Новини світу мікро- та наноелектроніки

electronica China 2025 will take place from April 15 to 17, 2025 at the Shanghai New International Expo Centre (SNIEC), in halls W3-W5 and N1-N5. It is expected to attract a total of almost 1,800 high-quality exhibitors from domestic and international markets covering 100,000 square meters. Register now and check out the latest updates on the exhibition area!

Click to register now: https://ec.global-eservice.com/?lang=en&channel=enmd

Fairgrounds Map

Overview some important exhibition areas

Semiconductors & Sensors

In recent years, the rapid development of the Internet of Things (IoT) industry in China has positioned sensors as one of the core components of the country’s “Strong Foundation Project”. As a result, the market scale and application scenarios for sensors in China have continued to grow. electronica China 2025 will include exhibition areas for semiconductors and sensors, gathering numerous high-quality exhibitors to discuss industry development on-site.

Test and Measurement & Power Supplies

The power supply industry in China has seen rapid growth fueled by international industrial relocation, the ongoing development of China’s information technology, and the continuous advancements in the defense and military sectors. The 2025 exhibition will feature bring together companies to showcase their products and participate in technological discussions in the test and measurement, and power supply exhibition areas.

Connectors, Switches and Cable Harness

In recent years, the connector market has experienced overall growth, fueled by the rapid development of downstream industries such as consumer electronics, new energy vehicles, communications, and industrial control. electronica China 2025 will feature a connector exhibition area, inviting numerous leading exhibitors from the connector industry to showcase their new products.

PCB & EMS

With the maturation of the EMS industry model and ongoing technological advancements and capacity upgrades by companies, the global EMS market is experiencing greater diversity in its downstream customer sectors. At present, EMS is extensively applied across a range of sectors, including consumer electronics, automotive electronics, industrial control electronics, etc. electronica China 2025 will set up PCB and EMS exhibition areas.

Passive Components & Distributors

The development of technologies like 5G communication, artificial intelligence (AI), and the IoT is presenting substantial opportunities for the passive components industry. Additionally, passive components are widely used in such fields as telecommunications, power, automotive electronics, and healthcare, providing substantial growth opportunities for the industry. electronica China 2025 will set up passive components and distributors exhibition areas.

Click to register now: https://ec.global-eservice.com/?lang=en&channel=enmd

Learn more about electronica China: https://www.electronicachina.com.cn/en

The post electronica China 2025: High-quality Chinese and International Companies Gather at this Can’t-miss Electronics Event with 100,000 Square Meters appeared first on ELE Times.

I'm in the process of stripping this old scoring machine down to replace the insides with an Arduino. I think this machine dates back to the 1960s. Interestingly, it only has modes for Épée and foil (no saber), but yeah it is a fairly interesting piece of history. submitted by /u/Spyhawkguy [link] [comments]

submitted by /u/weirdal1968 [link] [comments]

Silicon Austria Labs (SAL) and Graz University of Technology (TU Graz) have opened their new joint Power Electronics Research Laboratory (PERL), led by Roberto Petrella, staff scientist in Power Electronics at SAL and professor at the University of Udine in Italy, and Michael Hartmann, professor and head of the Electric Drives and Power Electronic Systems Institute (EALS) at TU Graz. Researchers from both SAL and TU Graz will work together, including four dedicated PhD students...

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 — says that the United States Patent Office (USPTO) has strengthened its US Patent No.8,350,294 by adding two new patent claims that are fundamental to commercial enhancement-mode GaN devices. However, the USPTO has also cancelled two claims that were the basis for the decision by the US International Trade Commission (ITC) that China-based Innoscience (Suzhou) Technology Holding Co Ltd infringed the patent. EPC will appeal the cancellation of these two claims...

Introduction: The Shift from Electronics to Photonics

As traditional semiconductor-based computing approaches its physical and energy efficiency limits, photonic chips and optical computing have emerged as transformative solutions. By harnessing the speed and parallelism of light, these technologies offer significant advantages over conventional electronics in high-performance computing (HPC), artificial intelligence (AI), and data centers. Optical computing has the potential to revolutionize the way information is processed, enabling faster, more energy-efficient computation with lower latency.

The Fundamentals of Photonic Chips

Photonic chips leverage integrated photonics to manipulate light for computing, communication, and sensing applications. Unlike traditional chips that use electrons as the primary carriers of information, photonic chips use photons, which can travel at the speed of light with minimal energy loss. Key components of photonic chips include:

• Waveguides: Optical channels that guide light through a photonic circuit, analogous to electrical traces in traditional chips.
• Modulators: Convert electrical signals into optical signals by modulating light properties such as intensity or phase.
• Detectors: Convert optical signals back into electrical signals for further processing.
• Resonators and Interferometers: Facilitate advanced signal processing functions such as filtering, multiplexing, and logic operations.
• Photonic Crystals: Control the flow of light by creating periodic dielectric structures, enhancing optical confinement and manipulation.

Optical Computing: A Seismic Change in Processing

Optical computing aims to replace or supplement electronic computation with light-based logic operations. This transition offers several key advantages:

1. Unparalleled Speed: Photons travel at the speed of light, reducing signal delay and increasing processing throughput.
2. Low Energy Consumption: Unlike electrical circuits that suffer from resistive heating, photonic systems dissipate minimal heat, enhancing energy efficiency.
3. Massive Parallelism: Optical systems can process multiple data streams simultaneously, significantly improving computational throughput.
4. Reduced Signal Crosstalk: Optical signals do not experience the same interference as electrical signals, reducing errors and noise in computation.

Core Technologies Enabling Photonic Computing 1. Silicon Photonics: Bridging Electronics and Photonics

Silicon photonics integrates optical components onto a silicon platform, enabling compatibility with existing semiconductor fabrication techniques. Key innovations in silicon photonics include:

• On-chip Optical Interconnects: Replace traditional copper interconnects with optical waveguides to reduce power consumption and signal delay.
• Optical RAM and Memory: Photonic memory elements store and retrieve data using light, enhancing data transfer speeds.
• Electro-Optical Modulators: Convert electronic signals to optical signals efficiently, allowing seamless integration into existing computing architectures.

2. Optical Logic Gates and Boolean Computation

Optical computing relies on photonic logic gates to perform fundamental computations. These gates operate using:

• Nonlinear Optical Effects: Enable all-optical switching without electronic intermediaries.
• Mach-Zehnder Interferometers (MZI): Implement XOR, AND, and OR logic functions using light phase interference.
• Optical Bistability: Maintains state information in optical latches, paving the way for optical flip-flops and memory elements.

3. Neuromorphic Optical Computing for AI Acceleration

With the growing demand for AI processing, photonic neural networks offer an alternative to traditional GPUs and TPUs. Optical deep learning accelerators employ:

• Matrix Multiplication with Light: Perform multiply-accumulate operations at light speed using photonic interference.
• Optical Tensor Processing Units (TPUs): Enhance AI inference by leveraging photonic components for ultra-fast computation.
• Wavelength-Division Multiplexing (WDM): Enables parallel processing by encoding multiple data streams onto different wavelengths of light.

4. Quantum Photonics: The Future of Secure Computation

Quantum computing benefits immensely from photonics due to the inherent properties of quantum light. Advancements in quantum photonic processors include:

• Single-Photon Sources and Detectors: Essential for quantum information processing and cryptographic applications.
• Quantum Key Distribution (QKD): Enables ultra-secure communication leveraging the principles of quantum entanglement.
• Optical Quantum Logic Gates: Facilitate complex quantum computations with minimal decoherence.

Industrial Applications and Use Cases 1. Data Centers and High-Performance Computing

Modern data centers face thermal constraints and power limitations due to electronic interconnects. Photonic interconnects dramatically reduce power consumption and increase bandwidth, making them an ideal solution for high-speed data transmission between servers and storage units.

2. Artificial Intelligence and Machine Learning Acceleration

AI workloads rely on extensive matrix operations, which photonic computing executes at orders of magnitude faster speeds than traditional electronics. Companies like Lightmatter and Lightelligence are pioneering photonic AI accelerators to enhance deep learning performance while reducing energy costs.

3. Telecommunications and Optical Networks

Fiber-optic networks already leverage photonics for data transmission, but photonic computing extends these advantages to real-time processing. Photonic switches enable ultra-fast data routing, improving the efficiency of 5G and future 6G networks.

4. Healthcare and Biophotonics

Optical computing is revolutionizing biomedical imaging and diagnostics. Photonic chips enable high-resolution imaging techniques such as optical coherence tomography (OCT) and bio-sensing applications, enhancing early disease detection.

5. Defense and Aerospace

The military and aerospace industries require ultra-fast, secure processing for signal intelligence, radar systems, and cryptographic applications. Optical computing’s speed and resistance to electromagnetic interference make it a critical enabler for next-generation defense systems.

Challenges and Future Roadmap 1. Fabrication Complexity and Scalability

While photonic chips leverage semiconductor manufacturing techniques, integrating large-scale optical circuits remains a challenge. Standardizing fabrication methods and developing CMOS-compatible photonic components are essential for commercial scalability.

2. Hybrid Photonic-Electronic Architectures

Despite the advantages of photonic computing, hybrid architectures that integrate both electronic and optical components are likely to dominate in the near term. Developing efficient electro-optic interfaces remains a key research focus.

3. Software and Algorithm Development

Current software is optimized for electronic computation, requiring a shift in programming paradigms for photonic systems. Developing photonic-aware compilers and simulation tools will accelerate adoption.

4. Energy Efficiency and Power Consumption

While photonic computing reduces heat dissipation, the challenge lies in optimizing light generation and detection components to minimize power consumption further.

Conclusion: The Dawn of the Photonic Computing Era

Photonic chips and optical computing represent a paradigm shift in computation, offering unparalleled speed, efficiency, and scalability. As silicon photonics, quantum optics, and neuromorphic photonic computing continue to advance, the technology is poised to revolutionize AI, data centers, telecommunications, and beyond. Overcoming fabrication, software, and integration challenges will be crucial for realizing the full potential of photonic computing, marking the beginning of a new era in information processing.

The post Breaking Boundaries with Photonic Chips and Optical Computing appeared first on ELE Times.

Due to their versatility, ease of design, and low cost, flyback converters have become one of the most widely used topologies in power electronics. Its structure derives from one of the three basic topologies—specifically, buck-boost topology. However, unlike buck-boost converters, flyback topologies allow the voltage output to be electrically isolated from the input power supply. This feature is vital for industrial and consumer applications.

Among the different control methods used to stabilize power converters, the most widely used control method is peak current mode, which continuously senses the primary current to provide important protection for the power supply.

Additionally, to obtain a higher design performance, it’s common to regulate the converter with the output that has the highest load using a technique called cross-regulation.

This article aims to show engineers how to correctly design the control loop that stabilizes the flyback converter in order to provide optimal functionality. This process includes minimizing the stationary error, increasing/decreasing the bandwidth as required, and increasing the phase/gain margin as much as possible.

Closed-loop flyback converter block diagram

Before making the necessary calculations for the controller to stabilize the peak current control mode flyback, it’s important to understand the components of the entire closed-loop system: the converter averaged model and the control loop (Figure 1).

              Figure 1 Here is how the components look in the entire closed-loop system. Source: Monolithic Power Systems

The design engineer’s main interest is to study the behavior of the converter under load changes. Considering a fixed input voltage (VIN), the open-loop transfer function can be modeled under small perturbations produced in the duty cycle to study the power supply’s dynamic response.

The summarized open-loop system can be modeled with Equation 1:              (1)

Where G is the current-sense gain transformed to voltage and GC(s) and GCI(s) are the transfer functions of the flyback converter in terms of output voltage and magnetizing current response (respectively) under small perturbations in the duty cycle. GαTS is the modeling of the ramp compensation to avoid the double-pole oscillation at half of the switching frequency.

Flyback converter control design

There are many decisions and tradeoffs involved in designing the flyback converter’s control loop. The following sections of the article will explain the design process step by step. Figure 2 shows the design flow.

Figure 2 The design flow highlights control loop creation step by step. Source: Monolithic Power Systems

Control loop design process and calculations

Step 1: Design inputs

Once the converter’s main parameters have been designed according to the relevant specifications, it’s time to define the parameters as inputs for the control loop design. These parameters include the input and output voltages (VIN and VOUT, respectively), operation mode, switching frequency (fSW), duty cycle, magnetizing inductance (LM), turns ratio (NP:NS), shunt resistor (RSHUNT), and output capacitance (COUT). Table 1 shows a summary of the design inputs for the circuit discussed in this article.

Table 1 Here is a summary of design inputs required for creating control loop. Source: Monolithic Power Systems

To design a flyback converter compensator, it’s necessary to first obtain all main components that make the converter. Here, HF500-40 flyback regulator is used to demonstrate design of a compensator using optocoupler feedback. This device is a fixed-frequency, current-mode regulator with built-in slope compensation. Because the converter works in continuous conduction mode (CCM) at low line input, a double-pole oscillation at half of the switching frequency is produced; built-in slope compensation dampens this oscillation, making its effect almost null.

Step 2: Calculate parameters of the open-loop transfer function

It’s vital to calculate the parameters of the open-loop transfer function and calculate the values for all of the compensator’s parameters that can optimize the converter at the dynamic behavior level.

The open-loop transfer function of the peak current control flyback converter (also including the compensation ramp factor) can be estimated with Equation 2:

      (2)

Where D’ is defined by the percentage of time that the secondary diode (or synchronous FET) is active during a switching cycle.

The basic canonical model can be defined with Equation 3:              (3)

Note that the equivalent series resistance (ESR) effect on the output capacitors has been included in the transfer function, as it’s the most significant parasitic effect.

By using Equation 2 and Equation 3, it’s possible to calculate the vital parameters.

The resonant frequency (fO) can be calculated with Equation 4:

              (4)

After inputting the relevant values, fO can be calculated with Equation 5:              (5)

The right-half-plane zero (fRHP) can be estimated with Equation 6:              (6)

The q-factor (Q) can be calculated with Equation 7:              (7)

After inputting the relevant values, Q can be estimated with Equation 8:              (8)

The DC gain (K) can be calculated with Equation 9:              (9)

After inputting the relevant values, K can be estimated with Equation 10:              (10)

The high-frequency zero (fHF) can be calculated with Equation 11:

              (11)

It’s important to note that with current mode control, it’s common to obtain values well below 0.5 for Q. With this in mind, the result of the second-degree polynomial in the denominator of the transfer function ends up giving two real and negative poles. This is different from voltage-control mode or when there is a very large compensation ramp, which results in two complex conjugate poles.

The two real and negative poles can be estimated with Equation 12:              (12)

The new open-loop transfer function can be calculated with Equation 13:              (13)

The cutoff frequency (fC) can be estimated with Equation 14:              (14)

The following sections will explain how the frequency compensator design achieves power supply stability and excellent performance.

Step 3: Frequency compensator design

Once the open-loop transfer function is modeled, it’s necessary to design the frequency compensator such that it achieves the best performance possible. Because the frequency response of the above transfer function has two separate poles—one at a low frequency and one at a high frequency—a simple Type II compensator can be designed. This compensator does not need an additional zero, which is not the case in voltage-control mode because there is a double pole that produces a resonance.

To minimize the steady-state error, it’s necessary to design an inverted-zero (or a pole at the origin) because it produces higher gains at low frequencies. To ensure that the system’s stability is not impacted, the frequency must be at least 10 times lower than the first pole, calculated with Equation 15:

           (15)

Due to the ESR parasitic effect at high frequencies, it’s necessary to design a high-frequency pole to compensate for and remove this effect. The pole can be estimated with Equation 16:

(16)

On the other hand, it’s common to modify the cutoff frequency to achieve a higher or lower bandwidth and produce faster or slower dynamic responses, respectively. Once the cutoff frequency is selected (in this case, fC is increased up to 6.5 kHz, or 10% of fSW), the compensator’s middle-frequency gain can be calculated with Equation 17:           (17)

Once the compensator has been designed within the frequency range, calculate the values of the passive components.

Step 4: Design the compensator’s passive components

The most common Type II compensator used for stabilization in current control mode flyback converters with cross-regulation is made up of an optocoupler feedback (Figure 3).

Figure 3 Type-II compensator is made up with optocoupler feedback. Source: Monolithic Power Systems

The compensator transfer function based on optocoupler feedback can be estimated with Equation 18:          (18)

The middle-frequency gain is formed in two stages: the optocoupler gain and the adjustable voltage reference compensator gain, calculated with Equation 19:

(19)

It’s important to calculate the maximum resistance to correctly bias the optocoupler. This resistance can be estimated with Equation 20:     (20)

The parameters necessary to calculate RD can be found in the optocoupler and the adjustable voltage reference datasheets. Table 2 shows the typical values for these parameters from the optocoupler.

Table 2 Here are the main optocoupler parameters. Source: Monolithic Power Systems

Table 3 shows the typical values for these parameters from the adjustable voltage reference.

Table 3 The above data shows adjustable voltage reference parameters. Source: Monolithic Power Systems

Once the above parameters have been obtained, RD can be calculated with Equation 21:              (21)

Once the value of R3 is obtained (in this case, R3 is internal to the HF500-40 controller, with a minimum value of 12 kΩ), as well as the values for R1, R2, and RD (where RD = 2 kΩ), RF can be estimated with Equation 22:   (22)

Where GCOMP is the compensator’s middle frequency gain, calculated with Equation (17). GCOMP is used to adjust the power supply’s bandwidth.

Because the inverted zero and high-frequency pole were already calculated, CF and CFB can be calculated with Equation 23 and Equation 24, respectively.         (23)           (24)

Once the open-loop system and compensator have been designed, the loop gain transfer function can be estimated with Equation 25:           (25)

Equation 25 is based on Equation 13 and Equation 18.

It’s important to calculate the phase and gain margins to ensure the stability of power supply.

The phase margin can be calculated with Equation 26:          (26)

After inputting the relevant values, the phase margin can be calculated with Equation 27:          (27)

If the phase margin exceeds 50°, it’s an important parameter necessary to comply with certain standards.

At the same time, the gain margin can be approximated with Equation 28:            (28)

Equation 29 is derived from Equation 25 at the specified frequency:     (29)

In this scenario, the gain margin is below -10dB, which is another important parameter to consider, particularly regarding compliance with regulation specifications. If the result is close to 0dB, some iteration is necessary to decrease the value; otherwise, the performance is suboptimal. This iteration must start by decreasing the value of the cutoff frequency.

This complete transfer function provides stability to the power supply and the best performance made possible by:

• Minimizing steady-state error
• Minimizing ESR parasitic effect
• Increasing bandwidth of power supply up to 6.5 kHz

Final design

After calculating all the passive component values for the feedback loop compensator and determining the converter’s main parameters, the entire flyback can be designed using the flyback regulator. Figure 4 shows the circuit’s final design using all calculated parameters.

Figure 4 Here is how the final design circuit schematic looks like. Source: Monolithic Power Systems

Figure 5 shows the bode plot of the complete loop gain frequency response.

Figure 5 Bode plot is shown for the complete loop gain frequency response. Source: Monolithic Power Systems

Obtaining the flyback averaged model via small-signal analysis is a complex process to most accurate approximation of the converter’s transfer functions. In addition, the cross-regulation technique involves secondary-side regulation through optocoupler feedback and an adjustable voltage reference, which complicates calculations.

However, by following the four steps explained in this article, a good approximation can be obtained to improve the power supply’s performance, as the output with the heaviest load is directly regulated. This means that the output can react quickly to load changes.

Joan Mampel is application engineer at Monolithic Power Systems (MPS).

Related Content

• Power Tips: Compensating Isolated Power Supplies
• Details on compensating voltage mode buck regulators
• Power Tips #139: How to simplify AC/DC flyback design with a self-biased converter
• Modeling and Loop Compensation Design of Switching Mode Power Supplies, Part 1
• Modeling and Loop Compensation Design of Switching Mode Power Supplies, Part 2

The post Design a feedback loop compensator for a flyback converter in four steps appeared first on EDN.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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