the potential to generate mass employment on a large scale, facilitate technology transfer, and improve global competitiveness. The unprecedented response is also regarded as an indicator of increased confidence in India’s manufacturing ecosystem.

Union Minister for Electronics and IT Ashwini Vaishnaw revealed that against a production target of ₹4,56,500 crore, the government had received proposals for manufacturing electronics components worth over ₹10,34,000 crore. This staggering response underscores the scale of industry interest and further validates the ECMS as a transformative initiative for India’s electronics manufacturing sector.

He called this a “game changer,” emphasizing how the scheme reflects global trust in India’s electronics sector and its potential to transform the country into a manufacturing powerhouse.

The sector has called upon state governments to supplement the Centre’s effort by enhancing ease of doing business, streamlining regulatory procedures, and providing sector-specific incentives to maintain the momentum of investments. Collective action is likely to open up more opportunities, especially in component manufacturing, which would be the bedrock of self-reliant electronics production.

Through involvement by both national and international firms, the ECMS is considered a horizontal programme that will benefit all verticals of the electronics industry. By promoting the creation of sub-assemblies and core components in the country, the initiative will enhance India’s capability in electronic manufacturing and provide a basis for industrial growth in the long term.

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Upgraded Smart Running Trail in Suzhou Industrial Park Achieves Perfect Fusion of Technology and Art

The Jinji Lake Luminous Trail, a project developed by the Suzhou Industrial Park, has won the 2025 MUSE Design Gold Award and the IES Illumination Award of Excellence.

MUSE Design Awards

In June, the Jinji Lake Luminous Trail was honored with the Gold Award in the Innovative Lighting Design category at the 2025 MUSE Design Awards. Recognized as a highly authoritative international award in the global creative design field and often hailed as “the Oscars of the design world,” the trail was one of only two recipients of the highest honor in this category, fully demonstrating the project’s outstanding innovation and design standards.

IES Illumination Awards

In addition to the MUSE Design Award, the Jinji Lake Luminous Trail also received the Award of Excellence in the Control Innovation category at the IES Illumination Awards in August of the same year. The IES Illumination Awards are presented annually by the Illuminating Engineering Society of North America (IES), an organization with over a century of history. Alongside the Lighting Design Awards from Lighting magazine and the IALD International Lighting Design Awards from the International Association of Lighting Designers, the IES Illumination Awards are considered one of the three most prestigious lighting design awards in the world today, representing the highest global design standards of the year. The IES award is the longest-standing of these accolades.

Nuvoton NUC1311 MCU Empowers the Interactive Lighting System and Smart Sensors of the Jinji Lake Trail

Within the trail’s overall smart system, the core control unit is powered by Nuvoton NUC1311 series microcontroller.

The NUC1311’s 5V operating voltage significantly enhances its stability and high noise immunity in harsh outdoor conditions. Furthermore, it supports Bosch-licensed CAN Bus IP, ensuring reliability and industrial-grade communication security during high-speed data transmission. These features enable the NUC1311 to provide real-time, stable smart lighting control for the Jinji Lake Trail’s interactive lighting systems and smart sensing devices, even in the humid and high-interference environment of the lakeside.

The Jinji Lake Luminous Trail was a key urban renewal project for the Suzhou Industrial Park in 2024. It involved upgrading the lighting systems of the Jinshuiwan Trestle Bridge and the lakeside walkways. By integrating technologies such as smart running poles and Bluetooth sensors, two distinctive trails were created: a 15 km smart running trail and a 3.5 km light-chasing interactive trail. The former combines smart planning and AI cameras to enable light interaction and cultural tours, while the latter utilizes DMX512 and Bluetooth dual-mode control, allowing users to select lighting effects via a mobile app for an immersive and interactive experience.

“Our years of partnership with Nuvoton have given us a deep appreciation for the high stability of their products, their comprehensive hardware and software ecosystem, and their excellent real-time service,” stated a representative from Suzhou Tianping. “These advantages are key reasons why we continue to choose Nuvoton products for our large-scale projects. We look forward to deepening our collaboration with Nuvoton in more areas in the future.”

The international design awards received by the Jinji Lake Luminous Trail not only symbolize the successful integration of technology and art but also highlight the formidable strength and international competitiveness of Nuvoton’s MCUs in the fields of smart cities, smart lighting, and interactive design. Moving forward, Nuvoton Technology will continue to provide high-quality, high-performance MCU solutions to help more partners create innovative applications and drive the development of smart cities to new heights.

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As technology advances, most everyday devices depend on short-range communication to exchange or gather data. Although wireless technologies such as Wi-Fi and Bluetooth dominate the market, they are not always the ideal option especially for low-power applications where efficiency, simplicity, and cost management are most important. In these instances, infrared (IR) communication is still an efficient option that energizes applications such as smart meters, wearable electronics, medical devices, and remote controls.

But using an infrared link is not always easy. An IR diode cannot just be attached to a microcontroller pin and be efficient. In order to avoid saturating the diode and to provide a robust signal, a low-frequency carrier is often employed, which then must be modulated by the data stream. Historically, this has involved using more modem chips, timers, and mixers increasing cost, complexity, and additional board space to the design.

The Inefficient Signal Generation Challenge

Fundamentally, infrared communication relies on two key signals:

1. Carrier Frequency – a square wave that paces the IR diode at a suitable frequency.
2. Data Stream – the content of the communication, which must modulate the carrier.

In most implementations, these signals are from various peripherals on a microcontroller and must be merged externally. This adds more components and uses multiple I/O pins, which is not conducive to small, battery-powered devices.

A Smarter Way Forward

Since recent microcontrollers started meeting this challenge, they now provide easier mechanisms for IR signal generation. Instead of needing a separate modem chip, some of these devices combine the timer output (carrier frequency) with the communication output (data) internally. The result is a ready modulation that can directly drive an infrared diode.

An example that offers such capability is RA4C1. Being an 80 MHz device with low-power operating modes down to 1.6 V, it offers an SCI/AGT mask function that combines a UART or IrDA interface output with a timer signal and thus makes it possible to generate the required modulated IR output without any external hardware.

Design Flexibility

The reason this method is efficient is because it is flexible:

• Developers have the option of utilizing a basic UART output that is modulated by a timer-generated carrier.
• Or they can implement an integrated IrDA interface, with provisions for direct modulation or phase-inverted output based on the application requirement.

Both schemes present a clean, stable signal while minimizing the amount of external components and I/O pins needed.

For designers of small electronics like handheld meters, fitness monitors, or household appliances space and power efficiency are key considerations. An IR communication solution with minimal IR circuitry saves cost and enhances reliability by eliminating outside circuitry. It also aids in speeding up product development as engineers no longer need to spend extra time connecting individual modem chips or modulation hardware.

Conclusion:

Infrared communication remains to provide a reliable, low-cost solution for short-range connectivity, particularly in environments where the inclusion of a full radio system is not warranted. With newer microcontrollers embracing built-in modulation capabilities, establishing an IR connection has never been simpler. This change makes it possible for developers to provide smarter, power-sensing products while maintaining simplicity and low cost.

(This article has been adapted and modified from content on Renesas.)

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Quintauris and Everspin Technologies, Inc. announced a strategic collaboration to bring advanced memory solutions into the Quintauris ecosystem.

The collaboration aims to strengthen the reliability and safety of RISC-V–based platforms, particularly for automotive, industrial and edge applications where data persistence, integrity, low latency and security are critical.

By integrating Everspin’s proven MRAM technologies with Quintauris’ reference architectures and real-time platforms, the partnership works to ensure memory subsystems meet the highest standards for performance and functional safety – one of the most pressing challenges in safety-driven markets.

Everspin’s strong commitment to the automotive market extends beyond technology to include proper certifications, manufacturing excellence, long-term supply and continuous quality improvement, values that align closely with Quintauris’ mission to make RISC-V commercially ready for automotive programs.

“Everspin’s leadership in MRAM and their track record of over 200 million products deployed make them a strong addition to our ecosystem,” said Pedro Lopez, Market Strategy Officer at Quintauris. “Together, we are closing the gap between innovation and dependability, enabling RISC-V to be confidently adopted in next-generation automotive programs.”

“RISC-V is opening new doors in safety-critical computing, but it also demands memory that can match its performance and reliability,” said David Schrenk, VP Business Development at Everspin Technologies. “By integrating our MRAM into the Quintauris platform, we’re helping developers build systems that retain data integrity under power loss, radiation or extreme temperatures, without compromising speed or security. This partnership strengthens the foundation for scalable, dependable platforms that will shape the future of automotive electronics.”

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Manufacturing today depends on processes that balance speed, precision, and scalability. Among them, injection molding has become indispensable for industries ranging from healthcare to consumer goods. Its ability to deliver identical, high-quality parts in massive volumes makes it one of the most reliable and cost-effective production methods. But what makes this process so vital, and how exactly does it work?

Understanding Injection Molding

Fundamentally, injection molding is about thrusting molten material into a precisely crafted mold, where it solidifies and takes on its final shape. Plastics are the stalwart of the operation, but producers also apply it to metals and testing uses in new industries. The greatest strength of injection molding is consistency and efficiency once a mold has been made, it can be used to churn out hundreds of thousands of duplicate parts with little deviation.

Unlike subtractive methods such as CNC machining, injection molding is less wasteful of material and can be more flexible in terms of design, with the ability to create everything from small medical devices to large automotive panels.

Industries that Depend on Injection Molding

• Food and Beverage

From yogurt cups to condiment containers, the packaging business relies heavily on injection molding for its light, disposable products. Moving beyond packaging, researchers at one of the University are testing whether this process can be used to mass-produce plant-based meat substitutes, demonstrating how versatile the method can be. In contrast to 3D printing, injection molding offers cost savings and is able to maintain taste and texture in food applications.

• Healthcare and Medical Devices

The medical sector applies injection molding in the production of syringes, implants, and wearables. Due to the stringent regulatory conditions, manufacturers tend to include sensors within the mold to check for temperature and pressure, allowing for perfect outcomes. Robotic equipment is also utilized, which removes faulty components automatically to ensure high levels of safety in patient-care products.

• Sporting Goods and Consumer Products

Leisure goods used daily picnic tableware, coolers, and even high-precision golf clubs are produced with this process as well. Metal injection molding enables golf club manufacturers to create products that improve performance and feedback. Molding single-piece coolers thinner but stronger walls speaks to the process’s efficiency and resilience.

The Injection Molding Process

In any industry and whether small, medium, or large, the injection molding process adheres to a systematic approach:

1. Material Selection – Companies select metals or polymers according to strength, flexibility, durability, or resistance characteristics. Polypropylene is suitable for packaging food, while polycarbonate resists UV exposure for use outside.
2. Design of Mold – Designers make precise steel or aluminum molds with orientation, core, cavity, and mold base in mind. CNC machining is usually employed to cut the mold exactly.
3. Clamping – A clamping mechanism provides pressure to keep the mold halves tightly closed, preventing any leak during the process of injection.
4. Injection – Pellets are melted into molten form, blended by a reciprocating screw, and injected into the mold at regulated velocities and pressures.
5. Dwelling – Pressure is held for a temporary period to guarantee the molten material fills all the cavities of the mold.
6. Cooling – The part solidifies within the mold, a phase often constituting the bulk of cycle time.
7. Opening and Removal – After cooling, the mold is opened and ejector pins force the part out. Any remaining flash material is removed and sometimes recycled.
8. Inspection – Finished parts are visually inspected and tested to detect defects, maintaining consistent quality control.

Why Injection Molding Remains Essential

The scalability, accuracy, and versatility to perform in various industries of the process make injection molding a corner stone of contemporary manufacturing. From life-saving medical technologies to common consumer products, the process continues to transform with automation, robotics, and intelligent sensors, which guarantee ever-greater levels of quality and efficiency.

As industries seek faster, more sustainable, and more innovative ways to produce goods, injection molding remains a cornerstone technology that bridges traditional manufacturing with future possibilities.

(This article has been adapted and modified from content on Revolutionized.)

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The world of manufacturing is changing very fast with digital intelligence merging with the conventional industrial processes. Physical AI lies at the heart of this revolution, bringing together sophisticated algorithms and machinery such as robotic arms, autonomous guided vehicles (AGVs), and CNC machinery. For these systems based on AI to function optimally, they depend on real-time information from industrial sensors. Serving as the “eyes and ears” of machines, sensors today do much more than make measurements they allow AI systems to learn, adapt, and optimize processes to enhance productivity, safety, and efficiency.

The two-part series addresses how industrial sensors enable physical AI applications. The first part discusses sensor types and functions in smart factories, while the second part will discuss innovations and trends that will dominate next-generation physical AI-powered industrial systems.

How Industrial Sensors Enable Physical AI

Industrial sensors measure physical parameters like motion, distance, pressure, temperature, or flow into electrical signals that undergo parameterization. These signals find their way into PLCs, CNC machines, and edge AI devices that carry out real-time decision making.

A typical sensor has some or all of these components: sensing element, operational amplifier OpAmp, ADC, processor, interface, and power management. All these or some of them constitute the sensor acting as a bridge between AI algorithms and the physical world, much like the nervous system transmitting information to the brain.

With a modern smart factory, there is an increase in the deployment of AI at the edge, embedding algorithms in sensors, robots, and controllers themselves. This obviates decision making in real-time being made on cloud-based IT systems alone.

Key Industrial Sensor Types

Vision (Image) Sensors: Cameras used to capture product images for machine vision, inspection, and quality control. They recognize orientation, defects, and positioning in real time. Next-generation short-wave infrared (SWIR) and low-power image sensors provide high dynamic range and low-light capabilities in demanding industrial settings.

Position & Torque Sensors: Hall-effect, optical, and inductive sensors are used to detect motor position and torque. Latest inductive PCB-based sensors combine analog front-ends and controllers to make mechanical design easier while providing improved temperature tolerance and contamination resistance.

Ultrasonic Sensors: Detect distance by emitting ultrasonic waves. Suitable for detecting transparent objects, ultrasonic sensors are widely applied in autonomous robots for navigation and obstacle detection and in process automation for flow and level measurement.

Photoelectric Sensors: Capture objects using light-based technologies infrared or laser and come in through-beam, retroreflective, and diffuse-reflective configurations. They are non-contact, flexible, and accommodate long detection ranges.

Proximity Sensors: Sense metallic objects using electromagnetic induction without contact. They are durable in harsh environments and can be used in conjunction with ultrasonic or photoelectric sensors to detect non-metallic objects.

Pressure Sensors: Condition clean-room environments and pneumatic or hydraulic systems. They deliver accurate voltage readings that represent system pressure using strain gauges or force resistors.

Temperature Sensors: Monitor and control temperature in various industries. Thermocouples, RTDs, and semiconductor temperature sensors protect machinery and stabilize processes.

Environmental Sensors: Add gas, chemical, rain, and light sensors to measure environmental conditions and workplace safety. For example, electrochemical sensors can measure chemical currents at low power consumption, providing constant monitoring.

Selecting the Correct Sensors for Intelligent Manufacturing

When designing industrial systems with AI, engineers should keep in mind:

1. Application Response Speed & Accuracy: Response speed and accuracy should be suited to the job, from control of robots to quality inspection in real time.
2. Data Reliability: Sensors need to deliver high-quality data reliably to enable AI learning and analytics.
3. Integration & Interoperability: Sensors need to integrate seamlessly with PLCs, field buses, and other industrial automation.
4. Data Privacy & Cybersecurity: Preserving sensitive operating data is essential, particularly as sensors communicate data through networks.
5. Energy Efficiency: Sensors with low power consumption allow widespread deployment without exceeding power budgets.

Conclusion:

Industrial sensors are critical to enable physical AI in the smart factory spaces. By sensing the physical world accurately and interpreting it, these sensors enable AI systems to make quicker, wiser, and more secure decisions. With advancements in sensor technologies, they will further propel more intelligent, adaptive, and more sustainable industrial activities, leading the way to Industry 5.0.

With its extensive sensor portfolio and application know-how, Onsemi continues to be the leader in intelligent sensing, assisting manufacturers to unlock the full value of physical AI.

(This article has been adapted and modified from content on Onsemi.)

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A breakthrough in wearable technology is redefining what smart glasses can do: generative AI running entirely on the device, without the need for a phone or cloud connection. Powered by the new Snapdragon AR1+ Gen 1 platform, the glasses allow an AI to interact seamlessly in every day scenarios-from shopping or any home tasks.

AI Fitting Inside Glasses

In a live demonstration, a generative AI assistant operated directly on smart glasses using a compact language model (SLM). During a simulated grocery trip, the assistant helped with a recipe, delivering audio guidance and text directly on the lenses all without any external device. This is a strong demonstration of what is going on with smart glasses from mere accessories to full-blown, standalone AI tools.

Snapdragon AR1+ Gen 1

The Snapdragon AR1+ Gen 1 processor, 26% smaller than previous generations, brings enhanced power efficiency, improved image quality, and the ability to run small language models directly on the glasses. These improvements are crucial for thinner, lighter frames that don’t compromise performance or functionality.

Flexible XR Ecosystem

Next-generation smart glasses will be available in various form factors. Some will be standalone, and others will be linked to nearby devices like smartphones, tablets, or portable computing “pucks.” This modular system provides flexible, high-performance experiences across various configurations while keeping AI interactions speedy, private, and responsive.

Improved Vision and Multimodal Inputs

Sophisticated camera features enable glasses to record and perceive the world in rich detail, enabling proactive suggestion and context-sensitive help. Even when not connected to other devices, these glasses can be paired with other wearables like smartwatches or rings, enabling new forms of interaction and input.

Conclusion

This demonstration represents the beginning of a new era in wearable AI, in which intelligent glasses have the capability to provide tailored, real-time support on the move. Powered by the Snapdragon AR1+ platform, Qualcomm is making some of the thinnest, cleverest, and most powerful glasses possible that might change the way we engage with technology in our everyday lives.

(This article has been adapted and modified from content on Qualcomm Technologies.)

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The hardware, software and  Intelligent Factory concept presented by market and technology leader ASMPT drew strong interest from trade visitors at this year’s productronica India.

At the joint booth with long-standing distribution partner Maxim SMT, the spotlight was on the fast, precise, and process-stable DEK TQ solder paste printer platform and the SIPLACE TX high-speed placement solution. The SIPLACE CP20 and SIPLACE CPP placement heads on display also proved particularly well suited to the high-volume production that characterizes the Indian market, offering manufacturers maximum flexibility and productivity in demanding high-volume production.

Integrative concepts for high-volume production

Many visitors took the opportunity to gain a detailed understanding of a complete ASMPT production line in personal technical discussions. Of particular interest was the integrated concept of the intelligent factory, where standardized interfaces across all ASMPT machines continuously collect and process data, making it available where it can be used to enhance quality, prevent errors, and eliminate production bottlenecks.

Comprehensive software portfolio

ASMPT’s extensive software portfolio attracted strong interest from the expert audience. At the core is the WORKS Software Suite, which supports all line-related processes, complemented by the Factory Solutions for holistic optimization across the entire manufacturing environment – including critical areas such as material intralogistics. Live demonstrations featured WORKS Optimization, the intelligent inline expert system for end-to-end process improvement; the Factory Equipment Center, an integrated asset and maintenance management system; the Material Flow Optimizer, ensuring efficient intralogistics and smooth material supply; and SMT Analytics, providing in-depth analysis of the entire SMT production process across all lines.

“We were very pleased with the strong interest shown in our insights and the solutions we showcased for state-of-the-art electronics manufacturing,” summarized Neeraj Bhardwaj, General Manager for India at ASMPT SMT Solutions. “The lively response confirms that we are on the right track in this important growth market.”

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New digital micromirror device with real-time correction enables equipment manufacturers to achieve high-resolution printing at scale, maximizing throughput and yield

What’s new

Texas Instruments is enhancing the next generation of digital lithography with the introduction of the DLP991UUV digital micromirror device (DMD), the company’s highest resolution direct imaging solution to date. With 8.9 million pixels, sub-micron resolution capabilities and a data rate of 110 gigapixels per second, the device eliminates the need for expensive mask technology while delivering the scalability, cost-effectiveness and precision needed for increasingly complex packaging.

Why it matters

Maskless digital lithography machines – which project light for etching circuit designs on materials without a photomask or high-end stencil – are becoming increasingly popular for the manufacturing of advanced packaging. Advanced packaging combines multiple chips and technologies into a single package, enabling high-computing applications, such as data centers and 5G, to have systems that are smaller, faster, and more power-efficient.

With TI DLP technology, system assembly equipment manufacturers can leverage maskless digital lithography to achieve the high-resolution printing at scale necessary for advanced packaging. The new DLP991UUV acts as a programmable photomask, offering precise pixel control with reliable high-speed performance.

“Just as we redefined cinema by enabling the transition from film to digital projection, TI’s DLP technology is once again at the forefront of a major industry shift,” said Jeff Marsh, vice president and general manager of DLP technology at TI. “We’re enabling the creation of maskless digital lithography systems that empower engineers around the world to breakthrough the current limits of advanced packaging and bring powerful computing solutions to market.”

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Anritsu Corporation will participate in the upcoming India Mobile Congress (IMC) 2025, taking place in New Delhi, India, from October 8 to October 11, to showcase its latest innovations in communications test and measurement solutions.

As the mobile and connectivity industry continues to expand with the rapid adoption of 5G, IoT, and emerging technologies such as AI-driven services, cloud computing, and immersive XR applications, the demand for robust, reliable, and efficient test solutions has never been greater. At IMC 2025, Anritsu will highlight its comprehensive portfolio designed to meet these evolving needs, supporting operators, device manufacturers, and ecosystem partners in accelerating their technology development and deployments.

Virtual Signalling Tester

5G Network Simulator, a software-based solution for 5G IoT chipset and device testing. It enables virtual 5G network simulation on a PC, supporting RedCap tests and efficient device verification.

Radio Communication Test Station MT8000A

All-in-One Support for RF Measurements, Protocol Tests and Applications Tests in FR1 (to 7.125 GHz) and FR2 (Millimeter-Wave) Bands. MT8000A is used by Mobile Chipset, Mobile Handset, IoT Device, 5G base Station R&D and manufacturing companies.

Field Master Pro MS2090A

Handheld Spectrum Analyzer delivers the highest continuous frequency coverage up to 54 GHz and real-time spectrum analysis bandwidth up to 150 MHz to address current and emerging applications such as 5G &LTE Base Station Measurement, Satellite System Monitoring, Interference Hunting, EMF measurement and much more.

Anritsu Collaborates with Altair to Demonstrate Integration of Anritsu Monitoring Systems with Spectrum Management Software WRAP.

Altair WRAP integrates georeferenced data from Anritsu spectrum analyzers to validate coverage, interference, and spectrum compliance with field reality.

VectorStar Broadband VNA ME7838

The VectorStar ME7838 Series broadband VNA offers the widest available 2-port single frequency sweep from 70 kHz to 110, 125, 145, and 220 GHz with mmWave bands up to 1.1 THz. Vector Star is a cost-effective solution for OnWafer Measurements, RIS, Novel Channel Sounding applications along with active and passive devices measurement supporting 5G and 6G technology.

Optical Spectrum Analyzer MS9740B

MS9740B offers Single mode and Multimode Fiber application and high-speed optical devices such as optical transceivers, VCSEL, and DFB light sources testing R&D and production.

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The industrial scenery is getting reshaped by digital twins and physical AI. These virtual replicas of factories, facilities, or even processes were once mainly conceived for planning purposes and now have become more operationally oriented, mainly concerned with training autonomous robots, AI-powered machinery, and operational systems to perform their tasks safely and efficiently in the real world. High-tech OpenUSD, immersive simulation tools, and AI-driven modeling are helping developers create high-fidelity digital twins at scale, removing most of their manual labor and fast-tracking industrial AI deployment.

Scaling Industrial AI and Physical AI with Digital Twins

Digital twins provide a virtual environment within which physical AI agents such as autonomous robots or smart factory systems can learn and adapt before deployment. Simulations of a finer quality came at the cost of much manual effort. Today, with advanced OpenUSD, neural reconstruction, and world foundation models (WFMs), developers can now set about constructing these complex digital replicas far more rapidly.

Key developments include:

SDKs bridging between simulators: They allow people to simulate robots and systems in diverse simulators, thus virtually providing access for robotics developers anywhere in the world.

• Neural rendering and 3D reconstruction libraries: These allow the capture and reconstruction of sensor data from the real world, simulation, and photorealistic rendering.
• Open-source robotics frameworks: Offer readymade environments and schemas for robots and sensors to help reduce the simulator-to-reality gap.
• World foundation models (WFMs): Used to create synthetic datasets and to carry out higher-order reasoning on these datasets for the benefit of physical AI applications.
• Advanced rendering and AI-assisted material modeling: Provide scalable ways to create industrial-grade digital twins.

OpenUSD: Powering the Future of Industrial 3D Innovation

OpenUSD constitutes the backbone of industrial 3D workflows, having become a standard for digital twin creation with interoperability between industrial and 3D data. By now, the Alliance for OpenUSD (AOUSD) has been extended to include Accenture, Esri, HCLTech, PTC, Renault, and Tech Soft 3D, thus showing great endorsement of OpenUSD and present objectives of uniting industrial 3D workflow.

To support this growing ecosystem, NVIDIA has introduced an industry-recognized OpenUSD development certification and a digital-twins learning path, helping developers gain the skills needed to build the factories and industrial systems of tomorrow.

Industry Applications Driving the Future:

Some of the global leaders use digital twins and OpenUSD for transforming industrial operations:

• Siemens: Teamcenter Digital Reality Viewer allows working with large-scale digital twins for visualization and collaboration, thereby reducing physical prototyping and faster time-to-market.
• Sight Machine: Operator Agent platform amalgamates live production data with AI-driven recommendations and digital twins for better plant visibility and faster decision-making.
• Rockwell Automation: Emulate3D Factory Test creates physics-based digital twins from simulation to optimize automation and autonomous systems.
• EDAG: Uses digital twin for project management, production layout optimization, worker training, and data-driven quality assurance.
• Amazon Devices & Services: Uses digital twin environments to train robot arms for assembly, testing, packaging, and auditing, all with no physical intervention.
• Vention: Offers plug-and-play digital twin and automation solutions so intelligent manufacturing systems can be deployed more speedily.

Conclusion:

The combination of OpenUSD, digital twins, and AI-driven simulation is transforming industrial operations on the ground. By proving the exact, scalable virtual environment, they allow manufacturers, robot developers, and physical AI engineers to innovate faster, cut down expenses, and systematize safer and smarter solutions faster than ever before.

(This article has been adapted and modified from content on NVIDIA.)

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The global energy sector is at a historic turning point. Renewable energy integration, EV promotion, and AI-driven consumption create more demand on already complex grids. The transformation calls for a new era of energy professionals who can build a bridge between traditional engineering and digital technologies-the infrastructure upgrades alone cannot solve the equation.

The Digital Shift in Energy Systems

Modern power systems evolve into interconnected, intelligent networks. Smart grids, real-time balancing, and consumer-driven energy management are redefining how electricity flows. Still, the digital revolution carries many challenges requiring upskilling and interdisciplinary knowledge to solve.

Top Challenges Facing the Next Generation Workforce:

1. Dual-Skill Gap

Engineers today need expertise in network-relevant issues and traditional grid operations, plus in cybersecurity matters. Still, there are few professionals with an engineering background and digital expertise; this scarcity leads to inefficiency in troubleshooting and system reliability.

2. A Shift Toward Virtualization

Careful changes from hardware-based to software-driven operations have increasingly taken protection and control functions onto a virtual platform. Hence, engineers will have to embrace digital tools with data analytics and server technologies that are not traditional to the power area.

3. Cross-system Collaborations

Data exchanges must be smooth as renewable assets such as solar and battery storage interfacing with distribution and transmission networks. Therefore, engineers must manage various protocols and formats, settling voltage, frequency, and power flows after the interface in real time.

Building the Workforce of Tomorrow

Such challenges require: Full-training in digital communication, grid standards such as IEC 61850, and advanced networking.

Simplified Tools and Platforms that reduce technical complexity and enable engineers to focus on system optimization.

Collaborative Ecosystems where power engineers, IT experts, and operators work together to maintain resilience across distributed networks.

Conclusion:

The future of energy will be shaped as much by people as by technology. Companies that invest in digital skills, upskilling programs, and collaborative frameworks will lead the transition to resilient, intelligent grids. Industry leaders such as Moxa, with their training initiatives and global expertise, are playing a vital role in equipping professionals to thrive in this new era ensuring the workforce is ready to power the grids of tomorrow.

(This article has been adapted and modified from content on Moxa.)

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ANRITSU CORPORATION has developed and launched its new 60 GHz optical sampling oscilloscope MP2110A-080 option for the BERTWave MP2110A. This option verifies the performance of 200G/Lane optical transceivers forming the foundation of faster data-center communications and growing AI deployment. It delivers high PAM4 TDECQ evaluation accuracy and measurement productivity for next-generation high-speed optical transceivers, such as 1.6T and 800G, supporting strong quality assurance of large-capacity, high-speed communications infrastructure.

This test solution was exhibited as a reference at the China International Optoelectronic Exposition (CIOE 2025) on September 10, 2025, and will also be showcased at the European Conference on Optical Communication (ECOC 2025), one of the world’s leading international conferences in the field of optical communications, to be held in Copenhagen, Denmark, from September 29 to October 1, 2025.

Development Background
With the growth of AI data centers, optical communication speeds are increasing from 800G to 1.6T, and transmission rates are shifting from 50 Gbaud (100G/Lane) to 100 Gbaud (200G/Lane). As transmission speeds increase, there is a growing need for wideband sampling oscilloscopes capable of evaluating higher frequency components in optical transceiver signals.

Product Features
The all-in-one MP2110A solution integrates the necessary functions for physical-layer evaluation of optical transceivers during development and manufacturing. This new 60 GHz oscilloscope MP2110A-080 option enables evaluation and analysis of next-generation high-speed 200G/Lane communication standards.

• High-Accuracy PAM4 TDECQ Measurement: With the performance of a reference receiver supporting PAM4 signals up to 120 Gbaud, the MP2110A offers reliable TDECQ evaluations by leveraging the high measurement accuracy of existing models.
• Improved Efficiency with Simultaneous 4-Channel Measurement: By measuring four optical signals simultaneously, the MP2110A cuts measurement time and improves operation efficiency. Batch evaluation of multiple channels simplifies measurement systems and processes to enhance productivity.
• Further Productivity Gains with Faster Measurement: Increasing the MP2110A sampling speed fourfold compared to previous models shortens measurement times even further. Stable operation with a built-in PC improves R&D and manufacturing efficiency.
• Cost-Effective 4-Channel Software Upgrade Option: With a software upgrade path to 4-channels, the 2-channel option lowers initial costs, allowing flexible deployment supporting future expansion matching budget and evaluation environment.

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When we ask if AI knows anything, we are, in the strictest sense, not referring to memory or experience as humans would. Instead, we are exploring a very complex mathematical domain in which AI predicts what comes next in a language. Upon realization, AI is not a particular source of truth; it is a system that simulates understanding through patterns, probabilities, and memory architecture. This article attempts to unravel the puzzle of how AI converts text into knowledge-like predictions, from tokens and embeddings to the machines that carry out these operations.

From Words to Tokens

AI does not interpret after human fashion. Upon encountering the sentence “The moral of Snow White is to never eat …,” it first converts it into some string of tokens-the smallest units it can process. Tokens can be whole words, parts of words, punctuations, or spaces. For example, the sentence above would be tokenized as:

[“The” | ” moral” | ” of” | ” Snow” | ” White” | ” is” | ” to” | ” never” | ” eat”]

This conversion is only the initial step of a highly structured process that takes human language and converts it into something an AI can work with.

Embeddings: From Tokens to Numbers

Upon tokenization, each token is mapped to an embedding-an abstract numerical representation revealing the statistical relationship S-theory between words. These embeddings exist in a high-dimensional embedding space-theoretical map of word associations learned after the analysis of great volumes of text. Words that appear in similar contexts cluster together-not really because the AI “understands” them in the human sense-but because language-based hypothesis-building patterns suggest they are related. For instance, “pirouette” and “arabesque” might cluster together, just as “apples” and “caramel.” The AI does not comprehend these words in human terms; it simply recognizes patterns of their co-occurrence.

Simulated Knowledge

Human beings derive meaning from experience, culture, and sensation. AI, on the other hand, simulates knowledge. So, when arguing for sentence completion, it invents statements: “food from strangers,” “a poisoned apple,” or simply “apples.” Each is statistically plausible, yet none comes from comprehension. AI is about predicting what is likely to be next, not what is “true” in a human sense.

The Abstract World of the Embedding Space

Embedding space is where AI’s predictions live. Each word becomes a point in hundreds or thousands of dimensions, having something to do with the patterns of meaning, syntax, and context. For example, in a simplified 2D space, “apple” might cluster near “fruit” and “red.” Add more dimensions, and it could relate to “knowledge,” “temptation,” or even “technology,” denoting its cultural and contextual associations.

Because such spaces are high-dimensional, they cannot be directly visualized, but serve as a backdrop against an AI’s scenario of language prediction. The AI does not consider concepts or narrative tension; it calculates statistically coherent sequences.

From Math to Memory

These embeddings are not just theoretical matrices; they require physical memory. The embedding of each token consists of hundreds or thousands of numerical entries, which are stored in various memory systems and worked upon by hardware. As the size of the AI model increases and it accords with more tokens, memory turns out to be one major issue, regarding the speed and complexity of predictions.

Originally created for scientific work, High-bandwidth memory (HBM) would be applied towards AI so models can efficiently handle overwhelming amounts of data. Memory is no longer merely a storage device; it determines the amount of context an AI remembers from training examples and how quickly it accesses this information to make predictions.

Looking Ahead

The knowledge base of an AI has always depended on what the AI can hold in-memory. As longer conversations or more complicated prompts would require more tokens and embeddings, so would the memory requirements. These limitations end up shaping the way the AI represents the context and keeps coherence in text generation.

Understanding AI’s statistical and hardware basis does not undermine the usefulness of AI; rather, it sets its interpretation to that of a very complex system of probabilities and memory, instead of some kind of conscious understanding.

(This article has been adapted and modified from content on Micron.)

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Beyond 300 Layers of Memory

The race to make denser, more-powerful 3D NAND flash memory has led to huge innovation but also new manufacturing challenges. Taller devices-three-hundred-plus layers-could be threatened in yield, performance, and reliability due to constructive-tier bending and material collapse. In this sense, these challenges come from stress mismatches in alternating stacks of silicon nitride (SiN) and oxide (TEOS) layers that constitute this memory structure.

To comprehend and solve the problem, the Semiverse Solutions team used SEMulator3D virtual Design of Experiments (DOE) to replicate, measure, and analyze stress-induced deformation in the fabrication process. The outcomes emphasize the very important consideration of stress management and material properties in realizing manufacturable high-layer-count NAND architectures.

Understanding How 3D NAND Is Built

It achieves higher densities in 3D NAND by stacking SiN and oxide layers vertically in a staircase arrangement. Contacts are etched through such tall stacks to reach underlying transistors, and slit etchings divide the structure into functional memory blocks.

Until SiN can be replaced by conductive metal, an oxide cantilever is temporarily formed: it is anchored at one end while being unsupported at the other end. This rather fragile structure increasingly becomes vulnerable as the number of layers grows, expanding from ~550 nm at 200 layers to ~700 nm at 300 layers. Various contributors to tier collapse are as follows:

• Stress and strain mismatches between SiN and oxide
• Surface tension during SiN removal
• Cantilever length and geometry

What the Virtual Studies Revealed

Using SEMulator3D’s stress analysis tools, the team conducted two DOE studies to characterize how stress may evolve with tier bending and collapse.

Key findings from the first DOE:

• SiN Stiffness (Young’s Modulus, Ey) and oxide thickness are the dominant variables influencing stress-based deformation.
• Present at low Ey values (70 GPa) due to minimal displacement.
• At 125 GPa, collapse occurred at longer cantilever lengths (700 nm), especially with thinner oxides.
• At 256 GPa, severe displacement and voiding occurred across all test conditions.
• Increasing oxide thickness improved resistance but did not eliminate failure risks.

The second DOE compared the effects of intrinsic SiN stress (compressive vs. tensile). Results showed compressive SiN caused larger displacements, widening the range of potential collapse.

The manufacturing implications

These studies present obvious engineer methods that can be employed to maximize yields in ultra-high-layer NAND:

• The SiN and oxide stress values need to be matched and hopefully reduced.
• Shorten cantilever length by designing an etch profile.
• If possible, increase oxide thickness to stabilize the stack.

Through virtual simulation of these interactions, SEMulator3D engineers have the ability to realize the process changes that actually matter without being solely reliant on expensive experimental work on the actual silicon.

Conclusion

With NAND flash closing in on 300 layers and more, tier bending and collapse remain edge manufacturing threats. Stress analyses and virtual DOE studies by the Semiverse team have revealed that exacting control of material properties and stack geometry is key to both securing yields and shortening time to market.

With the SEMulator3D platform from Lam Research, chipmakers gain a powerful predictive lens helping transform potential failure points into opportunities for robust, scalable memory innovation.

(This article has been adapted and modified from content on Lam Research.)

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‘Nature Electronics’ Paper Details System That Blends Best Traits Of Once-Incompatible Technologies—Ferroelectric Capacitors and Memristors

Breaking through a technological roadblock that has long limited efficient edge-AI learning, a team of French scientists developed the first hybrid memory technology to support adaptive local training and inference of artificial neural networks.

In a paper titled “A Ferroelectric-Memristor Memory for Both Training and Inference” published in Nature Electronics, the team presents a new hybrid memory system that combines the best traits of two previously incompatible technologies—ferroelectric capacitors and memristors into a single, CMOS-compatible memory stack. This novel architecture delivers a long-sought solution to one of edge AI’s most vexing challenges: how to perform both learning and inference on a chip without burning through energy budgets or challenging hardware constraints.

Led by CEA-Leti, and including scientists from several French microelectronic research centers, the project demonstrated that it is possible to perform on-chip training with competitive accuracy, sidestepping the need for off-chip updates and complex external systems. The team’s innovation enables edge systems and devices like autonomous vehicles, medical sensors, and industrial monitors to learn from real-world data as it arrives adapting models on the fly while keeping energy consumption and hardware wear under tight control.

The Challenge: A No-Win Tradeoff

Edge AI demands both inference (reading data to make decisions) and learning (updating models based on new data). But until now, memory technologies could only do one well:

• Memristors (resistive random access memories) excel at inference because they can store analog weights, are energy-efficient during read operations, and the support in-memory computing.
• Ferroelectric capacitors (FeCAPs) allow rapid, low-energy updates, but their read operations are destructive—making them unsuitable for inference.

As a result, hardware designers faced the choice of favoring inference and outsourcing training to the cloud, or attempt training with high costs and limited endurance.

Training at the Edge

The team’s guiding idea was that while the analog precision of memristors suffices for inference, it falls short for learning, which demands small, progressive weight adjustments.

“Inspired by quantized neural networks, we adopted a hybrid approach: Forward and backward passes use low-precision weights stored in analog in memristors, while updates are achieved using higher-precision FeCAPs. Memristors are periodically reprogrammed based on the most-significant bits stored in FeCAPs, ensuring efficient and accurate learning,” said Michele Martemucci, lead author of the paper.

The Breakthrough: One Memory, Two Personalities

The team engineered a unified memory stack made of silicon-doped hafnium oxide with a titanium scavenging layer. This dual-mode device can operate as a FeCAP or a memristor, depending on how it’s electrically “formed.”

• The same memory unit can be used for precise digital weight storage (training) and analog weight expression (inference), depending on its state.
• A digital-to-analog transfer method, requiring no formal DAC, converts hidden weights in FeCAPs into conductance levels in memristors.

This hardware was fabricated and tested on an 18,432-device array using standard 130nm CMOS technology, integrating both memory types and their periphery circuits on a single chip.

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Microchip’s MCP9604 thermocouple conditioning IC reduces the cost and complexity of in-line production applications that operate in high and low temperature extremes

Precision four-channel temperature measurement is critical for production-line applications ranging from chemical and food processing, manufacturing process control and medical and HVAC equipment to refrigerated, cryogenic and other carefully controlled environments. With the introduction of the MCP9604 integrated thermocouple conditioning IC, Microchip Technology has overcome a thermal measurement and integration barrier with the first single-chip, four-channel  I2C thermocouple conditioning IC to deliver up to ± 1.5°C accuracy and provide an alternative to discrete and multichip thermocouple conditioning solutions that can introduce errors and add system design complexity.

“For more than two centuries, the thermocouple has been a critical tool for measuring extremely high temperatures, but the necessary precision and accuracy could not be achieved with the level of integration and cost-effectiveness that is required for today’s demanding production-line applications,” said Keith Pazul, vice president of Microchip’s mixed-signal linear business unit. “Our device now delivers a combination of precision, integration and cost-effectiveness, helping reduce the need for as many as 15 discrete components and associated system design challenges.”

The MCP9604 device delivers its advanced measurement accuracy at four thermocouple locations by using higher-order NIST ITS-90 equations rather than the single-order linear approximations of analog amplifier designs. As an example, it achieves ninth-order accuracy with K-type thermocouples, all in one integrated chip containing the ADCs, cold junction compensation temperature sensors, amplifiers and other components required for the signal chain, temperature measurement and math engine.

Removing the need for external components simplifies PCB design, reduces bill of materials costs, and can help eliminate the weeks of costly, time-consuming and complex unit-by-unit in-line validation and calibration that discrete solutions require in the thermocouple measurement signal chain before they can begin reporting data to the host system.

The MCP9604 also offers flexibility and versatility by supporting the eight most common thermocouple types including the J option as well as the K option for operating at temperatures as low as
-200°C. In addition to supporting a wide, -200°C to +1372°C temperature range across a diverse range of industrial applications, the MCP9604 also supports I2C communication to allow easy integration with microcontrollers and other digital systems.

Building on Earlier Advancements

The MCP9604 builds on the release of Microchip’s single-channel thermocouple conditioning IC, the first all-in-one device to deliver up to ± 1.5°C accuracy. The core competencies that made this device possible have paved the way for the company’s four-channel single-chip MCP9604 device that delivers its digital temperature reading with industry-high accuracy levels for an I2C thermocouple conditioning device.

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As cities move toward electric mobility and smarter infrastructure, seamless and safe power delivery is more important than ever. Shivam Rajput, Founder and CEO of ElectraWireless, is pioneering wireless electricity solutions that reduce EV downtime, extend fleet lifecycles, and power devices without cords or plugs. Combining advanced materials science, adaptive resonant coupling, and smart thermal management, his innovations aim to make wireless power scalable, safe, and efficient. In this conversation with ELE Times, he shares lessons from pilots, technological breakthroughs, and how India could benefit from cost-effective, large-scale wireless EV infrastructure.

Excerpts:

ELE Times:  What implications does wireless electricity have for EV adoption, safety, and the broader global energy transition?

Shivam Rajput: Wireless electricity isn’t just about convenience, it addresses real consumer challenges and can help the EV market thrive. EV adoption today is often slowed by downtime, manual charging, connector wear, and safety concerns. Consumers want simple, safe, and sustainable solutions, not just car features. Wireless electricity ensures EVs charge automatically at parking spots or even while moving, maintaining battery health and keeping vehicles ready at all times. Beyond EVs, homes, workplaces, and cities become safer with fewer exposed wires and connectors, reducing the risk of accidents and outages. This technology also minimizes energy waste, making it a crucial step in the global energy transition.

ELE Times: What are the key breakthroughs that have enabled high-power wireless electricity transmission through everyday surfaces like wood, quartz, or automotive-grade materials?

Shivam Rajput: Our system delivers power only when needed, without heating surfaces or wasting energy. Materials innovation allows seamless integration into wood, quartz, automotive-grade panels, and other common surfaces. Safety is ensured through foreign object detection, which automatically halts transmission if anything interferes. For autonomous systems, from robotics to EVs, devices no longer need to stop to plug in; they charge automatically wherever transmitters are installed. These breakthroughs make high-power wireless electricity scalable, safe, and efficient across multiple sectors.

ELE Times:  What lessons emerged from pilots in robotics, kitchens, and workplace environments, and how are they shaping your approach to scaling the technology?

Shivam Rajput: Pilots highlighted three critical lessons: seamless integration, safety, and efficiency. In smart kitchens, multiple appliances operated wirelessly without interference, showing the importance of modular design. Workspaces benefited from embedded, unobtrusive power, improving usability and safety. In robotics and autonomous systems, wireless charging dramatically reduced downtime, enabling continuous operation and boosting productivity. Eliminating manual plug-ins also reduces electrical faults, making devices safer for children and workplaces. These insights inform a scalable platform ready for enterprise-level and consumer applications.

ELE Times:  In what ways could wireless charging reduce downtime and extend the lifecycle of EV fleets?

Shivam Rajput: Wireless charging allows EVs to charge in motion or at strategically located parking spots, reducing wear on connectors and preserving battery health. Fleets can operate longer, with fewer interruptions, while maintenance costs decrease. This contactless approach accelerates operations and reduces total cost of ownership, making EV fleet management more efficient and sustainable.

ELE Times:  Can wireless power assist in building scalable, cost-effective EV infrastructure in countries such as India?

Shivam Rajput: India is one of the most promising markets for EV adoption. Our retrofit-friendly wireless system integrates with existing grids, lowering installation complexity and costs. By embedding chargers into roads, parking spots, or city infrastructure, EVs can charge seamlessly while driving or parked, what we call “monorail charging.” This approach enables large-scale adoption, ensures reliability, and reduces safety risks associated with exposed connectors. The system supports faster EV market growth while building a sustainable, energy-efficient infrastructure.

ELE Times: What technological advances from ElectraWireless enabled them to scale the transmission of wireless power from as low as 5W all the way up to 40kW?

Shivam Rajput: Adaptive resonant coupling, dynamic field shaping, and smart thermal management allow safe and efficient power delivery across surfaces, from small electronics to EVs. Foreign object detection ensures absolute safety during transmission. Precision energy delivery reduces waste and maintains high efficiency for continuous operation. These advances unlock a fully scalable wireless electricity ecosystem, enabling applications in robotics, kitchens, workspaces, and urban EV infrastructure.

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Satellites, spacecraft, and defense systems rely on increasingly complex software ecosystems that integrate open-source, third-party, and legacy components. Recent cybersecurity events have highlighted how vital it is to track, secure, and manage these software supply chains.

The Risk of Vulnerable Third-Party Components

At Black Hat 2025, some very serious vulnerabilities were discovered in some of the most commonly used platforms for satellite control: Yamcs, OpenC3 Cosmos, and NASA’s cFS Aquila. Such flaws-range from remote code execution, denial of service, weak encryption to manipulation of satellite operations-force criminals into changing orbital paths or stealing cryptographic keys, usually without even detection.

Even seeming-to-be-secure encryption libraries such as CryptoLib-which NASA uses-were found to harbor multiple critical vulnerabilities. Exploiting these, attackers could crash the onboard software, reset its security state, or compromise encrypted communications. These findings reinforce that third-party components remain among the easiest risks to exploit in aerospace and defense software.

SBOMs: Ensuring Transparency Across the Software Stack

Software Bill of Materials lists all components within a system involved. In practice, it finds vulnerabilities, manages risk, considers compliance, and goes into incident response. The SBOM can be only as good as its accuracy, completeness, or governance structure.

In other words, to improve security posture, an organization must hold centralized processes for the validation, enrichment, and continuous surveillance of SBOMs, so that both upstream ones (those from development) and downstream ones (those from deployed systems) are held accountable, validated, and acted upon.

Closing the Gaps

Modern SBOM platforms, such as Keysight’s solutions, enhance binary similarity checks and code emulation to detect components when source information is partial or missing. This allows SBOMs to be reliably created for firmware and software or for container images so that no single component-in whatever form it exists-goes untracked.

Hence, giving full visibility, rigorous validation, and operational governance serve systems in aerospace and defense better in recognizing vulnerabilities, quick incident response, and establishing trust across software supply chains. This closes critical gaps while trying to keep mission-critical systems safe from the ever-evolving cyber threats.

(This article has been adapted and modified from content on Keysight Technologies.)

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Electrochemical impedance spectroscopy (EIS) is a powerful technique for studying electrochemical systems such as electrochemical cells, batteries, fuel cells, corrosion protection setups, and sensors. By differentiating processes such as charge transfer across the electrode interface, diffusion, double-layer behavior, etc., by applying small sinusoidal signals generated in random magnitudes over a wide frequency range, we invoke responses from such mechanisms. Equivalent circuits in the traditional sense can conveniently give impedance data representations; however, they do not suffice when overlapping or nonideal processes come into play. Modern physics-based modeling approaches enable the researcher to consider adsorption, mass transport, and electrode surface effects far beyond simple resistor–capacitor analogies.

EIS Real-Life Applications

Sensitivity renders EIS paramount for:

Batteries: Detects ion and electron transport at early stages of degradation and capacity fading.

Corrosion: Detects subtle interface changes between metal and electrolyte in pipelines, concrete, and marine structures.

Fuel Cells: Performance and durability improvements by separating contributions of catalyst layers, membranes, and reactant flows.

Sensors: Evaluates electrode interactions with target molecules, enabling applications like glucose monitoring.

The Limitations of Equivalent Circuits

For the simpler reactions, the impedance data frequently fit an elementary equivalent circuit: a resistor in series with a parallel resistor-capacitor pair. In a Nyquist plot, this will look like a neat semicircle corresponding to charge transfer resistance. However, rarely do real systems behave so nicely. Adsorption, diffusion, and electrode morphology will add time constants and overlapping processes with which the equivalent circuit cannot always keep up. Physics-based models are, therefore, chosen to solve the underlying electrochemical equations, thus providing a more accurate picture of how these processes may interrelate.

Consider the Nonidealities of EIS:

Important Factors

1. Adsorption–Desorption Dynamics

Intermediates may adsorb on electrodes during electrochemical reactions. The changing surface coverage may, over time, change the impedance response. For instance, with copper deposition, a progressive increase in coverage of additives changes the spectra from two capacitive loops into one dominated by an inductive loop at low frequency. Such effects demonstrate the crucial nature of adsorption in the design of such systems.

2. Mass Transport Limitations

In fuel cells, the diffusion and convection of gases such as hydrogen and oxygen significantly affect performance. Through impedance plots, one can observe the changes in charge-transfer and diffusion contributions as functions of the operating potential:

• Distinct high- and low-frequency loops at intermediate voltages
• At low voltages, loops combine with overlapping time constants
• On the strongly cathodic side, diffusion is dominant, and a single huge loop appears

This sequence clearly demonstrates the ability of EIS to differentiate between reaction kinetics and transport limitations.

3. Electrode Surface Effects

Surface roughness and uneven geometries alter the effective electrochemical area, thus shifting the impedance response. Accounting for electrode structures helps render better predictions in situations where morphology is important.

Handling Residual Behaviors

Sometimes, the impedance response cannot be explained by referring to adsorption, diffusion, or surface structure. A constant phase element (CPE) is then introduced to incorporate frequency-dependent effects that deviate from an ideal capacitive behavior. From a mechanistic standpoint, (CPE)behave as systems in which the mathematical expression describing a single mechanism can be modified with a continuous parameter that accounts for system complexity.

Conclusion:

Electrochemical impedance spectroscopy has remained one of the most versatile electrochemical experimental probes, and by moving beyond the simple circuit analogy to include adsorption, diffusion limitation, and surface-effects, researchers gained a more realistic view of the system behavior. Modeling platforms such as COMSOL Multiphysics support these newer approaches, albeit all electrochemical disciplines offer a general foundation.

From extending battery lifetimes to detecting early corrosion, EIS when paired with detailed physical insights continues to unlock new possibilities for innovation and reliability in electrochemical technologies.

(This article has been adapted and modified from content on COMSOL.)

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