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The code is based on the work of Johnathan Chiu which he posted here. I am using an ESP-32 with a potentiometer joystick, power is supplied trough a 18650 battery and I used a chep USB Type C charging module. I only modified Johnathan Chius code to include a part for reading from the potmeter. My experience with the remote: I built the remote itself about a year ago and since the used it a couple of times, so far without any trouble. Since I didn't add the code necesary to auto-pair the remote to the board, every time I turn on the remote I have to pair it to the board. The banana shape isn't as comfortable to hold as I thought it would be and I have to press on the deadman switch pretty hard, but it looks awesome. If you have any questions I'm glad to answear them! submitted by /u/thebananamanforever [link] [comments]

The typical regulator output network

Many voltage regulator chips, both linear and switching, use the same basic two-resistor network for output voltage programming. Figure 1 illustrates this feature in a typical switching (buck type) regulator, see R1 and R2, where:

Vout = Vsense(R1/R2 + 1) = 0.8v(11.5 + 1) = 10v

Figure 1 A typical regulator output programming network where the Vsense feedback node and values for R1 varies from type to type.

Quantitatively, the Vsense feedback node voltage varies from type to type and recommended values for R1 can vary too, but the topology doesn’t. Most conform faithfully to Figure 1. This de facto uniformity is useful if your application involves PWM control of Vout. 

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

The three-component PWM-to-regulator solution

Figure 2 shows the simple three-component solution that the above topology makes possible. Note, the PWM duty factor (DF) is from 0 to 1, where:

Vout = Vsense(R1/(R2/DF) + 1) = DF(11.5)0.8 + 0.8 = DF*9.2 + 0.8v

Figure 2 Three parts comprise a circuit for linear regulator programming with PWM.

To introduce linear PWM control to the Figure 1 regulator, all that’s required is to add three discrete components: the PWM switch Q1, and the ripple filter capacitors C1 and C2. Note that Vout will go to Vsense(C1/C2 + 1) = 10v for about 6 ms during power up while C1 and C2 are charging, but that should be okay.

The C2 capacitance required for 1 lsb (0.4%) PWM ripple attenuation is C2 = 2(N-2)/(R1*Fpwm), where N is number of PWM bits, and Fpwm is the PWM frequency (10 kHz illustrated).

Then, to avoid messing with U1’s designed loop gain, possibly reducing stability, C1 = C2*R2/R1. This capacitance ratio also provides protection for U1’s Vsense input, since it ensures that even a sudden short of Vout to ground can’t drive Vsense dangerously negative.

 This combination of time constants yields a first-order 8-bit settling time of T8 = R1C2ln(256) = 37ms. More on this lengthy number shortly.

A cool feature of this simple topology is that, unlike many other schemes for digital power supply control, only the precision of R1, R2, and the regulator’s internal voltage reference matter for regulation accuracy. Precision is therefore independent of external voltage sources, e.g., logic rails. Precision, measured as percentage of Vout, is also independent of Df, and remains equal to Vsense precision (e.g., ±1%) for all output voltages.

Speeding up the settling time

What if a 37-ms settling time is too lengthy for your application? What if you wouldn’t mind investing a couple more parts to speed it up? Figure 3 shows what.

Figure 3 Add R3 and C3 to get analog ripple subtraction, second-order filtering, and a 7-ms settling time. The symbol “*” represents a precision of 1% or better.

First disclosed in EDN Design Idea (DI), “Cancel PWM DAC ripple with analog subtraction,” a thrifty way to implement second-order PWM ripple filtering is through the analog subtraction of the AC component in the logic inverse of the PWM signal from the DC result. Figure 3 shows how that can be accomplished by simply adding R3 and C3 to the Figure 2 topology. Note that the impedance ratios of the added parts are equal to the ratio of the 5-Vpp PWM signal at Q1’s gate to the 0.8-Vpp logic complement at its drain = 5v/0.8v = 6.5.  This is why R3 = 6.5*R2 and C3 = C2/6.5.

In closing: This DI revises an earlier submission, “Three discretes suffice to interface PWM to switching regulators.” My thanks go to commenters oldrev, Ashutosh Sapre, and Val Filimonov for their helpful advice and constructive criticism. And special thanks go to editor Shaukat for her creation of an environment friendly to the DI teamwork that made this possible.

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

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The post Revisited: Three discretes suffice to interface PWM to switching regulators appeared first on EDN.

At the IEEE MTT-S International Microwave Symposium (IMS 2025) in Moscone Center, San Francisco, CA, USA (15–20 June), MACOM Technology Solutions Inc of Lowell, MA, USA is showcasing its portfolio of high-performance RF, microwave and millimeter-wave (mmWave) solutions and foundry services, with technical experts available to highlight performance advantages, plus a full lineup of technical presentations throughout the show...

Акредитація освітніх програм КПІ ім. Ігоря Сікорського 2025/05/28 kpi чт, 05/29/2025 - 14:48

Infineon Technologies AG of Munich, Germany has announced the first of a new family of radiation-hardened gallium nitride (GaN) transistors, fabricated at Infineon’s own foundry, based on its proven CoolGan technology. Designed to operate in harsh space environments, the new product is the first in-house-manufactured GaN transistor to earn the highest quality certification of reliability assigned by the US Defense Logistics Agency (DLA) to the Joint Army Navy Space (JANS) Specification MIL-PRF-19500/794...

The chip market for micro-LED display applications is rising at a compound annual growth rate (CAGR) of 93% from US$27.9m in 2024 and US$39m in 2025 to US$744.7m by 2029, forecasts TrendForce...

For decades, we heard silicon was the only answer. However, while the world’s largest fabs were busy taping out silicon, the communities of engineers and scientists working on non-silicon technologies continued pushing forward. Compound semiconductors—semiconductors made from two or more periodic table elements—include indium phosphide (InP), silicon nitride (SiN), gallium arsenide (GaAs), germanium (Ge), indium gallium arsenide (InGaAs), cadmium telluride (CdTe), gallium nitride (GaN), and silicon carbide (SiC).

Once Tesla introduced SiC MOSFETs in its EVs in 2018, SiC would no longer go unnoticed. The market has grown to more than $2.5 billion in 2024, and despite the temporary slowdown in 2025, is expected to continue growing at a staggering pace according to Yole and TrendForce.

Most EV electronics suppliers now offer SiC power ICs, creating a new ecosystem of material suppliers, capital equipment, fabless companies, foundries, and outsourced semiconductor assembly and test (OSAT) service suppliers.

Some integrated device manufacturers (IDMs)—including Bosch, Denso, Infineon, onsemi, Rohm, SanAn, STMicroelectronics, and Wolfspeed—went fully vertical starting with the SiC powder and ending with multi-die power modules.

Figure 1 SiC’s bubble size indicates its manufacturing volume and annual growth. Source: Author

Many newcomers got into the substrate business because of the high cost of the raw material. They invested heavily in mergers and acquisitions and organic growth and are now faced with the challenge of returning investment to shareholders. In this highly competitive environment, manufacturers are pushed to new levels in yield, quality, efficiency, and capacity.

Benefits are costly

SiC offers benefits to designers and consumers. Thanks to the material properties, SiC transistors can be operated at much higher voltages with lower resistance, showing less performance degradation with temperature, making SiC electronics appealing for power conversion and charging applications in vehicles and power grid applications.

However, the raw material is substantially more expensive than silicon. Crystal growth is orders of magnitude slower than silicon—its hardness, second only to diamond, makes it hard to slice, polish, and dice. High operating voltages require thick epitaxial layers that exhibit high defectivity. Next, vertical transistor architecture requires substantial wafer backside processing. All this translates to higher defectivity and lower yield with frequent yield excursions.

To the consumer, it’s higher product cost and lower reliability in the field.

Years behind silicon

“SiC is decades behind silicon,” is the common cliché among manufacturers. Here, the dominant wafer size is a good indication of material platform maturity. Historically, as silicon manufacturing matured, the industry transitioned to a larger wafer size, going through 100-, 150-, 200- and 300-millimeter (mm) wafers over the four decades, as shown in the figure below.

Figure 2 Most of the high-volume manufacturing capacity for SiCs is expected to remain on 150-mm wafers. Source: Author

Presently, SiC is made predominantly on 150-mm substrates. Meanwhile, several companies announced a transition to 200-mm substrates. While Chinese substrate supplier SICC demonstrated 300-mm substrate in 2024, use of such a large substrate is beyond the horizon. In the next several years, most of the capacity is expected to remain on 150-mm wafers.

Yes, SiC is 30 years behind silicon, judging by the substrate sizes in volume manufacturing.

Complexity of SiC circuits resembles silicon chips in the 1980s—integration into complex circuits today is at the package level rather than on a monolithic IC as seen in silicon. While the most complex silicon ICs count billions of transistors, SiC ICs are nowhere near such complexity. The reason is simple—die yield scales exponentially with the die area. At high defectivity levels, this becomes detrimental, and the only answer is going with a smaller die, integrating known good die at the package level into a more complex circuit.

However, while SiC seems decades behind silicon, it does not need decades to catch up.

The big data platform

Methodologies developed over the decades in silicon IC manufacturing are now available. One example is a solution that deploys data analytics for silicon utilized to streamline innovation. The benefits are numerous:

• Breaking the silos: The technology cycle from IC design to high-volume manufacturing is long with many players and data silos across operations. That’s where end-to-end big data platforms can connect all data end-to-end and make it available to a broad range of functions.
• Smart factory: Front-end factories are different from their predecessors. Today’s manufacturing ecosystem offers a variety of software capabilities from dozens of suppliers with well-established interoperability.
• Standardization: Thanks to several organizations—including SEMI, Global Semiconductor Alliance (GSA), and Semiconductor Industry Association (SIA)—there is a broad landscape of industry standards covering everything from equipment connectivity to data formats and specifications. Standards enable better interoperability between tools and suppliers, streamlining equipment and software deployments to support yield ramps.
• Material traceability: Whether the need is for tracing wafers in a fab or die in an assembly line, the task is complex and ranges from multiple substrate IDs and rework at different steps to substrate grading to cherry-picking. In an assembly line, it’s a challenge solved with traceability standards.
• Data models: A data model details material data, inline data from the fabs, and assembly and test data from OSATs. It describes physical entities such as equipment, wafers, dies and modules, processes including fab, assembly and test, and their relationships in the context of manufacturing flow.
• Artificial intelligence/machine learning (AI/ML): Decades ago, scientists had to develop analytical relationships between causes and effects, while software developers came up with software specifications. A myriad of data-centric frameworks and the ubiquity of AI/ML now shorten this cycle, eliminating numerous bottlenecks.
• Too much data: Vast amounts of data are generated per wafer throughout the manufacturing operations, though most of that data is never used. At the same time, engineers in the automotive segment are putting more stringent requirements on their chip and module suppliers regarding data collection and retention. The data platform must enable a good mix of storage options allowing tradeoffs between performance and cost and provide the knobs for data caching and aging.

Adopting an industry-standard solution allows manufacturers to improve efficiency and ramp yields faster than the competition.

What’s next

According to Yole, TrendForce, McKinsey and SEMI, growth is forecasted for most compound semiconductor devices, with silicon carbide at the top of that list. Following Gartner’s terminology of the “hype cycle,” it’s past disillusionment. Both silicon and GaN have been carving out more space in the power IC market. This change will push SiC for performance and cost.

At the same time, more suppliers are stepping into the market in each segment—material suppliers, foundries, fabless and IDMs. Competition will intensify, pushing manufacturers for higher yields, faster development cycles, and higher levels of integration.

Under pressure for cost and performance, designers and manufacturers must start adopting big data platforms.

Steve Zamek, director of product management at PDF Solutions Inc., is responsible for manufacturing data analytics solutions for foundries and IDMs. Prior to this, he was with KLA (former KLA-Tencor), where he led advanced technologies in imaging systems, image sensors, and advanced packaging.

Related Content

• The Road to 200-mm SiC Production
• Silicon carbide (SiC) counterviews at APEC 2024
• Wolfspeed to Build 200-mm SiC Wafer Fab in Germany
• Silicon carbide’s wafer cost conundrum and the way forward
• Reducing Costs, Improving Efficiency in SiC Wafer Production with CMP

The post Accelerating silicon carbide (SiC) manufacturing with big data platforms appeared first on EDN.

Interested in DIY a simple electronic impulse relay module? T. K. Hareendran designs an impulse relay circuit that mimics the functions of a conventional electromechanical impulse relay. This module switches the same load from several switching points. He also provides design details of different ICs that can be used in this hobby-level project. That includes pin-by-pin configurations and respective timing steps.

Read the full article at EDN’s sister publication, Planet Analog.

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The post Basic considerations for electronic impulse relay DIY appeared first on EDN.

Author: Lisa Trollo, MEMS Sensors Ecosystem and Digital Marketing Manager, STMicroelectronics

Lisa Trollo, MEMS Sensors Ecosystem and Digital Marketing Manager, STMicroelectronics

Biosensors are devices that can monitor physiological states, like heart rate or blood pressure, or detect biological parameters such as glucose levels or the presence of specific proteins in the blood.

The information biosensors collect can be used to support a medical diagnosis (for instance, a specific infection) or to provide feedback to the user on parameters of interest (for instance, the number of calories burned in a workout).

Originally developed in the 1960s for medical diagnostics, biosensors are now used by a diverse range of people – including medical patients, healthcare professionals, athletes, industrial workers, and even everyday consumers – to track their health, improve performance, and enhance safety.

Where biosensors are found

Biosensors are becoming an indispensable part of modern life. They are integrated into smartphones, smartwatches, and other wearable tech. From rings and earbuds to headsets, smart patches, and even clothing, biosensors make it easy to track health data in real-time.

In the consumer healthcare sector, wearable biosensors focus on detecting physiological signals for personalized health tracking – like monitoring athletic performance through smart watches, chest-bands or other accessories. Through these devices, they offer personalized health tracking, helping people monitor sleep quality, fitness progress, and overall wellbeing.

Biosensors are also a key component of medical devices like cardio or smart patches. They enable real-time monitoring of heart activity, glucose and various metrics, like sodium, potassium or calcium levels. These are used mainly for management of diabetes and to ensure timely medical interventions and personalized healthcare.

The science of detection: how do biosensors work?

Biosensors measure various biological levels and changes in the body, including heart rate, respiration, muscle activity, and blood oxygen levels. They use various technologies to convert these changes into electrical signals that can be used to provide real-time data for the users.

One of key characteristics of many biosensors is that they are non-invasive – meaning that they measure what is going on in the body from outside the body. This has been key for the proliferation of biosensors in consumer devices.

Biosensors take this measurement in a variety of ways.

Heart rate monitoring can be done by EKG (electrocardiogram) and augmented through a context aware analysis done with the fusion of motion signal captured by an accelerometer, and even by shining a light on the skin and collecting the reflected or transmitted wave of the light with a photodetector. Human body temperature can also be measured using infrared light to measure the temperature of the skin.

Hydration monitoring sensors, typically found in smartwatches or fitness bands, monitor hydration levels through bioimpedance or sweat analysis. In this case, biosensors aim to measure more electrolytes for single tests, which provides users with real-time analysis of their hydration and concentration levels of analytes like sodium and potassium.

It is not yet possible for all measurements to be made non-invasively. For diabetics, the current continuous glucose monitoring devices still require a small sensor wire to be inserted under the skin to measure the glucose levels in interstitial fluid. This is big improvement versus the multiple daily finger pricks needed before.

Biosensors and semiconductors

The semiconductor industry has played a crucial role in their evolution by enabling more precision, functionality, and miniaturization of biosensor devices. Advancements in biosensor technology have enabled them to be connected to IoT devices for seamless data sharing between devices; to become more sophisticated data-processors; and to be integrated into biocompatible materials to enable them to be worn close to the skin without causing discomfort – therefore enhancing the quality of data that can be captured from the human body.

Data protection and privacy

The growing use of biosensors has also raised a number of questions about issues of privacy. These devices collect vast amounts of personal data. Manufacturers are ensuring this data is encrypted, and protected by privacy laws like GDPR in Europe and HIPAA in the U.S. The future of biosensors must balance technological advancement with stringent data security to maintain user trust.

The growing biosensors market

The biosensors market is growing fast. Industry intelligence company Yole says the wearable biosensor sector has a growth rate of over 8%. While the technology wave in the 2010s featured fitness trackers and smartwatches, technology progression has advanced to so-called “hearable” devices, such as wireless earbuds, that can also track health data. Yole also expects biosensors to be used in augmented reality (AR) technology, furthering its use beyond its original application.

The future of biosensors: what’s next?

While smartwatches and fitness trackers have paved the way, upcoming innovations in hearables (earbuds that monitor health), augmented reality glasses, smart patches and smart clothing will push the boundaries of what biosensors can do. As demand for these devices increases, the focus will shift to making them more energy-efficient, secure, and even more embedded in daily life.

Expect biosensors to become an essential tool for tracking health and wellness in the years to come.

The post Proof of Life: The rapid evolution of biosensors for fitness, health, and wellness appeared first on ELE Times.

На війні загинув випускник нашого університету Фесенюк Іван Петрович kpi ср, 05/28/2025 - 23:48

Сучасне контрольно-вимірювальне обладнання від міжнародної високотехнологічної компанії kpi ср, 05/28/2025 - 23:42

Освіта нової України kpi ср, 05/28/2025 - 23:39

Last week was a biggie for those of you into tech conferences. First and foremost, of course, there was the 2025 iteration of the Silicon Valley-located Embedded Vision Summit, for which I have both personal interest and professional association. In parallel (and in Taiwan), a “little” computer conference called Computex was going on. And from a single-company-sponsored event standpoint, there were two dueling ones: Google with I/O in Mountain View, CA, which I’ll cover in my next post, and Microsoft, with Build in Seattle, WA, which I’ll detail today.

2024 launches

To begin, however, I’ll rewind two weeks further in the past. Revising my previous year’s (2024) Build coverage, you’ll note as I did at the time that this was the first time Microsoft launched new generations of the consumer-tailored versions of its various Surface family mobile computer products that exclusively leveraged Qualcomm’s Snapdragon X Arm-based SoCs.

And equally, if not more notable, last year was also the first time Microsoft added Arm-based variants to its “For Business” Surface product portfolio:

2025 launches

Fast forward to early May 2025 and, for some unknown reason, Microsoft decided to decouple its new-hardware unveilings from the main Build event, releasing the earlier announcements on May 6. Once again there were Arm-only Surface systems for consumer:

and business users:

Although this time, there weren’t any full-generation upticks. Instead, portfolio expansion and cost reduction (the latter aided by broader product line tweaks, albeit tempered by looming tariff-induced potential price increases) came to the fore. The Surface Pro is now available in both legacy 13” and new 12” form factors, while the Surface Laptop now comes in both legacy 13.8” and 15” and a new 13” size. Both newcomers are more svelte than their precursors: 0.61” versus 0.69” and 2.7 lbs. versus 2.96 lbs. for the Surface Laptop, and 0.30” vs 0.37” and 1.5 lbs. vs 1.96 lbs. (in both cases absent the optional keyboard case) for the Surface Pro.

The Surface Laptop’s hardware

I’d argue that the Surface Laptop’s form factor evolution is the more critical of the two from a competitive standpoint, an opinion which factors more generally into the fundamental reason why I’m devoting so much of today’s writeup to hardware. x86-based systems increasingly seem to me to be an afterthought for Microsoft, despite the fact that AMD and Intel have belatedly caught up with Qualcomm from a neural processor core performance standpoint and thereby gained the right to put the Copilot+ marketing moniker on systems containing their CPUs, too. Why is Microsoft becoming increasingly Arm-centric? Because I’d hypothesize, Microsoft is also becoming increasingly Apple-fixated, as the latter company’s half-decade-back announced transition from x86 to Arm-based Apple Silicon systems bears increasingly bountiful fruit.

The new 13” Surface Laptop pretty clearly has Apple’s MacBook Air in its sights, although whether it’ll actually hit its target (and if so, whether mortally or resulting only in a flesh wound) is less clear. For one thing, it’s based on the 8-core variant of the Snapdragon X Plus, versus the 10-core “Plus” and 12-core “Elite” SoCs found in the slightly larger system (that said, all Snapdragon X variants deliver the same level of NPU performance). The SSD is (slower) UFS in interface, versus NVMe, and tops out at 512 GBytes of capacity. There’s only one DRAM option offered: 16 GBytes. And although the display is only slightly smaller, its image quality specs are more notably diminished: 1920×1280 pixels at 60 Hz versus 2304×1536 pixels at 120 Hz.

The Surface Pro’s hardware

The 12” Surface Pro is similarly processor core count, mass storage capacity, and system memory size-encumbered, as is its display, albeit not as badly: IPS-based with 2196×1464 pixels at 90 Hz versus either IPS- or OLED-based 2880×1920 pixels at 120 Hz. That said, I concur with Ars Technica’s Andrew Cunningham; the return of the first few Surface Pro generations’ flimsy keyboard is baffling, especially when it had just been further reinforced with last year’s offering. Both new systems drop the proprietary Surface Connect port in favor of USB-C, curiously dispensing with MagSafe-like magnetic-connector charging capabilities in the process (I’m guessing the European Union might have had a little something to do with that decision).

Big picture, Microsoft is seemingly increasingly confident in Windows 11 Arm64’s Prism x86 code virtualization foundation’s robustness. Nobody (including me, repeatedly) was realistically saying so just a few years ago, but by focusing development attention on 64-bit- and Windows 11-only emulation, the Prism team has made tangible progress since then. I’ve got three Windows 11 Arm64-based systems here, and rarely do I encounter a glitch anymore (that said, I’m not a gamer). Further improving the situation, not only from inherent compatibility but also performance and power consumption standpoints, is the increasing prevalence of Arm64-native application variants (such as Dropbox: yay!). And the “dark cloud” of looming lawsuits between Arm and Qualcomm that I’d mentioned a year ago, thankfully, also dissipated a few months back.

AI-related announcements

Early May wasn’t all about hardware for Microsoft. The company also unveiled a raft of Copilot+-only new and expanded capabilities for Windows 11. And two weeks later, this trend extended even more broadly into Microsoft’s operating system and applications with numerous AI-related announcements at Build. Examples included:

• Web app “hooks” via the Edge browser into on-device deep learning models
• Open-source model support via another set of “hooks”, Windows AI Foundry and its local inference runtime, Windows ML

• Embrace of the Model Context Protocol (MCP), initially unveiled by Anthropic, enabling developers’ AI apps and agents to talk to other apps, web services…even Windows itself

• An AI coding agent offered by Microsoft-owned GitHub
• And even AI “actions” built into File Explorer

Microsoft also revealed a new command-line text editor, an open-source transition for the Windows Subsystem for Linux (WSL), and encryption algorithm enhancements, for example. That said, much of the rest of the keynote (at least; I wasn’t there so can’t speak to the training sessions), was rife with AI technobabble, IMHO, from both Microsoft execs and invited notable guests, complete with innumerable mentions of the “agentic web” and other trendy lingo.

Watch it yourself, or not

See below if you’re up for a slog through the entire 2 hours of oft-tedium:

Conversely, if a 15-minute summary is more to your liking, here’s The Verge’s take:

And with that, having just passed 1,000 words, I won’t force you to slog through any more of my technobabble As always, I’ll end with an invitation to share your thoughts in the comments!

—Brian Dipert is the Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

Related Content

• Microsoft’s Build 2024: Silicon and associated systems come to the fore
• Apple on Arm: How did we get here?
• Apple’s spring 2025 part II: Computers, tablets, and a new chip, too
• Microsoft’s Build 2024: Silicon and associated systems come to the fore

The post Microsoft Build 2025: Arm (and AI, of course) thrive appeared first on EDN.

Bosch Digital Fuel Twin documents the use of renewable synthetic fuels

• Digital Fuel Twin enables digital documentation and provides evidence of climate-friendly fleet operation
• The Bosch solution makes all important data on fuel properties and quantities used available via the cloud.
• Digital Fuel Twin paves the way for combustion vehicles to run on carbon-neutral fuels.

Vehicle fleets are a driver of carbon dioxide emissions, particularly for freight forwarders and transport companies. Opting to use renewable synthetic fuels can greatly reduce their carbon footprint – but documenting this, say for sustainability reports, is a challenge. That’s precisely where Bosch’s Digital Fuel Twin comes in: this software solution, integrated into the vehicle, records the use of climate-friendly fuels and documents the reduced carbon emissions. “Bosch’s Digital Fuel Twin makes it easy for companies to prove that they’re using renewable synthetic fuels,” says Thomas Pauer, the president of Bosch’s Power Solutions division. “It gives them auditable proof of the quantities and the carbon footprint of the fuel used per vehicle, which they can then use in their reporting.” In this way, companies not only comply with ever increasing reporting obligations, but can also document their environmental awareness. The Digital Fuel Twin is currently being used on the Tour d’Europe for the first time, which will also stop off at Bosch in Feuerbach on May 28. This rally to Brussels will see a fleet of cars and trucks with combustion engines refueling exclusively with renewable synthetic fuels at public filling stations as they make their way across Europe

“Bosch’s Digital Fuel Twin makes it easy for companies to prove that they’re using renewable synthetic fuels. It gives them auditable proof of the quantities and the carbon footprint of the fuel used per vehicle, which they can then use in their reporting.“
Thomas Pauer, the president of Bosch’s Power Solutions division

A further field of application for the Digital Fuel Twin would open up in the event that it becomes possible to reclassify vehicles with combustion engines as zero-emission vehicles if they use only renewable synthetic fuels. The EU intends to review this option this year. Its current plan starting in 2035 is to fine all manufacturers of combustion vehicles at such a high level as to make it no longer economically viable to sell them. “Renewable synthetic fuels should be a part of the solution. That’s the only way to achieve the climate targets in the transport sector,” Pauer says. “If the EU decides in favor of reclassification, the Digital Fuel Twin can be an important tool in implementing that.”

Purely digital records, plausibility checks, and documentation

The new Bosch software enables the reliable tracking of all a fuel’s climate-relevant properties: from production through all stages of the supply chain to the filling station and into the vehicle. To begin with, manufacturers of renewable synthetic fuels report to Bosch how much fuel they have sold, to whom, and what the fuel’s carbon footprint is. Transport companies in turn report how much fuel they purchased and when. The Digital Fuel Twin compares this data. If the time and quantity match both in the respective company books and with the recorded pump and sensor data of the transfer interfaces, the fuel properties – the type of fuel, its CO2 content, and reduction potential – are passed on in the supply chain. Any carbon emitted during further transportation is reassigned to the fuel – meaning the shorter the distances, the better for the climate. Finally, at the filling station, a “digital handshake” – an exchange of data between the filling station, vehicle, and cloud – documents exactly how much and what kind of fuel was purchased. Identification is carried out using, for example, a fleet management system. This database provides users of the Digital Fuel Twin with reliable information about the CO2 values of the fuel used as well as auditable proof of use. The fuel data is always mapped digitally as a virtual twin in a protected data room in the cloud. Bosch’s software solution can be used in cars, trucks, and buses, but also in construction vehicles and even ships.

The Digital Fuel Twin is currently undergoing testing in collaboration with many participants along the entire fuel supply chain. The system’s reliability and safety is being tested together with them and with vehicle manufacturers. To date, the Digital Fuel Twin has been retrofitted into vehicles. In the future, however, the plan is to integrate it into the vehicle’s own electronics as a pure software module, thereby ensuring the tamper-proof use of renewable synthetic fuels at the individual vehicle level. “We expect the Digital Fuel Twin to feature in production vehicles as early as 2026,” Pauer says

Renewable synthetic fuels have been available for many years

Renewable synthetic fuels are produced either from plant-based materials or with the help of renewable electricity. In contrast to fuels based on crude oil, they do not release any additional carbon dioxide into the atmosphere. Some of these fuels have been available for years. The most widely used is HVO100 (100 % recycled hydrotreated vegetable oils), which is obtained from waste oils and plant residues. Overall – taking into account the carbon emissions of the fuel itself plus the carbon emitted during its production (“well-to-wheel”) – this diesel fuel offers a CO2 advantage of up to 90 percent compared to its crude oil counterpart. Sales of this fuel have been freely permitted in Germany since 2024, but it has been available for much longer in countries such as Sweden and the Netherlands. For gasoline engines, there is also the ethanol-based fuel E85. Both fuels, HVO100 and E85, are each already available at more than 5,000 filling stations across Europe.

The post Providing seamless proof of sustainability appeared first on ELE Times.

Delta, a global leader in power management and smart green solutions, today unveiled its comprehensive solutions for the AI era with a focus on sustainability under the theme “Artificial Intelligence x Greening Intelligence.” The showcase features the newly launched AI containerized data center solution designed for edge computing. This 20-foot container, which integrates power, cooling, and IT equipment, is on display at Delta’s booth and drawing significant visitor interest. Delta is also announcing new certification for the in-rack CDU solution for NVIDIA GB200 NVL72, delivering more solutions for in-rack cooling for NVIDIA’s customers.  In response to the growing power demands of AI computing, Delta also introduces an innovative 800V High Voltage Direct Current (HVDC) power architecture solutions for AI data centers, along with a microgrid solution that enhances grid resilience. With its comprehensive developments in grid-to-chip power and thermal management solutions, Delta aims to optimize energy efficiency in the AI era and enable a sustainable AI future.

Ping Cheng, Delta’s Chairman and CEO, said, “With the rapid expansion of AI applications, industries worldwide are facing the dual challenge of meeting computing demands while maintaining sustainability. As a global leader in power and thermal management, Delta strives to enhance the energy efficiency of its products and optimize power architectures to reduce the stage of energy conversion and minimize total energy loss. For enterprise users looking to adopt AI, we also address the need for rapid and simplified deployment by offering a highly integrated containerized data center solution, including for NVIDIA GB200 NVL72. Through innovative technology, Delta is helping drive the development of sustainable AI.”

Power Solutions and Thermal Management for AI Data Centers

Benjamin Lin, President, Delta Electronics India said, “As India rapidly advances toward becoming a global technology and data hub, the demand for energy-efficient, AI-ready infrastructure is accelerating. Delta’s containerized data center and HVDC solutions represent our commitment to driving digital innovation while ensuring sustainability at scale. These next-generation technologies not only empower faster deployment and lower operational costs, but also align with India’s green data center and Digital India missions. We are proud to contribute to building a resilient digital future, where high-performance computing and clean energy solutions go hand in hand.”

As part of its HVDC solution, Delta showcases its Core Shell Liquid-Cooled Busbar and HVDC Air-Cooled Busbar, supporting up to 50VDC/8000A and 800VDC/1000A power capacity to ensure stable system operation. In advanced liquid cooling, Delta’s liquid-to-liquid cooling systems can provide up to 1,500 kW of cooling capacity. Delta also features rack-level coolant distribution units (CDUs) with cooling capacity up to 200kW, along with liquid-cooled cold plate modules designed for GPUs and CPUs, delivering robust thermal support for next-generation chips.

ICT and Energy Infrastructure Solutions for AI Data Centers

Kelvin Huang, VP and General Manager of Delta’s ICT Infrastructure Business Group, said, “In response to the high power consumption and high-density computing demands of AI servers, we showcase our AI containerized data center solution. Compared to traditional data centers, it can be deployed within weeks, significantly shortening construction time and reducing costs. It allows flexible deployment in remote areas, making it ideal for AI computing, enterprise edge nodes, and telecom facilities.”

Additionally, Delta also highlights that data centers are rapidly adopting microgrids and renewable energy solutions. With key technologies such as hydrogen energy and energy storage, Delta can integrate diverse power sources and dynamic load demands. Through intelligent energy dispatching, Delta’s solutions enable optimal energy allocation and stable power supply.

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While autonomous technology, the electric vehicle (EV) and increasing expectations of environmental sustainability and fuel economy change the nature of the automotive industry, CFD (Computational Fluid Dynamics) remains the heart of these technologies: an instrument of great power that allows an engineer to model, analyse and optimise fluid flows inside and outside of automobiles. This article discusses, in detail, the latest developments in automotive CFD, where CFD is applied, and in what new ways the scope is being stretched, and also delves on the key contribution Cadence has made toward breaking the frontiers of automotive simulation and design Cadence being the world leader in computational software.

Why CFD is Important in Automotive Design

CFD involves the numerical simulation of fluids flows by employing mathematical models and computational algorithms. In the automotive sector, CFD is used extensively in the modeling of airflow over the vehicle body, thermal management systems, engine cooling, aerodynamics, underhood airflow, HVAC etc. Being able to recreate complex fluid behaviors virtually offers a lot of advantages. It can help cut costs on building physical prototypes, speed up the design process and lead to better vehicle performance and efficiency. Plus, it can make rides more-comfortable and safe for passengers, all while shortening the development time.

Major Uses of CFD in the Automotive Sector

1. Aerodynamic Optimization

Shaping the vehicle to minimize drag and save fuel.

Reducing wind noise and lift forces for better ride quality and handling.

Optimization components like spoilers, grilles and underbody panels.

2. Thermal Management

Modeling airflow in the engine compartment to achieve best cooling.

Regulating battery temperatures for electric vehicles.

Improving cabin climate control and occupant comfort.

3. Combustion and Powertrain Simulation

This involves the modeling of air-fuel mixture and vehicles using I/C (Internal            Combustion) engines.

Simulating and analyzing lubrication and heat-dissipation in engine components.

4. EV and Battery Cooling

Cooling lithium-ion battery packs in a uniform manner to avoid further degradation and ensure safety.

Cooling inverter and electric motor loadings.

5. HVAC and Cabin Comfort

Modeling air distribution and temperature control within the cabin.

6. Water and Contaminant Management

Predicting the interaction between rain, snow or dirt with a car exterior.

Ensuring visibility to drivers and protection of sensitive components.

Technological Developments in Automotive CFD

With greater computational power comes the need for punishingly extended computations with highly detailed simulations, the domain of CFD is quickly changing with time. Few trends are:

High-Fidelity Simulation Modern solvers can tackle huge simulations with high mesh resolutions to give detailed insights into complex mechanisms such as turbulence, transient flows.

Machine Learning and AI Integration AI algorithms have been extended to predict fluid performance, optimize design parameters and automate parts of CFD workflow, thus reducing turnaround time.

Cloud Computing and HPC (High Performance Computing) Cloud-based simulation platform rendered CFD scalable and cost effective while speeding-up iterations and collaborative work among dispersed teams.

Digital Twins CFD plays a critical role in creating digital twins- virtual representations of physical systems that monitor, simulate and optimize performance dynamically.

Multiphysics and System-Level Simulation Integrating CFD with other physics disciplines such as structural, thermal and electromagnetic simulation allows for comprehensive system-level optimization.

With the automotive sector under transformation due to electrification, autonomy and sustainability, very high-fidelity fluid simulations are now in demand. Several big names like ANSYS, Altair, Cadence Design Systems have moved into leadership in computational fluid dynamics (CFD), each with a set of capabilities all their own.

How Cadence is shaping Automotive CFD

Cadence Design Systems, once considered and electronics design automation (EDA) powerhouse, now includes system-level modeling, including CFD, with acquisition of NUMECA and Pointwise. Since then, they have continuously expanded their CFD offerings into a powerful suite, disrupting how automotive engineers think fluid simulation.

Key Offerings of Cadence Automotive CFD

• Fidelity CFD Platform The Cadence proprietary CFD platform – Cadence Fidelity CFD, gives a complete and accurate solution for simulating complex fluid dynamics in automotive and aerospace systems. Included are the following capabilities:

-Unstructured and structured meshing (via Pointwise)

-Advanced turbulence models (RANS, LES DES)

-High-order numerical solvers

-Multi-domain and multi-physics simulation

-Automated workflows for design exploration

• Omnis Simulation Environment Cadence’s Omnis environment binds different simulation technologies into a single platform. It supports aerodynamics, acoustics, combustion and multiphase flows with AI-based mesh generation and user-friendly automation.
• Superior Meshing Using Pointwise Quality of meshing is vital to obtaining a good CFD solution. Pointwise offers the best meshing tools in the industry, with high-quality hexahedral and hybrid meshes to guarantee accuracy even for the most challenging automotive geometries.
• Faster Experiments with HPC and Cloud Link using Cadence CFD tools, big tests can run very fast because these tools are made to work well with HPC systems and easily use extra space in the cloud. This helps learn new things quickly while making designs.
• Works with ECAD for Electronic Cooling as cars get more electric wiring it is very important to keep parts like ECUs and power electronics from getting too hot. Cadence helps link ECAD and CFD which makes it easier to check electronic cooling.

Real-World Impacts

CFD tools provided by Cadence are being adopted by leading automotive OEMs and suppliers the world over. Some of them are:

• Reducing aerodynamic drag of next-generation electric vehicles
• Optimizing thermal management systems in battery electric buses
• Stimulating underbody airflow for race cars
• Managing cabin comfort and HVAC systems in luxury sedans

Challenges and The Road Ahead

Yet, all the advances still come with challenges:

• CFD simulations remain expensive, in terms of computing requirements and also time-consuming.
• Accurate representation of turbulence and multi-phase flows continues to be one intricate problem.
• Interdisciplinary collaboration between thermal, mechanical and electronic teams needs better integration.

However, companies like Cadence are actively working to address these gaps through Mesh refinement and solver tuning driven by AI, End-to-end automation of CFD workflows, Better user interfaces and learning tools for new users.

Conclusion:

CFD goes far beyond being just a means of simulation; it acts as a stepping stone toward innovation for the automotive industry. The whole new paradigm that CFD helps realize sits downstream of energy efficiency and safety improvements-for growing and realizing a modern-day vehicle design with the right set of parameters.

Cadence is playing a key role in CFD development through its development of next-generation Fidelity CFD, intelligent meshing, and integrated simulation environments. As the industry goes toward a more sustainable and connected form of mobility, CFD will become more inseparable, keeping Cadence ahead of the computational curve.

The post Designing the Drive: CFD as the Engine of Automotive Innovation appeared first on ELE Times.

At the IEEE’s 75th Electronic Components and Technology Conference (ECTC 2025) in Dallas, TX, USA (27–30 May), nanoelectronics research center imec of Leuven, Belgium is highlighting the performance and flexibility of its 300mm RF silicon interposer platform, which enables seamless integration of RF-to-sub-THz CMOS and III/V chiplets on a single carrier, achieving a record-low insertion loss of just 0.73dB/mm at frequencies up to 325GHz. This is said to pave the way for compact, low-loss and scalable next-generation RF and mixed-signal systems...

I've been hacking away lately, and I'm now proud to show off my newest project - The Icepi Zero! In case you don't know what an FPGA is, this phrase summarizes it perfectly: "FPGAs work like this. You don't tell them what to do, you tell them what to BE." You don't program them, but you rewrite the circuits they contain! So I've made a PCB that carries an ECP5 FPGA, and has a raspberry pi zero footprint. It also has a few improvements! Notably the 2 USB b ports are replaced with 3 USB C ports, and it has multiple LEDs. This board can output HDMI, read from a uSD, use a SDRAM and much more. I'm very proud the product of multiple weeks of work. (All the sources are at https://github.com/cheyao/icepi-zero under an open source license :D) submitted by /u/cyao12 [link] [comments]

I opened up my Eizo EV2316W and soldered two connections to the secondary stage of the internal power supply. Then, I connected a USB-C power supply and injected 15V DC — and it works! Now I can add a USB-C port and a PD trigger to power the monitor using a power bank. submitted by /u/Malsate [link] [comments]