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КПІшники отримали «зелений енергетичний Оскар» kpi вт, 04/21/2026 - 23:02

Quinas Technology Ltd of London, UK (which was spun off from Lancaster University in early 2023, and is developing ULTRARAM non-volatile memory technology) has announced a milestone in its R&D program, confirming the use of atomic layer etching (ALE) to fabricate and refine its quantum-engineered device structures at Saudi Arabia’s King Abdullah University of Science and Technology (KAUST) Core Labs (a system of multi-disciplinary and interconnected research laboratories)...

This little project is the mainboard for a noise activated roller-blind and the circuit incorporates 31 through-hole components and 20 SMD components. Now time to begin testing! PS: Please excuse the soldering submitted by /u/hjw5774 [link] [comments]

Проєктна аспірантура в КПІ ім. Ігоря Сікорського для вступників 2026 року kpi вт, 04/21/2026 - 18:35

Power semiconductor maker ROHM Semiconductor of Kyoto, Japan has developed the latest device of its EcoSiC series: the 5th Generation silicon carbide (SiC) MOSFETs optimized for high-efficiency power applications. The technology is suitable for automotive electric powertrain systems – such as traction inverters for electric vehicles (xEVs) – as well as power supplies for AI servers and industrial equipment such as data centers...

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

I have to brag, but first I have to tell on myself a little bit. You should really read the datasheet more thoroughly than I did. On the DRV8304 gate driver in 3PWM mode, the INLx pins need to be connected and pulled high for the phases to be turned on. If the pins are left unconnected or pulled low, the driver will put the phases into coast mode (all MOSFETs disabled). Also DVDD is an output pin, so don't connect it to 3V3. In this image you can see where I cut the trace from 3v3 to DVDD (between the C and the 5 of the C5 reference). Happily, I was able to scrape some soldermask off the trace before the cut, and then bodged some 34AWG magnet wire onto it and connected it to the INL pins to pull them high. After this (and some fixes to a couple of failed joints on other pins) the device was showing correct outputs on the phases. This is the first time I've ever bodged a PCB so I'm really excited I was able to get it working. This is just a test board for a more complete project I'm going to do down the line, so i'm not too worried about the longevity of this fix. But it's good to have this skill in the toolkit. submitted by /u/Direct_Rabbit_5389 [link] [comments]

It seems any expert who can spell “AI” has an opinion on its potential impact. There are countless predictions out there, many made with precision and confidence, and they are often contradictory.

Depending on who you listen to, AI will cause widespread disruption and unemployment, especially at starting and lower middle-levels, open up new vistas and ways of working and getting things done, resulting in the need to hardly do any work, or make us all work harder to stay in place…you get the picture. Whatever answer you want, you can find someone who has provided it.

I’ll jump in and give you my prediction on the impact of AI, with a two-part answer. First, I don’t know, and second, neither does anyone else.

If you look back at the track record of predictions about how past technical advances would unfold, one thing is clear: Most of these prediction underestimate or overestimate the reality, and most of them miss the actual nature of the change that these advances spur.

AI and analog: Round 1

Initially, I thought of doing a “thought experiment” about analog design and AI and go beyond the issues of general analog considerations. But then it made more sense to look at some of the specific stages of analog design, from ICs to circuits and systems, and all the way to final documentation.

However, I realized soon that it was a swamp. There were so many perspectives, so many considerations, and so many exceptions that it would take a lengthy treatise rather than a modest blog to begin to highlight the possibilities. The only meaningful possibility I could think of was using AI to help a beleaguered designer doing “best” component selection.

For example, this might be the task of choosing an op amp that fits the application priorities from among the dozens of vendors and thousands of models. Going further, AI might even help with some trade-off decisions (“show me an op amp that has 10% more dissipation than my stated maximum, if it gives me a 20% improvement in noise”).

AI and analog: Round 2

I then asked myself if it would make sense to instead look at AI and analog from the opposite direction: how can AI help analog-centric systems—meaning those with real-world front-end sensors—do a better job or perhaps implement innovative architectures.

My question was answered when I came across a project from researchers at the University of California, Davis. They used a different approach to miniaturization of a spectrometer that reduced its size to the scale of a grain of sand. This compact spectrometer-on-a-chip is designed for integration into portable devices. Instead of separating light into a spectrum physically, the system relies on computational reconstruction.

Conventional spectrometers rely on dispersive elements such as diffraction gratings or prisms to spatially separate light into its constituent wavelengths. But it requires long path lengths and bulky designs to separate individual wavelengths. The need to spatially disperse the light makes it challenging to miniaturize these delicate and expensive systems, making them unsuitable for portable applications.

On the contrary, the so-called reconstructive spectrometers use a unique set of numerous but compact photoresponsive detectors to directly encode the complex spectral information, which is later extracted using advanced computational algorithms. The team leveraged recent advances in machine learning and computational power, thus enabling further miniaturization toward chip-scale design with reduced manufacturing cost (Figure 1).

Figure 1 Working mechanisms of spectrometers include conventional spectrometers with uniform detector arrays that disperse the light spatially using diffraction gratings, which require long path lengths owing to their bulky nature (a). Then there are reconstructive spectrometers that utilize unique photodetectors capturing the minute variations in the incident light spectrum. The spectral information is then reconstructed using machine learning algorithms (b).

The chip replaces traditional optics with an array comprising 16 silicon detectors, each tuned to respond slightly differently to incoming light. Together, these detectors capture overlapping signals that encode the original spectrum, and they can provide wider bandwidth due to the use of staggered, tailored sensors for each spectrum slice. This process is similar to having multiple sensors that sample different elements of a complex signal, with the full picture emerging only after full analysis.

The analysis is performed using AI where the spectral reconstruction of an unknown spectrum is what has been defined as an inverse problem. The spectral reconstruction of the photon-trapping structures of the spectrometer is performed using a fully connected neural network that solves the inverse problem; an outline of the training and reconstruction process is shown in Figure 2.

Figure 2 Neural network model for spectral reconstruction shows demonstration of the training and reconstruction process of the neural network (a). Training and validation losses plotted against epoch show convergence of the model (b). The model is trained for 2,000 epochs with the loss function converging around 0.03, where comparison of spectral reconstruction uses matrix pseudo-inversion (c), linear combination of Gaussian functions (d), and neural network model (e).

The neural network model outperforms the other two methods in reconstructing the spectral profile of a 3-nm full width at half maximum (FWHM) laser peak. The root-mean-square error (RMSE) and Pearson’s R value (a correlation coefficient) for the neural network model are 0.046 and 0.87, respectively, indicating high accuracy in spectral reconstruction.

The training process involves learning the complex spectral encoding between the photocurrent of photon-trapping structure-enhanced photodetectors and their corresponding spectral information by back-propagating the loss function.

Their detailed modeling, analysis, and experimental results also demonstrated that this approach provided superior noise tolerance compared to traditional spectrometers despite the low photon intensity and small capture area. The fascinating story is presented in a highly readable paper “AI-augmented photon-trapping spectrometer-on-a-chip on silicon platform with extended near-infrared sensitivity” published in Advanced Photonics.

I’ll be honest: When I first saw this paper, my first if somewhat cynical thought was that this was just an attempt to dress up an old analog signal-chain technique with an AI “glow.” There are two basic ways to implement a precision sensor-based path. First, use top-grade components and various circuit topologies such as matched resistors to cancel errors to the extent possible. Second, use lesser components and just calibrate out inaccuracies.

But as I continued to read their paper, I saw that the neural network method added a new level of sophistication and ability to work through inherent weakness in the design and components to deliver an impressive result.

Where do you see AI helping, if at all, in the design cycle of an analog circuit or system? Allowing new topologies for sensor-based systems that were previously not viable or practical.

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• Achieving analog precision via components and design, or just trim and go

The post What’s the impact of AI on analog design appeared first on EDN.

AXT Inc of Fremont, CA, USA — which makes gallium arsenide (GaAs), indium phosphide (InP) and germanium (Ge) substrates and raw materials at plants in China — has priced its underwritten public offering of 8,560,311 shares of common stock (announced on 20 April) at $64.25 per share...

This design based on an SR latch and two RC networks is, unlike many alternative solutions, neither complex nor expensive.

Single and differential capacitance sensors are widely used to measure linear and angle displacement, pressure, proximity, humidity, fluid level, inclination and acceleration. Both analog and digital circuits are used to interface the sensors (References 1-4). Some of the solutions tend to be complex and expensive (References 5-9).

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

This Design Idea presents a very simple circuit to interface differential capacitance sensors (Figure 1). It is a relaxation oscillator made of an SR latch and two RC networks. When one of the capacitors is gradually charged through the corresponding resistor, the other capacitor is quickly discharged through a parallel switch. When the charging capacitor reaches the trip voltage VT of its gate, the latch changes its state. The other capacitor starts charging and the first one is quickly discharged. When the second charging capacitor reaches the trip level VT of its gate, the latch flips again returning to the initial state. The charge-discharge process repeats over and over again.

Figure 1 The sensor becomes part of a relaxation oscillator where one of the capacitors is charging when the other one is shorted; the two capacitors periodically swap their operation.

Signal VQ1 goes to a microcontroller, which measures time intervals t1 and t2 and calculates the average value VAVR = VDD * t1 / (t1 + t2). A number needs to be subtracted from this value so when the two capacitors are equal the average value is zero. Thus, the average value will be positive when C1 > C2 and negative when C1 < C2.

Circuit operation was tested with a bank of ten 50-pF capacitors. The left side of Figure 2 shows connections to set a duty cycle of 20%; the right side of the figure sets the duty cycle of 90%.

Figure 2 Sensor operation is simulated with a bank of 10 capacitors.

Figure 3 presents how period T and duty cycle D = t1 / T depend on the value of C1. Period barely changes between 96 and 98 µs, while the duty cycle is proportional to C1. A straight line fits perfectly the duty cycle data (the R2 factor equals 1); however, as Figure 4 shows, the line has a nonlinearity error of ±0.3%.

Figure 3 Circuit responses: at the top, the period is almost the same, below it, the duty cycle depends linearly on the value of C1.

Figure 4 The duty cycle response has a nonlinearity error of ±0.3 %.

The bump shape of the error graph means that a second-order polynomial may improve linearity. Indeed, equation y = 1*10-5 * x2 + 0.182 * x + 4.21 reduces the error down to ±0.1%. Such an equation is easy to implement in the microcontroller firmware.

Jordan Dimitrov is an electrical engineer & PhD with 40 years of experience. Currently, he teaches electrical and electronics courses at a Toronto community college.

Related Content

• From gap to signal: Non-contact capacitive displacement sensors
• Fundamentals in motion: Accelerometers demystified
• An introduction to capacitive sensing

References

1. Regtien P., E. Dertien. Sensors for mechatronics. 2nd ed., Ch. 5, Elsevier, 2018.
2. Northrop R. B. Introduction to instrumentation and measurement. 3rd ed., CRC Press, 2014.
3. Baxter L. Capacitive sensors. http://www.capsense.com/capsense-wp.pdf
4. Differential capacitance pressure sensor circuit. https://instrumentationtools.com/differential-capacitance-pressure-sensor-circuit/
5. Reverter F., O. Casas. Direct interface circuit for differential capacitive sensors. I2MTC 2008 – IEEE International Instrumentation and Measurement Technology Conference, Victoria, Vancouver Island, Canada, May 12-15, 2008.
6. Barile G. et al. Linear integrated interface for automatic differential capacitive sensing. Proceedings 2017, 1, 592.
7. Ferri G. et al. Automatic bridge-based interface for differential capacitive full sensing. 30th Eurosensors Conference, EUROSENSORS 2016. Procedia Engineering 168 (2016) 1585 – 1588.
8. Bai Y. et al. Absolute position sensing based on a robust differential capacitive sensor with a grounded shield window. Sensors (Basel). 2016 May; 16(5): 680. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883371/
9. De Marcellis A., C. Reig, M. Cubells-Beltrán. A capacitance-to-time converter-based electronic interface for differential capacitive sensors. MDPI Electronics, Jan 2019.

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Munich, April 21, 2026 — The power electronics market is being driven by stricter efficiency targets, higher power densities, and increasing integration with large-scale power grids. Consequently, engineers must cope with non-ideal component behavior, fast transient stresses on wide-bandgap devices, and ever more demanding EMC requirements. The conference will address these challenges by presenting measurement-centric solutions that can be implemented with modern oscilloscopes, vector network analyzers, and precision power analyzers.

The program opens on May 5 with a keynote by Tobias Keller (Hitachi Energy) entitled “Power Semiconductors: Shaping the Future Power Grid – Performance and Reliability for Future Decades”. Tobias Keller will discuss the qualification of silicon and silicon carbide (SiC) devices for high-voltage grid applications, focusing on thermal cycling, short-circuit robustness, and long-term reliability data.

A second keynote, delivered on May 6 by Veit Hellwig (Infineon Technologies), will examine the impact of gallium-nitride (GaN) technology on high-voltage motor inverter topologies.

In addition to the keynotes, the conference comprises a series of technical sessions. One presentation will analyze passive component characterization, highlighting methods for extracting parasitic inductance and capacitance at frequencies above 100 MHz and demonstrating the influence of these non-idealities on converter stability. Another session will detail automated dynamic characterization of SiC and GaN power devices, showing how double-pulse test rigs can be synchronized with high-speed digitizers to reduce measurement uncertainty and to capture fast recovery behavior.

Electromagnetic compatibility topics are covered in two dedicated talks. The first provides practical guidance on the use of near-field probes for pinpointing radiated emission sources and for validating the effectiveness of EMI filter designs. The second demonstrates a complete conducted emission measurement workflow on a small-scale prototype, using a Line Impedance Stabilization Network (LISN) together with a modern mixed signal oscilloscope. The presenter will also outline a filter design methodology that exploits the time-frequency capabilities of the instrument.

A further webinar addresses the growing need for accurate efficiency measurement in data center and AI server power supplies. By employing precision power analyzers capable of tracking distorted waveforms and rapid load transients, participants will learn how to obtain true input and output power values that satisfy 80 PLUS certification requirements.

The last session focuses on harmonic current and voltage flicker compliance for low-voltage, grid-connected products. The speaker will review the limits and test procedures defined in IEC/EN 61000-3-2/-3-3 and IEC/EN 61000-3-12/-3-11, and will demonstrate how integrated compliance testing software linked to a power analyzer can deliver automated pass/fail decisions from early prototype evaluation through to final type approval.

Speakers include subject matter experts from Rohde & Schwarz, Hitachi, Infineon, PE-Systems, Würth Elektronik, and the Universities of Bremen and Zaragoza. Their contributions combine academic insight with industrial experience, providing attendees with both theoretical background and hands-on measurement strategies.

The conference is free of charge, but registration is required. The full agenda, speaker biographies and the registration portal are available at: http://www.rohde-schwarz.com/power-electronics-conference

The post Rohde & Schwarz to host Power Electronics Online Conference “From Design to Validation” in May appeared first on ELE Times.

• Introducing the ST64UWB family: the first fully integrated ultra-wideband (UWB) solution supporting IEEE 802.15.4z and upcoming IEEE 802.15.4ab UWB standard with multi-millisecond ranging (MMS), including narrow-band assistance radio (NBA)
• ST64UWB family delivers industry-leading RF performance leveraging ST’s 18 nm FD-SOI technology
• Best-in-class performance enables new use cases and enhances user experience for automotive, smart home, and smart building applications

STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, introduces an ultra-wideband (UWB) chip family that comprehensively supports the next-generation wireless standard for localizing and tracking devices at distances up to several hundred meters. This UWB chip family combines extended range with greater processing power and robustness to enable new and improved automotive, consumer, and industrial use cases, including secure digital access control, presence and motion sensing, and precise approach detection.

“The ST64UWB family we announce today is an industry-first system-on-chip supporting the latest ultra-wideband specification, IEEE 802.15.4ab, including narrow-band assistance radio, with ultra-precise ranging and sensing,” said Rias Al-Kadi, General Manager, Ranging and Connectivity Division, STMicroelectronics. “These chips are tailored for automotive, consumer, and industrial applications, providing innovators with a powerful platform for the next wave of ultra-wideband use cases.”

The emerging standard builds on the IEEE 802.15.4z UWB wireless technology in today’s hands-free digital car keys that unlock a vehicle on approach. New technical enhancements enabled by multi-millisecond ranging (MMS) and narrowband assistance (NBA) extend operating range, strengthen connections with devices carried in bags or rear pockets, and enable direction finding at close range to better interpret user intent. IEEE 802.15.4ab also enhances radar mode, improving use cases such as child presence detection (CPD) in vehicles, a potentially life-saving feature recommended by Euro-NCAP, the independent vehicle safety assessment organization.

The devices are now sampling to major Tier 1s and original equipment manufacturers.

Why IEEE 802.15.4ab and ST64UWB matter

“IEEE 802.15.4ab is set to become the backbone of next-generation ultra-wideband,” said Andrew Zignani, Senior Research Director at ABI Research. “By 2030, we expect the vast majority of ultra-wideband-equipped vehicles to migrate to this new standard, leveraging a rapidly growing installed base of hundreds of millions of compatible smartphones. Meanwhile, backward compatibility with IEEE 802.15.4z allows the industry to adopt these enhancements quickly without disrupting existing deployments, while enabling valuable new user experiences and services across multiple end markets.”

“IEEE 802.15.4ab is the foundation for enabling a new generation of key fobs as part of a digital key system,” said Daniel Siekmann, Head of Car Access HW D&D Team, Forvia Hella. “It offers more than eight times the range of 802.15.4z and significantly better non-line-of-sight performance, which allows for key fob functionality to reliably perform from a back-pocket or inside a bag. With backward compatibility to 802.15.4z, it provides a practical path to replace legacy HF/LF key fobs with a modern ultra-wideband-based architecture, a transition that is further enabled by STMicroelectronics’ new ST64UWB chips.”

“By adopting 802.15.4ab, car access systems can simultaneously improve performance, cost efficiency, and robustness. The more than eightfold increase in range effectively mitigates back-pocket and other obstructed-signal conditions. At the same time, backward compatibility with 802.15.4z gives OEMs like LGIT the flexibility to either enhance reliability using their existing fixed reference points or reduce the number of reference points to lower overall system cost,” said William Jung, Team Leader, LG Innotek.

“With IEEE 802.15.4ab, the ability to drastically increase UWB performance, especially when the smartphone is left in the rear pocket, is highly appreciated,” said Bernd Bär, Expert Product Line Technology, Marquardt. “At the same time, operating within tight global homologation limits while remaining backward compatible with existing IEEE 802.15.4z ecosystems tremendously extends the applicability of UWB systems.”

“Over the last decade, Nuki has helped establish and shape the smart lock category in Europe. We firmly believe Ultra-Wideband is a transformative technology for precise, hands-free unlocking,” said Jürgen Pansi, Chief Innovation Officer, Nuki Home Solutions. “Together with STMicroelectronics and their ST64UWB solution, we are showcasing how the IEEE 802.15.4ab standard can bring the power of Aliro and UWB to our region.”

Further information for editors

The three SoCs introduced today (ST64UWB-A100, ST64UWB-A500, and ST64UWB-C100) are built on 18 nm FD-SOI process that boosts link budget by nearly 3dB versus standard bulk technologies, extending range by roughly 50% beyond the gains already delivered by the IEEE 802.15.4ab standard.

The ST64UWB-A series, designed for automotive applications and starting with the ST64UWB-A100 for use cases such as digital key and precise vehicle localization, features an Arm® Cortex®-M85 core and supports ASIL A(B) automotive safety concept. The ST64UWB-A500 adds AI acceleration and digital signal processing to support edge AI-powered radar applications, including child presence detection (CPD), kick sensing, and outward-facing use cases, such as parking sensors and radar-based vehicle-sentinel mode. These radar capabilities benefit from the new 15.4ab Kaiser pulse shape and the upgraded 1.3 GHz bandwidth of UWB channel 11, resulting in twice the accuracy compared to 500 MHz channels.

The ST64UWB-C100, built on an Arm Cortex-M85 core, targets commercial and consumer applications, delivering best-in-class hands-free and tap-free user experiences with full Aliro standard compatibility.

ST is accelerating next-generation UWB adoption with a comprehensive development kit including a UWB stack (PHY/MAC), a radar toolbox, development boards, a reference design for antennas, and application examples for both automotive and consumer markets. Find out more on product specification and 802.15.4ab technology at www.st.com/uwb

About STMicroelectronics

At ST, we are 48,000 creators and makers of semiconductor technologies, mastering the semiconductor supply chain with state-of-the-art manufacturing facilities. An integrated device manufacturer, we work with more than 200,000 customers and thousands of partners to design and build products, solutions, and ecosystems that address their challenges and opportunities, and the need to support a more sustainable world. Our technologies enable smarter mobility, more efficient power and energy management, and the wide-scale deployment of cloud-connected autonomous things. We are on track to be carbon neutral in all direct and indirect emissions (scopes 1 and 2), product transportation, business travel, and employee commuting emissions (our scope 3 focus), and to achieve our 100% renewable electricity sourcing goal by the end of 2027. Further information can be found at www.st.com

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The latest annual report from CSconnected confirms that the compound semiconductor cluster in South Wales continues to expand its economic contribution, now supporting £436m of gross value-added (GVA) and 3140 jobs across Wales...

Григорій Синиця: "Інтереси України мають бути понад усе" Інформація КП вт, 04/21/2026 - 13:00

AXT Inc of Fremont, CA, USA — which makes gallium arsenide (GaAs), indium phosphide (InP) and germanium (Ge) substrates and raw materials at plants in China — intends to offer and sell shares of common stock in a public offering. In connection with the offering, the firm also expects to grant the underwriters a 30-day overallotment option to purchase up to an additional 15% of the shares offered in the public offering price, minus the underwriting discounts...

Ordered a SOT323 diode instead of a SOD323, worked out in the end. Just had to make sure not to let pin 2 touch the exposed ground plane submitted by /u/circuitsable [link] [comments]

InPHRED Inc of Boston, MA, USA (a developer of next-generation photonics solutions for consumer sensing and digital health that was founded in 2023 at Yale University) has announced its formal expansion into the data-center optical interconnect market, extending its nanoporous platform into high-speed connectivity solutions for next-generation AI infrastructure...

I made this little piano using an ATtiny85 and a some push buttons. All 12 keys are read through a single ADC pin using a resistor-ladder voltage divider. Each button taps a different point in the chain, so the voltage tells the chip which key is down. Functional but quite limited as only one key really works at a time. This was my first project to learn the ATtiny85 and I'm happy with how it turned out. Sounds pretty rough though. submitted by /u/RAZK0M [link] [comments]

submitted by /u/ruumoo [link] [comments]