Feed aggregator

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has introduced its fifth-technology generation, demonstrating a substantial performance leap in efficiency for next-generation 1200V and 750V automotive and industrial applications...

In 2025, Innodisk launched the “AI beyond the edge” initiative at a forum that also hosted Intel, Nvidia, and Qualcomm, which shared details of their latest developments in edge AI. But what does “AI beyond the edge” really mean?

Don Yu, special assistant to the GM at Innodisk, said that “AI beyond the edge” is about enabling systems that operate autonomously, remain connected, and scale across real-world environments. He also mentioned two complementary domains as part of this initiative.

First, industry AI—built for smart manufacturing, automation, transportation, healthcare, retail, and smart cities—enhances on-site responsiveness through real-time recognition, predictive maintenance, and intelligent workflow optimization.

Second, enterprise AI—designed for data centers, on-premise AI, and advanced models such as large language models (LLMs) and visual language models (VLMs)—supports secure, intelligent decision-making across corporate, financial, medical, and public sectors. “That allows small and mid-size businesses (SMBs) to have their own AI engines locally instead of relying on the cloud,” Yu said.

But despite all the promise, deployment of edge AI has been a challenge so far. So, how are these edge AI initiatives faring so far, EDN asked Yu. And what is Innodisk doing to overcome these challenges in effectively implementing edge AI at scale?

Edge AI deployment challenges

Innodisk chairman Randy Chien acknowledges that the exponential rise of generative AI and LLMs has fundamentally changed the design equation at the edge. More specifically, as AI workloads grow in complexity, companies are facing increasing pressure in system integration, hardware-software coordination, and the ability to scale solutions across diverse deployment environments.

“Anticipating this shift early on, Innodisk has built on its strong hardware foundation by structuring its product portfolio into modular building blocks across memory, storage, camera modules, and a wide range of embedded peripherals,” Yu said. “On this foundation, the company has positioned itself as an AI architect, combining these building blocks to meet diverse industry requirements with tailored edge AI systems.”

So, edge AI developers can implement these solutions as individual modules or as fully integrated systems, depending on their application needs. Take the example of the APEX series of edge AI systems, which brings together key building blocks, including AI accelerators, DRAM modules, flash storage, industrial MIPI and GMSL camera modules, and embedded peripherals for networking and industrial I/O.

“The platform enables flexible system configuration based on specific use cases, while supporting customization to meet diverse deployment requirements,” Yu said.

Figure 1 Individual modules are fully integrated systems tailored according to edge AI application needs. Source: Innodisk

Yu added that Innodisk is heavily investing in firmware and software development to bolster its design ecosystem. Take vision-related AI, for instance, where Innodisk provides fully ported drivers for industrial camera modules, supporting both VLMs and computer-vision applications to streamline deployment and minimize integration friction.

Innodisk also provides specialized software toolkits to accelerate system integration. For example, it has introduced IQ Studio to support the development of Qualcomm-powered edge AI systems. IQ Studio is an open-source developer portal that provides essential board support packages (BSPs), reference code, and benchmarking tools.

How modular solutions aid system integrators

These modular solutions—segmented across five layers of compute, memory, storage, sensing and connectivity, and software—are aimed at addressing design challenges before the last mile of AI deployment in vertical markets. This cohesive system-level approach addresses common development challenges for system integrators and solution providers, enabling them to focus on developing their applications rather than managing integration.

Figure 2 Modular solutions handle integration complexity, which allows system integrators to focus on developing their applications. Source: Innodisk

Moreover, there is a wide range of pre-validated solutions that significantly shorten system integration development cycles. Case in point: AI on Arm series of computer-on-modules (COMs) are designed to be deployment-ready. “They can be directly integrated into customer systems with minimal development effort,” Yu said. “Additionally, they can be paired with Innodisk carrier boards and peripherals to support different system configurations.”

Figure 3 COM modules can be paired with carrier boards and peripherals to support different system configurations. Source: Innodisk

These deployment-ready solutions provide system integrators with practical reference points and inspiration for application design when applied in real-world scenarios. Take the APEX-X200 edge AI platform, for instance, which Innodisk showcased at Nvidia GTC 2026. This on-device inference platform analyzes X-ray and CT images in real time, generating draft medical reports and clinical insights through AI-assisted healthcare workflows.

APEX-X200, powered by an Intel Core Ultra 9 processor, also integrates an Nvidia RTX PRO 6000 Blackwell Server Edition GPU with 24,064 CUDA cores and 752 Tensor cores. Furthermore, it supports up to 96 GB of industrial-grade DDR5 memory and a 1 TB PCIe Gen5 x4 NVMe SSD.

Innodisk has also developed perception systems for heavy machinery and large vehicles in collaboration with its subsidiary Aetina. It integrates the Nvidia Jetson AGX Orin platform with up to eight GMSL2 camera modules alongside capture cards and extenders that support cable lengths up to 30 meters.

Figure 4 The edge AI-based perception system facilitates surround-view stitching, blind-spot detection, and driver-monitoring functions. Source: Innodisk

These perception systems enable surround-view stitching, blind-spot detection, and driver-monitoring functions, supporting real-time environmental awareness and helping identify potential risks such as fatigue or distraction under complex operating conditions. “It’s also an example of a modular architecture that supports future system upgrades without requiring major redesign efforts,” Yu said.

Eyeing U.S. and Europe

Innodisk, headquartered in New Taipei City, Taiwan, has global ambitions with more than 1,000 field-proven edge AI deployments worldwide. In Europe and the Unites States, it’s operating in close collaboration with regional distributors and partners in edge AI segments such as industrial automation, healthcare, aviation, and professional workstations.

Innodisk considers industry events a key tool for bolstering its presence in these crucial markets. It has showcased its edge AI solutions at Nvidia GTC 2026 in the United States, ICE Barcelona in Spain, and Embedded World 2026 and CloudFest 2026 in Germany.

Next, to support global deployment requirements, the company ensures its products comply with regional regulations. Its edge AI solutions meet CE and UKCA requirements for Europe and the U.K. and FCC regulations for the United States.

Also, in Europe, where cybersecurity requirements have become increasingly mandatory, Innodisk attained IEC 62443-4-1 certification in late 2025, embedding security throughout the product development lifecycle rather than treating it as a separate feature. It’s critical because the EU Cyber Resilience Act (CRA) is expected to be fully enforced by 2027.

Related Content

• Top 10 edge AI chips
• Speak Up to Shape Next-Gen Edge AI
• Edge AI powers the next wave of industrial intelligence
• How Advanced Packaging is Unleashing Possibilities for Edge AI
• Edge AI Is Forcing a Rethink of Predictive Maintenance Architecture

The post Edge AI deployment made easy for system integrators appeared first on EDN.

Im so happy that it even works! Took me about an hour. submitted by /u/Such_Network1389 [link] [comments]

Discrete device designer and manufacturer Nexperia B.V. of Nijmegen, the Netherlands (which operates wafer fabs in Hamburg, Germany, and Hazel Grove Manchester, UK) and power electronics firm Semikron Danfoss GmbH of Nuremberg, Germany have signed a memorandum of understanding (MoU) to explore a strategic collaboration on silicon carbide (SiC)-based power modules for automotive traction inverter applications. The collaboration aims to combine Nexperia’s expertise in SiC semiconductor technology with Semikron Danfoss’ capabilities in power module packaging and integration. Together, the firms intend to evaluate how a joint approach can enable high-performance, scalable solutions for next-generation electric vehicles...

Відновлювана енергетика як одна з підвалин енергетичної стійкості України Інформація КП вт, 06/09/2026 - 15:22

Engineers can build safer, higher-performing electric vehicles and energy storage systems with TI’s new BQ79826Z-Q1 battery monitor

News highlights:

• The industry’s first 26-cells-in-series channel battery monitor delivers best-in-class sensing accuracy, reducing system costs by supporting more cells per device than competing solutions.
• Integrated smart EIS engine enables early warning of thermal runaway from inside battery cells, helping ensure safety in EVs and ESSs.
• Supports engineers to create safer, higher-performing automotive and industrial applications, the BQ79826Z-Q1 is the latest addition to TI’s portfolio of BMS devices.

Texas Instruments (TI) today introduced the industry’s highest-cell-count battery monitor with an integrated electrochemical impedance spectroscopy (EIS) engine, bringing predictive intelligence, comprehensive data, and real-time diagnostics to battery monitoring in electric vehicles (EV) and Energy Storage System (ESS) applications.

The BQ79826Z-Q1 battery monitor enhances safety and extends battery life by detecting potential failures from within battery cells. The single chip delivers the highest cell count monitoring in its class, tracking up to 44% more channels than previous generations. With this increase in channels, the device significantly decreases the number of components required in a battery pack, reducing system complexity and cost without compromising reliability.

“The electrification of transportation and the rapid expansion of energy storage are redefining what battery performance must deliver, and as a leader in battery management technology, TI is uniquely positioned to meet that challenge,” said Wenjia Liu, vice president and general manager, battery management systems (BMS) at TI.

Delivering safety and performance with EIS technology

Just as an electrocardiogram (EKG) monitors the heart, EIS monitors a battery. It delivers continuous, real-time insight that reveals the battery’s health and warns of issues before they become critical. Integrated EIS technology enables the BQ78926Z-Q1 to detect fault conditions earlier from inside the cells helping maintain safety and notifying passengers of potential vehicle hazards such as thermal runaway.

These same benefits extend to ESSs, where reliable battery monitoring is critical to meeting the growing power demands of artificial intelligence data centers. As effective storage solutions become increasingly vital in the grid-to-gate ecosystem, EIS gives engineers real-time visibility into the state of charge and state of health of each battery cell, regardless of system size.

Maximizing efficiency with industry-leading cell count

The performance of an EV or ESS is fundamentally affected by the quality and efficiency of its batteries. The BQ79826Z-Q1 supports up to 26 cells per device, eight more than any competing solution, setting a new industry standard. Fewer monitoring devices mean a lower bill of materials, simplified architecture, and reduced board space requirements, translating to meaningful cost savings per channel without sacrificing quality or reliability.

When paired with the BQ79881-Q1 pack monitor and optional TI communications bridge, these devices create a powerful chipset that works across different module sizes, battery chemistries, and mechanical designs, giving engineers the flexibility to design once and deploy everywhere. This scalability reduces engineering overhead and accelerates time to market for automotive and energy storage designers.

Calculating charge readings with the best-in-class accuracy

With a voltage accuracy of <2mV across a full temperature range of –40°C to +125°C, higher resolution analog-to-digital converters, and ultra-low noise, the BQ78926Z-Q1 enables more accurate state-of-charge calculations, directly addressing one of the biggest concerns for EV drivers: range anxiety. Utilizing EIS technology, this device enables more accurate temperature and state-of-charge estimation, helping designers achieve longer battery life and faster charging without compromising battery health. With an EIS measurement time that is five times faster than previous solutions, this device delivers the highest functional safety voltage reading per cell. Compliance with Automotive Safety Integrity Level D and International Organization for Standardization 26262 gives designers a smarter, more efficient path to safer, longer-lasting batteries.

The post TI Launches a High-Cell-Count Battery Monitor featuring EIS appeared first on ELE Times.

How to design a simpler filter (or filter-like circuit) with a varying time constant dependent on what kind of waveform is fed to it.

Discussions with some former coworkers have focused on how to design a filter or circuit with filter-like performance that has the characteristic of a slower time constant on on increasing-signal waveforms and a faster time constant on decreasing-signal ones. Such a circuit was proposed in Reference 1, which made use of the Analog Devices AD534 chip.

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

Along with the “squirming baby” example in Reference 1, another example using such a filter might be a scale at a deli counter, filtering weight as a slice or two is added to the order. When weighing is complete and the slices are removed from the scale, the reading should conversely decrease quickly.

Could there be a different, simplified circuit that might find use in accomplishing the same effect? Thus this Design Idea.

Simplification using an op amp

One way to simplify is to use the same input voltage level as the output, which precludes requiring an input isolation circuit. See Figure 1 for an example.

Figure 1 This simplified derivative-controlled low pass filter has its output at V.

Starting with the circuit in Reference 1 as a foundation, the simplified circuit requires an R1C2 combination to act as the derivative function. The input signal requires a filter, R3C1 as the filter time constant. This derivative signal should be wired to a transistor switch, Q1, a 2N2907A, which discharges that capacitor at a faster rate, R4C1. A non inverting amplifier, ¼ of an LM324N, acts to provide isolation of the derivative input to the transistor switch. This is accomplished by ensuring that the Q1 emitter to base junction is zero, therefore not conducting at steady state.

Figures 2-4 show the actual circuit being tested, and the results.

Figure 2 The circuit in this Design Idea was breadboarded and lab-tested, not just simulated.

Figure 3 In this graph of test results, the red trace is the input, with the output at C1 in blue. Note that the output is at the same level as the input, but the time constants are different.

Figure 4 Conversely, in this graph of test results, the red trace is the output and the blue trace shows the derivative action.

Further simplification

Removing the op amp is possible if the emitter to base junction is biased below the cut-in voltage. Reference 2 has an extensive discussion on the subject, based on the Shockley diode equation. The emitter base junction is the diode in question. There is a point where the forward bias current quite low, assumed to be 1% of the maximum load current. The voltage at that point is considered to be the cut-in voltage; for silicon devices it is assumed to be 0.6V.

For this application, R1 is lowered to 500Ω, which results in a 0.238V difference across the forward-biased Q1 junction, below the cut-in voltage at steady state.

Figure 5 This schematic shows a further simplification of the previous circuit.

Figure 6 In this graph of test results for the further simplified version of the circuit, the red trace is again the input, with the output at C1 in blue.

Figure 7 Conversely, in this graph of test results for the further simplified version of the circuit, the red trace shows the voltage across R1, with the blue trace referencing the C1 voltage. Note the voltage difference in this case.

Conclusion

This circuit will not work for small changes in the input voltage, a topic which is discussed in Reference 1. The values used in these circuits are arbitrary; they can be scaled based on filtering requirements.

References

1. Sheingold, Daniel H., Transducer Interfacing Handbook, Analog Devices, Inc., Norwood, MA., 1980.
2. Millman, J.; Taub, H., Pulse, Digital, and Switching Waveforms, McGraw-Hill, New York, NY., 1965.

Robert Heider is a retired engineer with over 50 years’ experience with emphasis on the design of advanced process controls and process development.

Related Content

• Simple low-pass filters tunable with a single potentiometer
• Component tolerance sensitivities of single op-amp filter sections
• Filter impedance control

The post Derivative-controlled low pass filter, simplified appeared first on EDN.

Following publication of the UK Government’s AI Hardware Plan, to address a critical gap in UK AI infrastructure the Compound Semiconductor Applications (CSA) Catapult is to transition over this summer to become the Semiconductor Catapult, developed from its existing capability to focus on R&D for energy-efficient, deployable systems to accelerate the journey from AI research to real-world deployment, for data centers and industry...

200 kva fireworks 😅 submitted by /u/No-Childhood320 [link] [comments]

The localized website reinforces DigiKey’s commitment to supporting Vietnam’s dynamic and fast-growing electronics and automation industries

DigiKey, the global distributor of electronic components and automation products, announces the launch of its regional Vietnam website. The new website is tailored to meet the escalating demand for robust supply chain solutions in Vietnam’s expanding electronics and manufacturing sectors.

Vietnam’s exports of computers, electronic products, and components reached $30.72 billion in Q1 of 2026, a 45.5% year-on-year increase, according to Vietnam Customs, underscoring the industry’s role as a leading driver of export growth. Vietnam also remained one of the world’s top mobile phone exporters, ranking third globally, making it an ideal market for DigiKey to support as it grows as a key hub for global electronics manufacturing and supply chain diversification.

“The new DigiKey Vietnam website demonstrates our commitment to supporting our partners and customers in one of Asia’s most dynamic markets,” said Dave Doherty, CEO for DigiKey. “This new platform gives Vietnamese customers access to DigiKey’s global inventory of more than 18 million products, with an emphasis on tailored, localized support and faster, frictionless digital tools. We are thrilled to empower Vietnam’s electronics industry with improved supply chain visibility and custom solutions.” 

The post DigiKey Expands Asian Electronics Industry with Launch of Vietnam Website appeared first on ELE Times.

Take two, two years later. That’s the 2026 WWDC in a nutshell, at least for developers. And for consumers? If your Apple Watch is more than a few years old, it’s headed for retirement-and-replacement.

Ironically, albeit not atypically, Apple announced no new hardware at this year’s Worldwide Developers Conference (WWDC) keynote, even though the featured image for the event’s summary press release contained an assortment of it:

And also typically (of late, at least) and as-always disappointingly, the keynote was as-usual pre-recorded.

Which was particularly disappointing in this instance, as the company’s messaging would have benefitted greatly from the presence of live demos, regardless of whether (but especially if) they went off without a hitch. Why? In 2024, Apple made big promises regarding the AI-enhanced version of its Siri virtual assistant and the broader AI-enabled capabilities of its various coming-soon operating systems and application suites.

Two years and a $250 million class action lawsuit settlement later, the company’s trying again, this time in partnership with Google (who held its own developer event just a few weeks ago). I concur with TechCrunch that the demo videos seemed more genuine this time around, with real people interacting with real devices and doing real-life-reminiscent things. Still…pre-recorded.

It’s 2009 all over again

But Apple didn’t lead with AI…sorry, Apple Intelligence…this year. Instead, it focused first on the broader nips and tucks that upcoming (and in the first three cases, already available in developer beta form) 27-series operating systems for computers (just-christened MacOS “Golden Gate”), iOS, iPadOS, watchOS, visionOS and tvOS aspire to deliver above and beyond their generational precursors. All of which takes me back nearly two decades.

At the June 2009 WWDC, Apple unveiled Mac OS 10.6 “Snow Leopard”, which the company proudly trumpeted as having “zero new features” versus its two-years-earlier Mac OS 10.5 “Leopard” predecessor. Instead, Apple focused on, quoting from the Wikipedia entry, “improved performance, greater efficiency and the reduction of its overall memory footprint.” One key means of doing so (quite effectively, in my personal experience along with broader industry reputation) was to strip out legacy PowerPC CPU support. And one year and one O/S generation later, OS X Lion 10.7 also dropped the Rosetta emulation support that had enabled legacy PowerPC-compiled applications to continue to run on top of an Intel x86-centric operating system base.

Fast forward to today and the sense of déjà vu is strong. The last clutch of Intel-based systems (two of which I ironically own, as noted in my last-year’s WWDC coverage) are no longer supported in MacOS 27. And although Rosetta 2 emulation support for x86-compiled code is still baked in, I’d wager that (again like last time) it won’t remain there for long. More generally, all the new operating system versions focused notably on performance, stability and other improvements, such as Liquid Glass U/I tweaks.

The enemy of my enemy…

I still struggle a bit to wrap my head around the partnership between Apple and Google on both AI models and cloud services (the latter alongside NVIDIA, interestingly)…but only a bit. After all, as I noted in my recent Google I/O coverage, Google’s on quite a roll right now. Apple had previously worked with OpenAI to add ChatGPT support to Siri, with limited-at-best success as far as I can tell. And OpenAI’s made no secret of its aspirations to deliver Apple-competitive hardware, going so far as to partner with former Apple design chief Sir Jony Ive.

Yes, Google (Android and derivates, including Wear OS, plus ChromeOS and the upcoming “Aluminum”) and Apple (iOS, iPadOS, watchOS, visionOS and tvOS) are market competitors, but so too are Microsoft (Windows) and Apple (MacOS). Microsoft is increasingly becoming a broad AI technology supplier in its own right. And then there’s Meta, still pushing VR, increasingly enthusiastic about smart glasses and rumored to be branching into other hardware. And Amazon, supposedly flirting with smartphones again. And…get my point?

While Apple (along with Apple fanboy sites) goes to great pains to position the Google arrangement as a partnership, I strongly suspect that in reality, Google-developed models were distilled (at most, and maybe not even that) to come up with Apple architecture-optimized versions, leveraging unique acceleration coprocessor capabilities, for example, or using data formats (and sizes of those formats) that inference-execute optimally on Apple Silicon.

Beyond that, along with (I suppose) a dedicated Siri AI app this time around, it all sorta feels like two years ago all over again, this time leveraging a robust trained-model foundation. Which isn’t a bad thing, mind you, quite the contrary. And Apple’s not unrecoverably late, mind you, although if the company had kept waffling for another year or few, I might be saying something different. It’s all just …well…meh.

Obsolescence by design strikes again

Switching to hardware (still mentioned, albeit not newly introduced), and beyond the aforementioned Intel-based computer support demise, the messaging was something of a mixed bag. The company is already beginning to feature-set differentiate between various Apple Silicon system generations, although it hasn’t (yet, at least) started culling any of them from the supported-at-all list. The same goes for iPhones.

Apple has apparently decided that in the midst of a shaky economy, telling folks that they need to go buy new iPhones isn’t a particularly wise move. Similarly, although not exactly so, many (but not all) iPads that run iPadOS 26 are upgradeable to iPad OS 27, too, including I’m happy to say the four fondleslabs in the Dipert household.

And what about smart watches? The story here is unfortunately far more ugly. Apple has apparently decided that in the midst of a shaky economy, it’s still going to be able to (or at least try to) tell lots of folks that they need to go buy new Apple Watches. Including my wife, whose first-generation Watch Ultra has just gotten knifed. I guess I now know what I’ll be buying her for her birthday in a few months…

I’ve only hit here what I thought were the high points; plenty more announcements and tidbits also got covered elsewhere. But what do you think about what I’ve focused on in this piece? As always, let me know your thoughts in the comments!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• The 2024 WWDC: AI stands for Apple Intelligence, you see…
• The 2025 WWDC: From Intel, Apple’s Nearly Free, and the New Interfaces Are…More Shiny?
• Google I/O 2026: Agentic AI gets serious
• Build 2026: Accumulating evidence of Microsoft’s AI independence

The post Apple’s question for the developer: Are you up for an AI do-over? appeared first on EDN.

Gallium nitride (GaN) power IC and silicon carbide (SiC) technology firm Navitas Semiconductor Corp of Torrance, CA, USA has launched its new UHV‑TO‑247‑4‑ISO package, setting what is claimed to be a new benchmark for high‑performance discrete power devices...

🎉 Ajax Systems запрошує студентів інженерних спеціальностей відвідати CES kpi вт, 06/09/2026 - 10:51

WIN Semiconductors Corp of Taoyuan City, Taiwan — which provides pure-play gallium arsenide (GaAs) and gallium nitride (GaN) wafer foundry services for the wireless, infrastructure and networking markets — says that its NP12-0B process has been qualified for 40V operation...

The study lays out six levers to help the sector ease execution bottlenecks, which improve delivery capacity and unlock transformation at scale.

India’s aerospace and defence (A&D) sector is entering a high-growth phase. Still, execution constraints emerge as the biggest risk to sustaining momentum, according to a new PwC India study titled ‘Accelerating aerospace and defence manufacturing through operational excellence and supply chain resilience’.

The A&D sector is ready to play a catalytic role in India’s economic transformation, helping drive the nearly 16-fold expansion in manufacturing needed to realise the country’s ambition of a $30 trillion economy by 2047. While strong demand, rising exports, and policy support have firmly positioned A&D manufacturing as a key pillar of India’s economic growth, the study highlights that large order backlogs—potentially taking up to a decade to clear—could test the sector’s ability to deliver at scale.

“For India’s aerospace and defence sector, the next phase of growth will be shaped not just by demand, but by the ability to execute with consistency, speed, and precision with scale. Companies that strengthen planning, modernise operations, and build resilient, digitally connected supply chains will be best placed to convert today’s order pipeline into timely, high-quality output at scale,” says Captain Vishal Kanwar, Aerospace Defence and Space Leader, PwC India.

Execution is the real challenge, not the demand

India’s A&D sector is at a turning point. Demand is strong. Exports are rising. But the next phase will be defined by one thing: execution at scale. India now exports defence products to nearly 100 countries. Domestic defence production reached a record ₹1.54 lakh crore in FY25. Yet, large order books are creating pressure on delivery capacity.

For major manufacturers, order backlogs are already significant:

•  1.71x to 6.88x order book-to-revenue multiples
•  2–7 years of execution backlog
•  In some segments, up to 5–10 years to clear existing orders

This points to a clear structural challenge. As Dinesh Arora, Partner and Leader, Advisory, PwC India, notes: “The real test for India’s aerospace and defence sector is no longer whether demand exists, but whether the ecosystem can execute with speed, precision, and resilience. As order books expand, companies will need to move beyond incremental capacity addition and fundamentally strengthen planning, shopfloor productivity, supplier coordination, and digital integration. Those that build these capabilities early will be better positioned to convert growth momentum into reliable, globally competitive delivery. Put simply, India has the opportunity. Now it must build the execution engine to match it.”

The blueprint to convert backlog into output

To address the widening gap between order books and execution capacity, the study outlines six priority transformation areas:

• Supply chain efficiency
• Operational excellence
• Planning and governance
• R&D acceleration
• Workforce productivity
• Digital integration (digital thread)

These transformation levers will help the sector move from backlog-led growth to execution-led scale—from stronger operations and shopfloor discipline to digital integration, fostering indigenous vendor ecosystems and supply chain resilience, and smarter use of advanced technologies. These shifts collectively enhance productivity, minimise rework, and provide manufacturers with the necessary tools to execute operations faster, more reliably, and in line with global competitive standards.

The post India’s Defence Boom Risks 10 Years Order Backlog: PwC Study appeared first on ELE Times.

Continuous health monitoring is transforming modern medicine. Instead of relying only on occasional hospital visits and laboratory tests, doctors and patients can now access real-time physiological data through advanced sensors. These technologies are broadly divided into two categories: implantable sensors placed inside the body and non-invasive wearable sensors used externally. Together, they are reshaping healthcare by enabling early disease detection, personalized treatment, and remote patient monitoring.

The Rise of Continuous Health Monitoring

Traditional healthcare systems often depend on periodic measurements such as blood pressure checks or glucose testing. However, many medical conditions change continuously throughout the day. Diseases like diabetes, heart disorders, hypertension, and respiratory illnesses require constant observation to prevent complications.

Continuous health sensors solve this problem by collecting data 24/7. Modern devices can monitor heart rate, blood glucose, oxygen saturation, body temperature, movement, respiration, and even biochemical markers in sweat or interstitial fluid. Advances in microelectronics, wireless communication, artificial intelligence (AI), and biosensor engineering have accelerated the development of these smart healthcare systems.

Implantable Health Sensors

Implantable sensors are devices inserted under the skin or within organs to monitor biological signals directly from the body. These sensors provide highly accurate and continuous data because they interact closely with tissues and body fluids.

Examples of Implantable Sensors

One of the most successful implantable technologies is the continuous glucose monitor (CGM) used for diabetes management. Devices such as implantable glucose sensors can remain under the skin for months and transmit blood sugar readings to smartphones in real time. Recent FDA-cleared systems can operate for up to one year before replacement.

Another major application is implantable cardiac monitors. These miniature devices continuously track heart rhythms and help physicians detect arrhythmias or irregular heartbeats. Modern systems are tiny, minimally invasive, and capable of remote data transmission to healthcare providers.

Researchers are also developing advanced implantable biosensors capable of measuring oxygen levels, tissue health, metabolic activity, and even neurological signals. Some experimental devices are battery-free and powered wirelessly through magnetic or inductive coupling technologies.

Illustration: Implantable Biosensor Technology

Advantages of Implantable Sensors

Implantable sensors offer several important advantages:

• High measurement accuracy due to direct contact with internal tissues
• Continuous long-term monitoring without user intervention
• Early detection of medical emergencies
• Improved disease management and personalized treatment
• Reduced hospital visits through remote monitoring

These devices are especially useful for chronic diseases that require precise data over long periods.

Challenges and Risks

Despite their advantages, implantable devices face technical and ethical challenges. Biocompatibility is critical because the body may react negatively to foreign materials. Power supply and wireless communication remain engineering challenges, particularly for miniaturized implants.

Cybersecurity is another concern. Since implantable devices transmit sensitive health data wirelessly, they may become targets for hacking or unauthorized access. Researchers are therefore developing secure communication protocols for medical implants.

Non-Invasive Wearable Sensors

Non-invasive sensors are external devices worn on the body. These include smartwatches, fitness bands, adhesive patches, smart clothing, and portable biosensors. Wearables have become extremely popular because they are convenient, affordable, and easy to use.

Modern wearable devices can measure heart rate, electrocardiograms (ECG), sleep patterns, stress levels, physical activity, oxygen saturation, and body temperature. Some advanced systems also estimate blood pressure and glucose levels using optical or electrochemical techniques.

Illustration: Wearable Health Monitoring Devices

Wearable Biosensors in Healthcare

Wearable biosensors are increasingly used in hospitals and home healthcare environments. Chest-worn biosensors can continuously monitor ECG, respiration, temperature, and motion while transmitting data to cloud platforms for medical analysis.

Smartwatches now include AI-driven health features capable of detecting irregular heart rhythms and providing health alerts. During the COVID-19 pandemic, wearable monitoring gained importance because patients could be observed remotely without frequent hospital visits.

Flexible and epidermal sensors are another exciting innovation. These ultra-thin electronic patches attach directly to the skin and can monitor sweat composition, hydration, muscle activity, and biochemical signals with minimal discomfort.

Role of Artificial Intelligence and Big Data

Artificial intelligence is becoming a central component of continuous health monitoring systems. AI algorithms analyze sensor data to identify abnormalities, predict disease risks, and provide personalized recommendations.

For example, AI can detect early signs of atrial fibrillation from smartwatch ECG data or predict dangerous glucose fluctuations before symptoms occur. Cloud computing and Internet of Things (IoT) technologies allow healthcare providers to monitor thousands of patients remotely and respond quickly during emergencies.

The integration of AI with biosensors is expected to create predictive healthcare systems where diseases are identified before they become severe.

Future Research and Innovations

The future of health sensors lies in miniaturization, flexibility, and multi-parameter monitoring. Researchers are developing implantable biosensors that can simultaneously measure multiple biochemical markers using advanced nanotechnology and microelectromechanical systems (MEMS).

Future devices may include:

• Battery-free implantable sensors
• Smart tattoos for biochemical monitoring
• Flexible electronic skin
• AI-powered diagnostic wearables
• Wireless neural implants
• Real-time personalized drug delivery systems

As technology advances, healthcare may shift from reactive treatment to proactive prevention.

Conclusion

Implantable and non-invasive continuous health sensors represent one of the most important technological revolutions in modern medicine. Implantable devices provide accurate internal monitoring, while wearable sensors offer convenient and affordable health tracking for everyday use. Together with AI, wireless communication, and biosensor research, these technologies are enabling a future of personalized, preventive, and data-driven healthcare.

Although challenges related to safety, cybersecurity, cost, and regulatory approval remain, continuous health sensors are expected to play a major role in improving global healthcare systems and patient quality of life in the coming decades.

The post Implantable and Non-Invasive Continuous Health Sensors appeared first on ELE Times.

Courtesy Texas Instruments

Why do general-purpose chips lay the foundation for technological innovations that are redefining our lives?

Do you remember when your phone was tethered to a wall? Or when a visit to the doctor was the primary way to see your health data?

Today, your phone fits in your pocket and lets you connect with anyone from almost anywhere. Wearable rings and watches offer you insight into data about your health almost instantly.

Anyone can relate to technology becoming more complex while interactions feel more effortless. Homes are becoming more responsive and automated. And intelligent vehicles are reshaping what people expect from the road. The list goes on.

Technology has silently rewritten everyday life in several ways – but how?

It starts with semiconductors.

How semiconductors enable innovation

The technology people notice first is usually the experience: the phone that lasts longer, the wearable that tracks health in real time, or the vehicle that responds intelligently.

Semiconductors are the driving force behind these electronics. Some chips, called application-specific products, are highly specialized and can integrate different functions. Others are general-purpose chips: the foundational, ubiquitous, flexible components that sometimes make up close to 90% of the ICs in an electronic system.

The two work together to help engineers optimize designs based on cost, size, availability, performance, and functionality. While general-purpose chips may not always be the most visible part of innovation, they often make innovation practical.

General-purpose chips help electronic systems sense, control, and manage power reliably. For example, engineers might use:

• Amplifiers’ signals that are converted by basic data converters and processed by microcontrollers in a smoke detector’s sensor. Clocks also provide basic timing on the board. These parts all enable the sensor to detect smoke and trigger an alarm to keep people and their belongings safe.
• Microcontrollers to manage the timing, logic, and inputs in a washing machine, helping turn a set of mechanical steps into an automatic cleaning cycle.
• Power management chips step voltages up or down inside a phone, helping each subsystem, such as the camera or display, regulate its voltage.

Why general-purpose chips are crucial

Breakthrough technology doesn’t usually start with a blank sheet of paper. It starts with a dependable foundation.

By handling essential functions such as power management, signal processing, sensing, and control, general-purpose chips free engineers to focus on what makes a design more advanced, efficient, or differentiated. Without those foundational components, development can slow, complexity can grow, and innovation can become harder to scale.

What does this look like in real life?

Imagine a data center. Have you ever thought about the millions of chips making the delivery of information feel seamless whenever you ask a large language model a question?

Inside an AI server rack, application-specific products such as AI accelerators may handle the intense parallel computations required for training and inference. But data centers also depend on a broad set of general-purpose chips, such as power management devices that control multi-stage voltage regulation, sequencing, monitoring, and protection.

Together, general-purpose and application-specific products help engineers build systems that can process massive amounts of data while balancing cost, size, power, availability, and performance at scale.

Making the power of general-purpose a reality

For engineers, the value of a general-purpose chip extends beyond the function it performs. A component used in a data center server or phone must also be available, consistent across product generations, and flexible enough to support the surrounding application-specific products.

Consider a company building several generations of connected appliances. The most visible features may change with time, but many of the foundational needs remain: managing power, reading signals, coordinating inputs, and helping the system operate reliably. When engineers can rely on a consistent set of general-purpose components across those designs, they can reduce redesign work and spend more time advancing the features customers notice.

That’s where breadth and longevity of portfolio, attentiveness to quality, manufacturing scale, and long-term consistency matter. TI’s expansive general-purpose portfolio gives designers access to widely used embedded, signal chain, and embedded parts that can support many applications, and engineers still have the flexibility to customize their selections for their needs. This breadth, combined with our continued investment in process technology, helps improve efficiency, performance, and high-quality supply over time.

Those advances can simplify development, helping engineers spend less time reworking foundational functions and more time creating electronics that are easier to scale, bring to market, and improve across product generations.

The unseen truth behind visible progress

Modern life can make extraordinary technology feel routine. Video calls across continents. Homes that sense, respond and adapt. A new generation of more sustainable and autonomous mobility. These experiences can feel seamless now, almost inevitable. But they had to start somewhere. They only exist because layers of engineering are working together with remarkable precision behind the scenes.

This is the hidden truth inside visible progress: innovation only moves forward when the fundamentals are resolved. Without general-purpose chips, development slows, complexity grows and the future takes longer to arrive.

Semiconductors don’t change the world on their own – but the world doesn’t change without them.

The post The Chips That Change The World appeared first on ELE Times.

The global electronics industry is witnessing its most significant restructuring since the rise of Asia as the world’s manufacturing hub three decades ago. What was once driven primarily by cost efficiencies and globalization is now being reshaped by geopolitics, strategic autonomy, supply chain security, and technological sovereignty. Electronics has ceased to be merely an industrial sector; it has become a geopolitical instrument.

The emergence of the “China Plus One” strategy symbolizes this transformation. Nations and corporations across the world are seeking to diversify manufacturing footprints beyond China, not necessarily to replace China, but to reduce excessive dependence on a single geography. The disruptions caused by the COVID-19 pandemic, semiconductor shortages, trade tensions, and evolving geopolitical rivalries exposed vulnerabilities in global supply chains that had long been ignored in pursuit of efficiency.

China’s dominance in electronics manufacturing remains unparalleled. From consumer electronics and telecom equipment to batteries, solar cells, and semiconductor packaging, China has built an industrial ecosystem that is difficult to replicate overnight. More importantly, it controls a substantial portion of the global rare earth value chain, including mining, refining, and processing. Rare earth elements are indispensable for electric vehicles, renewable energy systems, advanced electronics, defence platforms, and semiconductor manufacturing.

This concentration of strategic resources has become a major concern for governments worldwide. As geopolitical competition intensifies, access to critical minerals is increasingly viewed through the lens of national security. The world has learned that dependence on a single source for critical inputs can become a strategic vulnerability.

Consequently, efforts are accelerating to identify alternatives. Countries including the United States, Australia, Canada, Japan, and members of the European Union are investing heavily in alternative rare earth supply chains. Research institutions and technology companies are exploring substitute materials, recycling technologies, and rare-earth-free magnet designs. Innovations in ferrite magnets, advanced composites, nanomaterials, and material science are gradually reducing dependence on traditional rare earths in selected applications.

However, the reality remains that there is no immediate substitute for many critical rare earth elements. The challenge is not merely discovering alternatives but achieving commercial viability at scale. The future is therefore likely to be defined by a combination of diversification, recycling, strategic stockpiling, and technological innovation.

At the same time, the United States faces a different challenge. Despite being the world’s leader in semiconductor design, software, and innovation, it has struggled to maintain large-scale manufacturing competitiveness. Decades of offshoring have hollowed out portions of the manufacturing ecosystem. While initiatives such as the CHIPS Act represent a major commitment toward rebuilding domestic semiconductor capacity, establishing fabrication facilities is only one piece of the puzzle. Manufacturing excellence requires an entire ecosystem of suppliers, materials, a skilled workforce, logistics, packaging, testing, and supporting industries.

This reality underscores a fundamental lesson: manufacturing ecosystems cannot be created overnight. They evolve through sustained investments, policy consistency, talent development, and industrial clustering over decades.

Against this backdrop, India finds itself at a historic inflection point.

India’s Electronics System Design and Manufacturing (ESDM) journey has evolved remarkably over the past decade. From being largely an importer of electronic products, the country has steadily built capabilities in mobile phone manufacturing, electronics assembly, semiconductor packaging, design services, and component production. Policy initiatives such as Production Linked Incentive (PLI) schemes, semiconductor missions, design-linked incentives, and infrastructure development have begun to attract global investments.

India’s strengths extend beyond cost competitiveness. The country possesses one of the world’s largest engineering talent pools, a rapidly growing domestic market, a vibrant startup ecosystem, and increasing geopolitical trust among major global powers. As companies seek resilient and diversified supply chains, India is emerging as a credible long-term partner.

Yet, India must recognize that the opportunity presented by China Plus One is not automatic. Competing nations such as Vietnam, Thailand, Malaysia, Indonesia, and Mexico are equally determined to attract global investments. The race is not merely for assembly operations but for ownership of high-value segments including semiconductor fabrication, advanced packaging, component manufacturing, industrial electronics, defence electronics, and next-generation technologies.

The next phase of India’s ESDM evolution must therefore focus on deep manufacturing capabilities. Component ecosystems, semiconductor materials, specialty chemicals, electronic-grade gases, passive components, sensors, power electronics, and advanced manufacturing equipment need equal attention. Without these foundational layers, value addition remains limited.

The electronics industry today sits at the intersection of economics, technology, and geopolitics. Semiconductors have become strategic assets. Rare earth minerals have become instruments of influence. Supply chains have become matters of national security. Manufacturing capacity has become a measure of strategic resilience.

A new world order is emerging where technological capability will increasingly determine economic power and geopolitical influence. Nations that control critical technologies, advanced manufacturing, intellectual property, and strategic resources will shape the contours of the twenty-first century.

For India, this moment represents more than an industrial opportunity. It is an opportunity to redefine its position in the global technology landscape. The objective should not merely be to become an alternative manufacturing destination but to emerge as a technology creator, innovation hub, and trusted global electronics powerhouse.

The global electronics industry is entering an era where resilience may become as important as efficiency, strategic autonomy as important as globalization, and innovation as important as scale. In this evolving landscape, India has the potential to become one of the defining success stories of the next industrial age.

The question is no longer whether the global electronics supply chain will diversify. The question is who will lead the next chapter of that transformation.

India has a rare opportunity to ensure that it is among the leaders writing that story.

The post The New Electronics World Order: Opportunity, Risk, and India’s Moment appeared first on ELE Times.

Introduction

For more than half a century, digital electronics has relied on binary systems in which information is represented by bits existing as either 0 or 1. From microcontrollers to supercomputers, this binary architecture has powered modern civilization. However, the increasing demand for ultra-fast computation, secure communication, and advanced artificial intelligence is pushing conventional semiconductor technology toward its physical limitations. Quantum computing and quantum cryptography are emerging as revolutionary technologies capable of transforming the future of electronics engineering.

Unlike classical systems, quantum electronics operate using qubits (quantum bits), which exploit the principles of quantum mechanics such as superposition and entanglement. These properties allow quantum computers to solve highly complex problems in seconds that would require traditional supercomputers thousands of years to complete.

Understanding Qubits

A classical bit can exist only in one state at a time: either 0 or 1. In contrast, a qubit can exist simultaneously in multiple states due to the phenomenon known as superposition.

|\psi\rangle = \alpha|0\rangle + \beta|1\rangle

This quantum state representation enables parallel computation on a massive scale. Furthermore, qubits can become entangled, meaning the state of one qubit instantly affects another regardless of distance. Entanglement dramatically increases processing power and computational efficiency.

Quantum computers leverage these effects to perform operations on enormous datasets simultaneously. As a result, applications such as molecular simulation, optimization algorithms, cryptographic analysis, and machine learning become significantly faster and more efficient.

Quantum Computing Hardware Challenges

Building practical quantum computers is one of the greatest engineering challenges of the 21st century. Qubits are extremely sensitive to environmental disturbances such as heat, electromagnetic noise, and vibration. Even minimal interference can collapse the fragile quantum state, a problem known as decoherence.

To overcome this issue, engineers are developing highly specialized hardware systems, including:

• Superconducting circuits
• Trapped ion processors
• Photonic quantum systems
• Topological qubits
• Cryogenic cooling systems

Most quantum processors operate at temperatures near absolute zero using dilution refrigerators. These ultra-cold environments reduce thermal noise and help maintain quantum coherence.

Schematics of superconducting quantum computers. A). The conventional approach to manipulating and reading out of a superconducting quantum processor. Room temperature electronics are used as control units to generate analog microwave pulses with a well-defined frequency, amplitude, and phase, which are sent to the cryogenic quantum processing unit (QPU) through coaxial cables with careful attenuation and filtering. The significant hardware overhead limits the scaling of the quantum computer. B). A conceptual superconducting quantum computer that integrates the QPU with its control units at cryogenic temperatures. The control units may compose cryogenic microwave pulse generators and their control electronics. Such a monolithic integrated architecture enables large-scale superconducting quantum computers

Comparison chart between classical bits and quantum qubits

Another major challenge is achieving fault-tolerant quantum computing. Quantum systems naturally produce errors because qubits are unstable. Engineers, therefore, implement quantum error correction techniques to maintain computational accuracy. The race among technology companies and research laboratories is focused on creating scalable, stable, and fault-tolerant quantum processors.

Major organizations, including IBM, Google, Intel, and Microsoft, are investing billions of dollars into quantum hardware development.

Quantum Cryptography and Cybersecurity

While quantum computing offers immense computational power, it also threatens existing cybersecurity systems. Modern encryption methods such as RSA and ECC rely on mathematical problems that classical computers cannot solve efficiently. However, quantum algorithms such as Shor’s Algorithm could potentially break these cryptographic systems within minutes.

Quantum cryptography addresses this challenge by using the laws of quantum mechanics to secure communications. The most important application is Quantum Key Distribution (QKD), where encryption keys are transmitted using quantum particles such as photons.

The security advantage of QKD lies in the Heisenberg Uncertainty Principle. Any attempt to intercept or measure the quantum transmission changes its state, immediately alerting the communicating parties to potential eavesdropping.

Schematic of a two-node implementation of Quantum Key Distribution.
Photons are distributed using a quantum channel, usually an optical link, and detected using single-photon detectors. Parties follow a protocol allowing them to simultaneously generate identical keys at two distant locations by communicating measurement details over a data channel. Security is guaranteed by quantum physics, which predicts that an eavesdropper inadvertently produces detectable errors through her activities.

Quantum cryptography provides several advantages:

• Extremely high security
• Real-time intrusion detection
• Resistance against quantum attacks
• Secure military and financial communications

Countries and corporations worldwide are now investing heavily in quantum-secure communication networks to prepare for the post-quantum era.

Applications of Quantum Technology

Quantum technologies are expected to revolutionize multiple industries, including:

1. Healthcare and Drug Discovery

Quantum simulations can model molecular interactions accurately, accelerating pharmaceutical research and personalized medicine.

2. Artificial Intelligence

Quantum machine learning may process vast datasets faster than conventional AI systems.

3. Financial Modeling

Banks can optimize trading strategies, risk analysis, and portfolio management using quantum algorithms.

4. Logistics and Optimization

Complex optimization problems in transportation and supply chains can be solved more efficiently.

5. Defense and Space Research

Quantum sensors and secure communication systems are becoming critical for national security and satellite networks.

Future Outlook

Quantum computing remains in its early developmental stage, but progress is accelerating rapidly. Electronics engineers will play a central role in designing quantum processors, cryogenic electronics, photonic systems, RF control circuits, and quantum communication networks.

As Moore’s Law approaches its practical limit, quantum electronics may become the next major technological revolution. The transition from binary systems to quantum architectures represents not merely an upgrade in computing power, but a complete transformation in how information is processed, transmitted, and secured.

The coming decades will likely witness the integration of classical and quantum systems, creating hybrid computing platforms capable of solving problems previously considered impossible. For electronics engineers, mastering quantum technologies today could define the future of next-generation innovation.

The post Quantum Computing and Quantum Cryptography: The Future Beyond Binary Electronics appeared first on ELE Times.

Brandworks Technologies, India’s fastest growing design-driven, R&D-led electronics manufacturing powerhouse, receives official recognition for its In-House Research & Development (R&D) Unit from the Department of Scientific and Industrial Research (DSIR), under the Ministry of Science & Technology, Government of India. 

Brandwork Technologies receives an award from DSIR’s Industrial R&D Promotion Programme (IRDPP). Brandworks Technologies continues to strengthen its capabilities across electronics design, embedded systems, AI-enabled hardware, and smart manufacturing technologies. It also reflects the company’s ongoing investments in internal research, engineering infrastructure, and product innovation. The company’s strategic positioning within sectors such as AI-enabled electronics, smart devices, embedded engineering, industrial IoT, and next-generation manufacturing systems.

Commenting on the development, Ishwar Kumhar, Founder & CEO, Brandworks Technologies, said, “At Brandworks, we have always believed that strong engineering and R&D capabilities are fundamental to building globally competitive technology products. This recognition from DSIR is a significant validation of the work our teams have been doing across product development, design, and innovation. As the electronics ecosystem in India continues to evolve, we remain focused on building technologies and products that are designed, engineered, and developed in India for global markets.” 

The design and manufacturing ecosystem focuses on developing scalable and high-tech solutions. The company deals with product conceptualisation, engineering, prototyping, validation, and precision manufacturing across multiple technology categories.

The development comes at a time when Brandworks Technologies continues to expand its capabilities across product design, latest engineering, precision manufacturing, and AI-native hardware ecosystems. With advanced manufacturing infrastructure, dedicated R&D centres, and growing expertise in end-to-end electronics development, the company remains focused on contributing to India’s emergence as a global hub for advanced electronics and deeptech manufacturing.

The post Brandworks Technologies receives DSIR recognition appeared first on ELE Times.