Feed aggregator

The XDP711-001 48-V digital hot-swap controller from Infineon offers programmable SOA current control for high-power AI servers. It provides I/O voltage monitoring with an accuracy of ≤0.4% and system input current monitoring with an accuracy of ≤0.75% across the full ADC range, enhancing fault detection and reporting.

Built on a three-block architecture, the XDP711-001 integrates high-precision telemetry, digital SOA control, and high-current gate drivers capable of driving up to eight N-channel power MOSFETs. It is designed to drive multiple MOSFETs in parallel, supporting the development of power delivery boards for 4-kW, 6-kW, and 8-kW applications.

The controller operates within an input voltage range of 7 V to 80 V and can withstand transients up to 100 V for 500 ms. It provides input power monitoring with reporting accuracy of ≤1.15% and features a high-speed PMBus interface for active monitoring.

Programmable gate shutdown for severe overcurrent protection ensures shutdown within 1 µs. With options for external FET selection, one-time programming, and customizable fault detection, warning programming, and de-glitch timers, the XDP711-001 offers flexibility for various use cases. Additionally, its analog-assisted digital mode maintains backward compatibility with legacy analog hot swap controllers.

The XDP711-001 will be available for order in mid-2025. For more information on the XPD series of protection and monitoring ICs, click here.

Infineon Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Hot-swap controller protects AI servers appeared first on EDN.

Qualcomm unveiled the Snapdragon G series, a lineup of three chips for advanced handheld, dedicated gaming devices. The G3 Gen 3, G2 Gen 2, and G1 Gen 2 SoCs support various play styles and form factors, enabling gamers to play cloud, console, Android, or PC games.

Snapdragon G3 Gen 3 is the first in the G Series to support Lumen, Unreal Engine 5’s dynamic global illumination and reflections technology, for Android handheld gaming. Gen3 Gen 3 offers 30% faster CPU performance, 28% faster graphics, and improved power efficiency over the previous generation. Wi-Fi 7 support reduces latency and boosts bandwidth.

Snapdragon G2 Gen 2 is optimized for gaming and cloud gaming at 144 frames/s, delivering 2.3x faster CPU performance and 3.8x faster GPU capabilities compared to G2 Gen 1. It also supports Wi-Fi 7 for faster, more reliable connections.

Snapdragon G1 Gen 2 targets a wider audience, supporting 1080p at 120 frames/s over Wi-Fi. Designed for cloud gaming on handheld Android devices, it boosts CPU performance by 80% and GPU performance by 25% for smooth gameplay.

Starting this quarter, OEMs like AYANEO, ONEXSUGAR, and Retroid Pocket will release devices powered by the Snapdragon G series. For more details on all three platforms, click here.

Qualcomm Technologies

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Snapdragon G chips drive next-gen handheld gaming appeared first on EDN.

Microchip’s AVR SD entry-level MCUs feature built-in functional safety mechanisms and a dedicated safety software framework. Intended for applications requiring rigorous safety assurance, they meet ASIL C and SIL 2 requirements and are developed under a TÜV Rheinland-certified functional safety management system.

Hardware safety features include a dual-core lockstep CPU, dual ADCs, ECC on all memory, an error controller, error injection, and voltage and clock monitors. These features reduce fault detection time and software complexity. The AVR SD family detects internal faults quickly and deterministically, meeting Fault Detection Time Interval (FDTI) targets as low as 1 ms to enhance reliability and prevent hazards.

Microchip’s safety framework software integrates with MCU hardware features to manage diagnostics, enabling the devices to detect errors and initiate a safe state autonomously. The AVR SD microcontrollers serve as main processors for critical tasks such as thermal runaway detection and sensor monitoring while consuming minimal power. They can also function as coprocessors, mirroring or offloading safety functions in systems with safety integrity levels up to ASIL D/SIL 3.

Prices for the AVR SD microcontrollers start at $0.93 each in lots of 5000 units, with lower pricing for higher volumes.

AVR SD product page

Microchip Technology 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post MCUs support ASIL C/SIL 2 safety appeared first on EDN.

Nexperia has expanded its GaN FET portfolio with 12 new E-mode devices, available in both low- and high-voltage options. The additions address the demand for higher efficiency and compact designs across consumer, industrial, server/computing, and telecommunications markets. Nexperia’s portfolio includes both cascode and E-mode GaN FETs, available in a wide variety of packages, providing flexibility for diverse design needs.

The new offerings include 40-V bidirectional devices (RDS(on) <12 mΩ), designed for overvoltage protection, load switching, and low-voltage applications such as battery management systems in mobile devices and laptop computers. These devices provide critical support for applications requiring efficient and reliable switching.

Also featured are 100-V and 150-V devices (RDS(on) <7 mΩ), useful for synchronous rectification in power supplies for consumer devices, DC/DC converters in datacom and telecom equipment, photovoltaic micro-inverters, Class-D audio amplifiers, and motor control systems in e-bikes, forklifts, and light electric vehicles. The release also includes 700-V devices (RDS(on) >140 mΩ) for LED drivers and power factor correction (PFC) applications, along with 650-V devices (RDS(on) >350 mΩ) suitable for AC/DC converters, where slightly higher on-resistance is acceptable for the specific application.

To learn more about Nexperia’s E-mode GaN FETs, click here.

Nexperia

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post Broad GaN FET lineup eases design headaches appeared first on EDN.

NVIDIA’s Spectrum-X and Quantum-X silicon photonics-based network switches connect millions of GPUs, scaling AI compute. They achieve up to 1.6 Tbps per port and up to 400 Tbps aggregate bandwidth. NVIDIA reports the switch platforms use 4x fewer lasers for 3.5x better power efficiency, 63x greater signal integrity, 10x higher network resiliency at scale, and 1.3x faster deployment than conventional networks.

Spectrum-X Photonics Ethernet switches support 128 ports of 800 Gbps or 512 ports of 200 Gbps, delivering 100 Tbps of total bandwidth. A high-capacity variant offers 512 ports of 800 Gbps or 2048 ports of 200 Gbps, for a total throughput of 400 Tbps.

Quantum-X Photonics InfiniBand switches provide 144 ports of 800 Gbps, achieved using 200 Gbps SerDes per port. Built-in liquid cooling keeps the onboard silicon photonics from overheating. According to NVIDIA, Quantum-X Photonics switches are 2x faster and offer 5x higher scalability for AI compute fabrics compared to the previous generation.

NVIDIA’s silicon photonics ecosystem includes collaborations with TSMC, Coherent, Corning, Foxconn, Lumentum, and SENKO to develop an integrated silicon-optics process and robust supply chain.

Quantum-X Photonics InfiniBand switches are expected to be available later this year. Spectrum-X Photonics Ethernet switches will be coming in 2026 from leading infrastructure and system vendors. Learn more about NVIDIA’s silicon photonics here.

NVIDIA

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

The post NVIDIA switches scale AI with silicon photonics appeared first on EDN.

Canadian mineral exploration company Quantum Critical Metals Corp has announced the staking of its Prophecy Germanium-Gallium-Zinc Project, a new, highly prospective critical metals property in northern British Columbia. Additionally, the firm has staked a second zinc-focused property in southern British Columbia, further expanding its strategic critical metals portfolio...

To simplify data-center design, at the Applied Power Electronics Conference (APEC 2025) in Atlanta, GA, USA (16–20 March) Dallas-based Texas Instruments Inc (TI) has introduced a new family of integrated GaN power stages in industry-standard transistor outline leadless (TOLL) packaging, , allowing designers to take advantage of the efficiency of TI’s GaN without costly and time-consuming redesigns...

submitted by /u/Patate-Furtif [link] [comments]

Frequent design idea (DI) contributor Nick Cornford recently published a synergistic pair of DIs “A pitch-linear VCO, part 1: Getting it going” and “A pitch-linear VCO, part 2: taking it further.”

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

The main theme of these articles is design techniques for audio VCOs that have an exponential (a.k.a. linear in pitch) relationship between control voltage and frequency. Great work Nick! I became particularly interested in the topic during a lively discussion (typical of editor Aalyia’s DI kitchen) in the comments section. The debate was about whether such a VCO could be built around the venerable 555 analog timer. Some said nay, others yea. I leaned toward the latter opinion and decided to try to put a schematic where my mouth was. Figure 1 is the result.

Figure 1 555 VCO discharges timing cap C1 completely to the negative rail via a Reset pulse.

The nay-sayers’ case hinged on a perceived inability of the 555 architecture to completely discharge the timing capacitor, C1 in Figure 1. They seemed to have a good argument because, in its usual mode of operation, the discharge of C1 ends when the trigger input level is crossed. This normally happens at one third of the supply rail differential and one third is a long way from zero! But it turns out the 555, despite being such an old dog, knows a different trick, it involves a very seldom used feature of this ancient chip: the reset pin 4.

The 555 datasheet says a pulse on reset will override trigger and also force discharge of C1. In Figure 1, R3 and C2 provide such a pulse when the OUT pin goes low at the end of the timing cycle. The R3C2 product ensures the pulse is long enough for the 15 Ω Ron of the Dch pin to accurately evacuate C1. 

And that’s it. Problem solved as sketched in Figure 2.

Figure 2 The VCO waveforms; reset pulses at the end of each timing cycle, and is triggered when Vc1 = Vcon, to force an adequately complete discharge of C1.

Figure 3 illustrates the resulting satisfactory log conformity (due mostly to my shameless theft of Nick’s clever resistor ratios) of the resulting 555. VCO, showing good exponential (linear in pitch) behavior over the desired two octaves of 250 to 1000 Hz.

Figure 3 Log plot of the frequency versus control voltage for the two-octave linear-in-pitch VCO. [X axis = Vcon volts (inverted), Y axis = Hz / 16 = 250 Hz to 1 kHz]

In fact, at the price of an extra resistor, it might be possible to improve linearity enough to pick up another half a volt and half an octave on both ends of the pitch range to span 177 Hz to 1410 Hz. See Figure 4 and Figure 5.

Figure 4 R4 sums ~6% of Vcon with the C1 timing ramp to get the improvement in linearity shown in Figure 5.

Figure 5 The effect of the R4 modification showing a linearity improvement. [X axis = Vcon volts (inverted), Y axis = Hz / 16]

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.

Related Content

• A pitch-linear VCO, part 1: Getting it going
• A pitch-linear VCO, part 2: taking it further
• VCO using the TL431 reference
• Ultra-low distortion oscillator, part 1: how not to do it.
• How to control your impulses—part 1
• A two transistor sine wave oscillator

The post Can a free running LMC555 VCO discharge its timing cap to zero? appeared first on EDN.

Introduction: The Convergence of AI and RF Engineering

The integration of Artificial Intelligence (AI) into Radio Frequency (RF) systems marks a paradigm shift in wireless communications. Traditional RF design relies on static, rule-based optimization, whereas AI enables dynamic, data-driven adaptation. With the rise of 5G, mmWave, satellite communications, and radar technologies, AI-driven RF solutions are crucial for maximizing spectral efficiency, improving signal integrity, and reducing energy consumption.

The Urgency for AI in RF Systems: Industry Challenges & Market Trends

The RF industry is under immense pressure to meet growing demands for higher data rates, better spectral utilization, and reduced latency. One of the key challenges is Dynamic Spectrum Management, where the increasing scarcity of available spectrum forces telecom providers to adopt intelligent allocation mechanisms. AI-powered systems can predict and allocate spectrum dynamically, ensuring optimal utilization and minimizing congestion.

Another significant challenge is Electromagnetic Interference (EMI) Mitigation. As the density of wireless devices grows, the likelihood of interference between different RF signals increases. AI can analyze vast amounts of data in real-time to predict and mitigate EMI, thus improving overall signal integrity.

Power Efficiency is another major concern, especially in battery-operated and energy-constrained applications. AI-driven power control mechanisms in RF front-ends enable systems to dynamically adjust transmission power based on network conditions, leading to significant energy savings. Additionally, Edge Processing Demands are increasing with the advent of autonomous systems that require real-time, AI-driven RF adaptation for high-speed decision-making and low-latency communications.

Advanced AI Techniques in RF System Optimization

Industry leaders like Qualcomm, Ericsson, and NVIDIA are investing heavily in AI-driven RF innovations. The following AI methodologies are transforming RF architectures:

Reinforcement Learning for Adaptive Spectrum Allocation

AI-driven Cognitive Radio Networks (CRNs) leverage Deep Reinforcement Learning (DRL) to optimize spectrum usage dynamically. By continuously learning from environmental conditions and past allocations, DRL can predict interference patterns and proactively assign spectrum in a way that maximizes efficiency. This allows for the intelligent utilization of both sub-6 GHz and mmWave bands, ensuring high data throughput while minimizing collisions and latency.

Deep Neural Networks for RF Signal Classification & Modulation Recognition

Traditional RF signal classification methods struggle in complex, noisy environments. AI-based techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs) networks enhance modulation recognition accuracy, even in fading channels. These deep learning models can also be used for RF fingerprinting, which improves security by uniquely identifying signal sources. Furthermore, AI-based anomaly detection helps identify and counteract jamming or spoofing attempts in critical communication systems.

AI-Driven Beamforming for Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) is a cornerstone technology for 5G and 6G networks. AI-driven beamforming techniques use deep reinforcement learning to dynamically adjust transmission beams, improving directional accuracy and link reliability. Additionally, unsupervised clustering methods help optimize beam selection by analyzing traffic load variations, ensuring that the best possible configuration is applied in real-time.

Generative Adversarial Networks (GANs) for RF Signal Synthesis

GANs are being explored for RF waveform synthesis, where they generate realistic signal patterns that adapt to changing environmental conditions. This capability is particularly beneficial in electronic warfare (EW) applications, where adaptive waveform generation can enhance jamming resilience. GANs are also useful for RF data augmentation, allowing AI models to be trained on synthetic RF datasets when real-world data is scarce.

AI-Enabled Digital Predistortion (DPD) for Power Amplifiers

Power amplifiers (PAs) suffer from nonlinearities that introduce spectral regrowth, degrading signal quality. AI-driven Digital Predistortion (DPD) techniques leverage neural network-based PA modeling to compensate for these distortions in real-time. Bayesian optimization is used to fine-tune DPD parameters dynamically, ensuring optimal performance under varying transmission conditions. Additionally, adaptive biasing techniques help improve PA efficiency by adjusting power consumption based on the input signal’s requirements.

Industry-Specific Applications of AI-Optimized RF Systems

The impact of AI-driven RF innovation extends across multiple high-tech industries:

Telecommunications: AI-Powered 5G & 6G Networks

AI plays a crucial role in optimizing adaptive coding and modulation (ACM) techniques, allowing for dynamic throughput adjustments based on network conditions. Additionally, AI-enhanced network slicing enables operators to allocate bandwidth efficiently, ensuring quality-of-service (QoS) for diverse applications. AI-based predictive analytics also assist in proactive interference management, allowing networks to mitigate potential disruptions before they occur.

Defense & Aerospace: Cognitive RF for Military Applications

In military communications, AI is revolutionizing RF situational awareness, enabling autonomous systems to detect and analyze threats in real-time. AI-driven electronic countermeasures (ECMs) help counteract enemy jamming techniques, ensuring robust and secure battlefield communications. Machine learning algorithms are also being deployed for predictive maintenance of radar and RF systems, reducing operational downtime and enhancing mission readiness.

Automotive & IoT: AI-Driven RF Optimization for V2X Communication

Vehicle-to-everything (V2X) communication requires reliable, low-latency RF links for applications such as autonomous driving and smart traffic management. AI-powered spectrum sharing ensures that vehicular networks can coexist efficiently with other wireless systems. Predictive congestion control algorithms allow urban IoT deployments to adapt to traffic variations dynamically, improving efficiency. Additionally, AI-driven adaptive RF front-end tuning enhances communication reliability in connected vehicles by automatically adjusting antenna parameters based on driving conditions.

Satellite Communications: AI-Enabled Adaptive Link Optimization

Satellite communication systems benefit from AI-driven link adaptation, where AI models adjust signal parameters based on atmospheric conditions such as rain fade and ionospheric disturbances. Machine learning algorithms are also being used for RF interference classification, helping satellite networks distinguish between different types of interference sources. Predictive beam hopping strategies optimize resource allocation in non-geostationary satellite constellations, improving coverage and efficiency.

The Future of AI-Optimized RF: Key Challenges and Technological Roadmap

While AI is revolutionizing RF systems, several roadblocks must be addressed. One major challenge is computational overhead, as implementing AI at the edge requires energy-efficient neuromorphic computing solutions. The lack of standardization in AI-driven RF methodologies also hinders widespread adoption, necessitating global collaboration to establish common frameworks. Furthermore, security vulnerabilities pose risks, as adversarial attacks on AI models can compromise RF system integrity.

Future Innovations

One promising area is Quantum Machine Learning for RF Signal Processing, which could enable ultra-low-latency decision-making in complex RF environments. Another key advancement is Federated Learning for Secure Distributed RF Intelligence, allowing multiple RF systems to share AI models while preserving data privacy. Additionally, AI-Optimized RF ASICs & Chipsets are expected to revolutionize real-time signal processing by embedding AI functionalities directly into hardware.

Conclusion

AI-driven RF optimization is at the forefront of wireless communication evolution, offering unparalleled efficiency, adaptability, and intelligence. Industry pioneers are integrating AI into RF design to enhance spectrum utilization, interference mitigation, and power efficiency. As AI algorithms and RF hardware continue to co-evolve, the fusion of these technologies will redefine the future of telecommunications, defense, IoT, and satellite communications.

The post Enhancing Wireless Communication with AI-Optimized RF Systems appeared first on ELE Times.

In its report ‘Automotive MicroLED Comparison 2025’ focusing on the new micro-LED-based technology emerging in the automotive sector, market research and strategy consulting company Yole Group notes that two leading LED companies ams OSRAM and Nichia have developed dedicated micro-LED solutions, enabling more than a 100-fold increase in resolution compared to existing matrix LED systems based on discrete LEDs...

Just submitted by /u/JaxTheReal_88 [link] [comments]

Tiger Group and GESemi are now accepting offers for equipment used to produce high-efficiency gallium arsenide (GaAs)-based thin-film photovoltaic (PV) cells. The fully decommissioned, ready-to-ship manufacturing assets from Ubiquity Solar of Endicott, NY, USA — including nearly 600 crates stored in South Central New York —features brands such as Aixtron Group, Attolight, GigaMat, SCHMID, Hercules and KLA Corp (KLA-Tencor)...

Photonic-crystal surface-emitting laser (PCSEL) firm Vector Photonics Ltd of Glasgow, Scotland, UK says that its chief technology officer Dr Richard Taylor has won the Sir Henry Royce Memorial Foundation Medal at the annual IET (Institution of Engineering and Technology) Achievement Awards, which aim to recognize and inspire engineering excellence...

Gallium nitride (GaN) power IC firm Navitas Semiconductor of El Segundo, CA, USA and Dublin, Ireland, says that Dr Ranbir Singh, executive VP of its GeneSiC business unit, has been inducted into the North Carolina State University’s Department of Electrical and Computer Engineering (ECE) Alumni Hall of Fame...

Commonwealth Edison Company (ComEd, a unit of Chicago-based energy provider Exelon Corp), in partnership with North Carolina State University’s FREEDM Systems Center, has been awarded a $200,000 federal research and development grant from the US Department of Energy (DOE) to help fund a $5m research project focused on improving the efficiency of, and reducing the cost of, extreme fast charging (XFC) for electric vehicles (EVs)...

Innoscience Technology of Suzhou, China has filled out its portfolio of 650V enhancement-mode (E-mode) gallium nitride high-electron-mobility transistors (GaN HEMTs) by adding devices with on-resistances (RDS(on)) of 190mΩ, 350mΩ and 600mΩ. Available in industry-standard 8x8 and 5x6 DFN packages, they join the previously announced 140mΩ, 240mΩ and 500mΩ RDS(on) parts...

Efficient Power Conversion Corp (EPC) of El Segundo, CA, USA – which makes enhancement-mode gallium nitride on silicon (eGaN) power field-effect transistors (FETs) and integrated circuits for power management applications – has expanded its selection of off-the-shelf GaN FETs in thermally enhanced QFN packages by introducing the 150V EPC2308 designed for motor drives in power tools and robots, high-density DC-DC from/to 80V–100V for industrial applications, synchronous rectification to 28V–54V for chargers, adaptors and power supplies, smartphone USB fast chargers, and in solar optimizers and micro-inverters...

Guerrilla RF Inc (GRF) of Greensboro, NC, USA – a provider of radio-frequency integrated circuits (RFICs) and monolithic microwave integrated circuits (MMICs) for wireless applications – has introduced the GRF5521, one of ten ¼W linear power amplifiers (PAs) released as part of its continued expansion into the cellular market. The indium gallium phosphide (InGaP) heterojunction bipolar transistor (HBT) amplifiers were designed for 5G/4G wireless infrastructure applications requiring exceptional native linearity over temperature extremes of –40°C to 85°C...

On 4 October, in ‘EPISTAR Corp versus Lowe’s Home Centers LLC’, the United States District Court of the Central District of California issued its post-trial decision that confirmed the jury verdict that Lowe’s Home Centers’ LED filament bulbs infringed three patents of EPISTAR of Hsinchu, Taiwan (a subsidiary of Ennostar Inc) and rejected Lowe’s arguments that challenged the validity of the EPISTAR patents...