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South Korean LED maker Seoul Semiconductor Co Ltd says that the Supreme Court of Korea has recently upheld a lower court ruling convicting Taiwan-based LED maker Everlight Electronics Co Ltd of violating the Act on Prevention and Protection of Industrial Technology for misappropriating Seoul Semiconductor’s patented LED technology....

The ongoing electrification of cars brings new trends and requirements with every new design cycle. One trend for battery electric vehicles is reducing the size of the low-voltage batteries, which power either 12-V or 48-V systems. Some auto manufacturers are even investigating whether it’s possible to eliminate low-voltage batteries completely. Regardless, you’ll need isolated high- to low-voltage DC/DC converters as a backup or buffer for the low-voltage battery rail. In all of these cases, the high-voltage battery powers the DC/DC converters. Many high-voltage battery systems in cars currently in production or in development use a 400-V or 800-V architecture.

Given the disadvantages of discharging the high-voltage battery more than necessary, high- to low-voltage DC/DC converters need to support operation with the highest possible efficiency. Different activity states in the car require different power levels in the subsystems—for example, 60 W when the driver opens the car, 300 W when the car is in standby but not moving, and 3 kW or more when the car is in drive and fully operational. It is not possible to optimize a single DC/DC converter to cover all three potential output power levels with high-efficiency operation over the whole load range; in the examples given here, you would need two or three independent power converters.

Converter topology selection

In this power tip, I will focus on the 300-W output power range, also known as a micro DC/DC converter. Suitable DC/DC topologies for this output power range include half- and full-bridge converters. Resonant topologies such as half-bridge inductor-inductor-capacitor (LLC) converters offer higher efficiency conversion than their hard-switched counterparts through zero-voltage switching (ZVS) on the primary side and zero-current switching (ZCS) on the secondary side. Another potential topology is the phase-shifted full-bridge (PSFB) topology, which also employs soft switching by leveraging ZVS but is less cost-effective for the 300-W target output power level, since it requires four switches on the primary side.

Figure 1 shows the converter efficiency for various input voltages and load values for the Texas Instruments (TI) Automotive 300 W Micro DC/DC Converter Reference Design Using Half-Bridge LLC. Optimized for 400-V battery inputs and a 48-V output, this design reflects a good compromise between efficiency and cost of the four different topologies.

Figure 1 Efficiency plot of the automotive 300-W micro DC/DC converter reference design. Source: Texas Instruments

In an electric vehicle with a 400-V architecture, the battery voltage can vary from 200 V to 450 V. In general, LLC converters are not known to work well with very wide input voltage ranges because, with peak current-mode control, such a wide input voltage range could lead to the converter prematurely entering light-load efficiency mode (also known as burst mode) under full load conditions, or reaching overload conditions too early under low input-voltage conditions. The reason for both effects is that the feedback voltage is scaled in the controller with the input voltage, making it switching frequency-dependent.

So why should you even consider an LLC for this type of application? The UCC256612-Q1 LLC controller from TI uses input-power proportional control (IPPC), which overcomes these limitations. The feedback voltage only scales with the input power, and stays quasi-constant over the whole input voltage range for a constant load current. Figure 2 shows the differences between IPPC feedback voltage behavior (Figure 2a) and traditional peak current-mode control feedback voltage behavior (Figure 2b).

Figure 2 Feedback over input voltage using (a) IPPC and (b) traditional LLC control. Source: Texas Instruments

Accurate output voltage regulation with isolation

The proper regulation of isolated power supplies in electric vehicles is a tricky topic. Optocouplers, typically used for secondary-side regulation (SSR) in nonautomotive applications, are considered unreliable in automotive applications because of aging effects on the internal glass passivation over their lifetime. An alternative way to provide output feedback to a controller on the primary side is primary-side regulation (PSR) through an auxiliary winding. PSR is not very accurate for high output currents because the voltage drop across the rectifier(s) and droop across traces to the load will be current-dependent but not visible on the auxiliary winding. A second option is to use isolated amplifiers.

For SSR, the reference design uses the TI ISOM8110-Q1 automotive-qualified pin-to-pin replacement for traditional optocoupler devices. Superior aging performance and smaller current transfer ratio (CTR) variations of the ISOM8110-Q1 enable more accurate and reliable designs, which are crucial for automotive systems with expected lifetimes of at least 10 years. In addition, the ISOM8110-Q1 has a slightly different transfer function than traditional optocouplers, enabling higher control loop bandwidths that can ultimately save costs because lower output capacitance values will be able to meet similar load transient requirements.

Figure 3 shows a load transient from 3 A to 6.25 A and back to 3 A for the reference design with a 48-V output. The output voltage deviation with four 82-µF output capacitors is only 400 mV.

Figure 3 Load transient behavior, 400 VIN, 3 A to 6.25 A, and back to 3 A. Source: Texas Instruments

Apart from dynamic output accuracy, load regulation under static load conditions is important too. Figure 4 shows the load regulation across different input voltages for the reference design.

Figure 4 Load regulation over various input voltage levels, illustrating good load regulation under static load conditions. Source: Texas Instruments

For full functionality, the ISOM8110-Q1 requires a bias current of at least 700 µA on the diode side of the device and 700 µA multiplied by the worst-case CTR on the transistor side, which is 155% with a 5 mA bias current and 180% with a 2 mA bias current. Because some control ICs are optimized for minimum standby power, the feedback pin of such a controller might not be capable of sourcing sufficient current to supply the ISOM8110-Q1 on its own. A simple workaround for such a scenario is to provide the bias current with a pull-up resistor from a regulated voltage rail to the feedback pin. The UCC256612-Q1 generates a 5-V rail with an internal low-dropout regulator, which is externally accessible and can therefore provide the bias current for the opto-emulator IC. The block diagram in Figure 5 demonstrates the implementation of this workaround.

Figure 5 Secondary-side feedback implementation using the ISOM8110-Q1, with external bias from a control IC on the primary side. Source: Texas Instruments

Alternative for micro DC/DC converters

The reference design demonstrates that the half-bridge LLC topology can be a viable alternative for automotive micro DC/DC converters in the 300 W power range, demonstrating good efficiency as well as excellent static and dynamic output voltage regulation.

The ISOM8110-Q1 is a cost-effective, accurate and reliable option to close the loop of isolated power converters in automotive applications. It works well with controllers optimized for low standby power when there is the possibility of an external bias voltage.

Markus Zehendner is a systems engineer and Member Group Technical Staff in TI’s EMEA Power Supply Design Services group. He holds a bachelor’s degree in electrical engineering and a master’s degree in electrical and microsystems engineering from the Technical University of Applied Sciences in Regensburg, Germany. His main focus lies on automotive low-voltage designs for advanced driver assistance systems and infotainment, as well as high-voltage designs for hybrid and electric vehicle applications.

 Related Content

• Power Tips #75: USB Power Delivery for automotive systems
• Power Tips #118: Using interleaved ground planes to improve noise filtering from isolated power supplies
• Power Tips #101: Use a thermal camera to assess temperatures in automotive environments
• Power Tips #104: Design considerations for boosting power density in infotainment
• Power Tips #129: Driving high voltage silicon FETs in 1000-V flybacks

The post Power Tips #144: Designing an efficient, cost-effective micro DC/DC converter with high output accuracy for automotive applications appeared first on EDN.

Старт набору студентів на онлайн-курси проєкту TANDEM-UA-DE в зимовому семестрі 2025/2026 (реєстрація до 09.09.2025) kpi ср, 08/27/2025 - 16:49

💢 Сесія професорсько-викладацького складу 2025 kpi ср, 08/27/2025 - 15:33

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Specialty analog foundry Tower Semiconductor Ltd of Migdal Haemek, Israel and Xscape Photonics Inc of Fort Lee, NJ and Santa Clara, CA, USA (which is funded by industry leaders such as NVIDIA and Cisco, and develops silicon photonics interconnects for AI data centers) have announced the successful prototyping and validation kit availability of what is claimed to be the industry’s first on-chip, optically pumped, multi-wavelength laser source. Built on Tower’s mature, high-volume PH18 silicon photonics platform, the solution supports CWDM and DWDM wavelength grids and is tailored for AI data-center fabrics, where bandwidth density, power efficiency and scalability are essential...

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Nowadays, individuals own an increasing number of devices—such as fitness trackers and smartphones that continuously generate valuable data. At the same time, organizations like banks, hospitals, and enterprises produce vast amounts of sensitive information. However, due to strict privacy regulations, this data cannot be openly shared for centralized processing. In such scenarios, federated learning offers a transformative solution: it enables machine learning models to be trained directly on-device or within institutional boundaries, without transferring raw data. This approach preserves privacy while unlocking powerful, collaborative AI capabilities. As a result, data from diverse sources both personal and institutional can be securely leveraged to extract insights and drive smarter decisions. Below are 10 compelling real-world applications where federated learning is making a significant impact.

1. Telecommunications

The federated model enables telecommunication firms to study patterns of their clients, enhance network performance, and make accurate tele-service projections for their distributed systems. This fosters efficient network systems while safeguarding customer information. In the same context, mobile operators stand to enhance calling services from user data sourced from spatially dispersed systems.

2. Autonomous Vehicles

Self-driving cars and connected vehicles utilize federated learning to collaborate vehicles enhance navigation, obstacle identification, and safety measures. This eliminates the need to consolidate personal driving information. Drivers of self-driven automobiles and fleet operators utilize federated learning to enhance safety, navigation, and object detection with the aid of local sensor data consisting of cameras, LIDAR, and object detection.

3. Finance

Banks and fintech companies use federated learning for detecting fraud, credit scoring, and modeling credit risk. One example is the training of a multi-bank fraud detection model to recognise suspicious transactions while safeguarding user information.

4. Smart Devices & IoT

Smartphones, as well as other wearable devices, use federated learning to enhance voice recognition, keyboard prediction, and health tracking functions. An instance is the Gboard keyboard from Google, which leverages federated learning to upgrade its autocorrect as well as next-word prediction features grounded on users’ typing patterns.

5. Cybersecurity

Federated learning is employed in factories for process optimization, predictive maintenance, and even defect detection. Federated learning enables multiple organizations to collaboratively train intrusion detection models using local network logs. This approach enhances threat detection accuracy while preserving sensitive data and complying with privacy regulations.

6. Manufacturing

Factories use federated learning for predictive maintenance, defect detection, and process optimization. For instance, multiple production lines can train a model to predict equipment failure using local sensor data, reducing downtime.

7. Energy & Utilities

Energy companies and power grids use advanced techniques to forecast demand and anticipate failures in the system by learning from distributed sensor data across substations and smart meters. Use Case includes a national utility company uses federated learning to predict peak electricity usage across cities, helping balance load distribution without accessing individual household data.

8. Retail & E-commerce

Retailers customize product recommendations cross-sell and up-sell suggestions and basket-level cross-product purchase analytics across different store locations without sharing any stepwise item-level purchase data of shoppers. A classic use case is a global fashion retailer who wants to suggest outfit combinations based on current trends of different geographies. The retailer can now use the federated approach, enabling training of the model across all the stores in the regions while protecting shopper and purchase data.

9. Content Platforms

With less risk to user privacy, platforms can better personalize user feeds and automatically moderate content by learning locally from user interactions. Use Case: A video streaming app enhances its recommendation system by locally training on user watch histories stored on devices, ensuring tailored recommendations while refraining from uploading any viewing data to the cloud.

10. Aviation

Carriers and aircraft manufacturers develop models from flight execution and servicing records over different fleets in an attempt to improve safety and cut downtime, with the added benefit of keeping proprietary data private. A use case is offered by the federated model training from different airlines that enables the prediction of an engine’s wear and tear based on flight conditions, which aids in the scheduling of proactive maintenance without the need to share sensitive operational data.

Conclusion:

Federated learning protects privacy while facilitating cooperative model training across dispersed data sources. It lowers the risks associated with data transfers, conforms with data protection laws, and enables businesses to leverage insights without jeopardizing user privacy.

The post Top 10 Federated Learning Applications and Use Cases appeared first on ELE Times.

Announced today, the new MCUs future-proof industrial and data center systems in anticipation for the post-quantum world.

Photonic application-specific integrated circuit (PASIC) chip designer and manufacturer OpenLight of Santa Barbara, CA, USA (which launched as an independent company in June 2022, introducing the first open silicon photonics platform with heterogeneously integrated III-V lasers, modulators, amplifiers and detectors) has closed its oversubscribed $34m Series A fundraising round, which was co-led by Xora Innovation and Capricorn Investment Group. Other participants include Mayfield; Juniper Networks (now part of HPE); Lam Capital (the corporate venture arm of Lam Research Corp); New Legacy Ventures; and K2 Access...

Federated learning is transforming AI’s potential in India by allowing models to be trained without infringing on the privacy of decentralized data. Federated learning is of critical importance in healthcare, finance, and consumer technology due to the rising needs of industries for AI that is secure, compliant with regulations, and privacy-preserving. Due to India having a flourishing technology ecosystem as well as a strong pool of AI talents, India is emerging as a leader in this technology. This article will discuss the leading 10 companies in India that focus on federated learning.

1. TCS Research

TCS Research as the innovation wing of Tata Consultancy Services, TCS Research collaborates with federated learning for enterprise AI. Their initiatives cover healthcare, banking, and smart city projects, centering on the safe training of models over distributed data silos.

2. Wipro HOLMES

Wipro’s AI platform, uses federated learning to provide intelligent automation and edge AI. Its application in telecommunications, manufacturing, and IT services aids in the development of AI models without eroding data privacy.

3. Infosys Nia

Infosys Nia An all-in-one AI platform, Infosys Nia also offers federated learning for decentralized data modeling, which is especially beneficial in retail, and finance, where data sensitivity is high and compliance is critical.

4. SigTuple

With its headquarters in Bengaluru, SigTuple is a health tech company which employs federated learning to streamline the analysis of medical images and diagnostics, while still maintaining patient data privacy. Their AI solutions not only save time but also improve the decision-making processes of pathologists and radiologists.

5. Qure.ai

With over a decade of specialization in AI-driven radiology, Qure.ai is a clear leader. They are notable examples of the application of federated learning in radiology, not only for advancing diagnostic precision but also for safeguarding critical medical information.

6. Vaidik AI

Vaidik AI marks a new chapter in the federated learning narrative of India. It launched an extensive selection of AI services, including the fine-tuning of LLMs and multilingual AI. Its multidisciplinary expertise in data annotation and the privacy-first approach to AI model development is well known. It provides healthcare, finance, and education sectors with economical and scalable solutions.

7. ActionLabs AI

ActionLabs AI is located in Bengaluru and works with federated learning, edge AI, and generative model creation. Though healthcare and fintech startups appearing to be ActionLabs’ primary areas of focus, the company’s small size allows it to efficiently cater to a wider range of companies.

8. Accenture India

Accenture adapts federated learning to its Responsible AI framework, assisting clients spanning the energy sector to public services in securely training models on decentralized data.

9. Fractal Analytics

Fractal Analytics Fractal applies federated learning to generate consumer insights for retail and CPG. Their solutions enable brands to analyze consumer behavior without pooling sensitive data.

10. Intel India

Intel India, with its offices in Bengaluru and Hyderabad, is pivotal in advancing federated learning as it refines secure hardware platforms such as Trusted Execution Environments (TEEs) and furthers AI research through Intel Labs. It also champions privacy-preserving AI in healthcare, smart cities, and edge computing.

Conclusion:

The federated learning ecosystem in India is evolving rapidly with the presence of global technology leaders such as Intel and the innovative local startups such as ActionLabs AI, Vaidik AI, and SigTuple. These firms not only expand the frontiers of privacy-preserving AI but also position the federated learning ecosystem to thrive on data collaboration devoid of security risks. With growing demand across healthcare, finance, and edge computing, federated learning is becoming a cornerstone of ethical AI development in India.

The post Top 10 Federated Learning Companies in India appeared first on ELE Times.

New Cadence Palladium Dynamic Power Analysis App enables designers of AI/ML chips and systems to create more energy-efficient designs and accelerate time to market

Cadence announced a significant leap forward in the power analysis of pre-silicon designs through its close collaboration with NVIDIA. Leveraging the advanced capabilities of the Cadence Palladium Z3 Enterprise Emulation Platform, utilizing the new Cadence Dynamic Power Analysis (DPA) App, Cadence and NVIDIA have achieved what was previously considered impossible: hardware accelerated dynamic power analysis of billion-gate AI designs, spanning billions of cycles within a few hours with up to 97 percent accuracy. This milestone enables semiconductor and systems developers targeting AI, machine learning (ML) and GPU-accelerated applications to design more energy-efficient systems and accelerate their time to market.

The massive complexity and computational requirements of today’s most advanced semiconductors and systems present a challenge for designers, who have until now been unable to accurately predict their power consumption under realistic conditions. Conventional power analysis tools cannot scale beyond a few hundred thousand cycles without requiring impractical timelines. In close collaboration with NVIDIA, Cadence has overcome these challenges through hardware-assisted power acceleration and parallel processing innovations, enabling previously unattainable precision across billions of cycles in early-stage designs.

“Cadence and NVIDIA are building on our long history of introducing transformative technologies developed through deep collaboration,” said Dhiraj Goswami, corporate vice president and general manager at Cadence. “This project redefined boundaries, processing billions of cycles in as few as two to three hours. This empowers customers to confidently meet aggressive performance and power targets and accelerate their time to silicon.”

“As the era of agentic AI and next-generation AI infrastructure rapidly evolves, engineers need sophisticated tools to design more energy-efficient solutions,” said Narendra Konda, vice president, Hardware Engineering at NVIDIA. “By combining NVIDIA’s accelerated computing expertise with Cadence’s EDA leadership, we’re advancing hardware-accelerated power profiling to enable more precise efficiency in accelerated computing platforms.”

The Palladium Z3 Platform uses the DPA App to accurately estimate power consumption under real-world workloads, allowing functionality, power usage and performance to be verified before tapeout, when the design can still be optimized. Especially useful in AI, ML and GPU-accelerated applications, early power modeling increases energy efficiency while avoiding delays from over- or under-designed semiconductors. Palladium DPA is integrated into the Cadence analysis and implementation solution to allow designers to address power estimation, reduction and signoff throughout the entire design process, resulting in the most efficient silicon and system designs possible.

The post Cadence Accelerates Development of Billion-Gate AI Designs with Innovative Power Analysis Technology Built on NVIDIA appeared first on ELE Times.

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Sixfab’s ALPON X5 AI aims to make real-world edge AI deployment faster, cheaper, and far less frustrating.

Fabless firm Cambridge GaN Devices Ltd (CGD) — which was spun out of the University of Cambridge in 2016 to design, develop and commercialize power transistors and ICs that use GaN-on-silicon substrates — has appointed Robin Lyle, a 30-year veteran of the power semiconductor industry, as vice president of R&D...

Toshiba Electronic Devices & Storage Corp of Kawasaki, Japan and Chinese silicon carbide (SiC) supplier SICC Co Ltd have signed a memorandum of understanding (MOU) to explore collaboration in improving the characteristics and quality of silicon carbide (SiC) power semiconductor wafers developed and manufactured by SICC, and expanded supply of stable, high-quality wafers from SICC to Toshiba. The two firms will discuss the scope of their joint efforts and mutual support...

Infineon Technologies AG Munich, Germany is expanding its CoolSiC MOSFETs 650V G2 portfolio with new 75mΩ variants to meet the demand for more compact and powerful systems...

The new series targets CPU-attached NVMe deployments with disaggregated architecture and hardware offload.

❤️ 500 000 гривень за кращі цифрові рішення для громад: EGAP Ideathon 2025 kpi вт, 08/26/2025 - 17:14

Accurate, inexpensive, and mature platinum resistance temperature detectors (PRTDs) with an operating range extending from the cryogenic to the incendiary are a gold (no! platinum!) standard for temperature measurement.

Similarly, the 4 to 20 mA analog current loop is a legacy, but still popular, noise- and wiring-resistance-tolerant interconnection method with good built-in fault detection and transmitter “phantom-power” features.

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

Figure 1 combines them in a simple, cheap, and cheerful temperature sensor using just eight off-the-shelf (OTS) parts, counting the PRTD. Here’s how it works.

Figure 1 PRTD current loop sensor with Ix = 500 µA constant current excitation.
Ix = 2.5v/R2, PRTD resistance = R1(Io/Ix – 1)
R1 and R2 are 0.1% tolerance (ideally)

The key to measurement accuracy is the 2.50-V LM4040x25 shunt reference, available with accuracy grade suffixes of  0.1% (x = A), 0.2% (B), 0.5% (C), and 1% (D). The “B” grade is consistent (just barely) with a temperature measurement accuracy of ±0.5oC.

R1 and R2 should have similar precision. R2 throttles the 2.5 V to provide Ix = 2.5/R2 = 500 µA excitation to T1. Because A1 continuously servos the Io output current to hold pin3 = pin4 = LM4040 anode, the 2.5 V across R2 is held constant, therefore Ix is likewise.

Thus, the voltage across output sense resistor R1 is forced to Vr1 = Ix(Rprtd) and Io = Ix(Rprtd/R1 + 1). This makes Io/Ix = Rprtd/R1 + 1 and Rprtd/R1 = Io/Ix – 1 for Rprtd = R1(Io/Ix – 1).

Wrapping it all up with a bow: Rprtd = R1(Io/(2.5/R2) – 1). Note that accommodation of different Rprtd resistance (and therefore temperature) ranges is a simple matter of choosing different R1 and/or R2 values.

Conversion of the Io reading to Rprtd is an easy chore in software, and the step from there to temperature isn’t much worse, thanks to Callendar Van Dusen math.

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 two-wire temperature transmitter using an RTD sensor
• Improved PRTD circuit is product of EDN DI teamwork
• The power of practical positive feedback to perfect PRTDs
• Hotwire thermostat: Using fine copper wire as integrated sensor and heater for temperature control
• High-accuracy temperature measurements call for PRTDs and precision delta-sigma ADCs
• Minimize measurement errors in RTD circuits
• DIY RTD for a DMM

The post Simple but accurate 4 to 20 mA two-wire transmitter for PRTDs appeared first on EDN.