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The curators of gallium nitride (GaN) foundry BelGaN of Oudenaarde, East Flanders (Belgium’s last industrial chip manufacturer) – which filed for bankruptcy in July 2024, leaving more than 440 staff unemployed – have reached an agreement for a European investor to acquire the premises for €20.35m (including an advance payment of €2m that has been paid). The facility includes two large cleanrooms, but excludes manufacturing equipment, which was auctioned online between late November and mid-March in about 1800 lots (including wafer processing machines, mainframe computers and other specialized equipment), raising over €23m...

The phasing method

In radio communications, one way to generate single sideband (SSB) signals is to make a double sideband signal by feeding a carrier and a modulation signal into a balanced modulator to create a double sideband (DSB) signal and then filter out one of the two resulting sidebands.

If you filter out the lower sideband, you’re left with the upper sideband and if you filter out the upper sideband, you’re left with the lower sideband. However, another way to generate SSB without that filtering has been called “the phasing method.”

Let’s look at that in the following sketch in Figure 1.

Figure 1 Phasing method of generating an SSB signal where the outputs of Fc and Fm are 90° apart with respect to each other

The outputs of the carrier (Fc) quadrature phase shifter and the modulating signal (Fm) quadrature phase shifter need only be 90° apart with respect to each other. The phase relationships to their respective inputs are irrelevant.

Four cases of SSB generation

In the following equations, those two unimportant phase shifts are called “phi” and “chi” for no particular reason other than their pronunciations happen to rhyme. Mathematically, we examine four cases of SSB generation.

Case 1, where “Fc at 90°” and “Fm at 90°” are both +90°, or in the same directions (Figure 2). Case 2, where “Fc at 90°” and “Fm at 90°” are both -90°, or in the same directions (Figure 3).

Figure 2 Mathematically solving for upper and lower side bands where “Fc at 90°” and “Fm at 90°” are both +90°, or in the same directions.

Figure 3 Mathematically solving for upper and lower side bands where “Fc at 90°” and “Fm at 90°” are both -90°, or in the same directions.

Case 3, where “Fc at 90°” is -90°and “Fm at 90°” is +90°, or in the opposite directions (Figure 4). Case 4, where “Fc at 90°” is +90°and “Fm at 90°” is -90°, or in the opposite directions (Figure 5).

Figure 4 Mathematically solving for upper and lower side bands where “Fc at 90°” is -90°and “Fm at 90°” is +90°, or in the opposite directions

Figure 5 Mathematically solving for upper and lower side bands where “Fc at 90°” is +90°and “Fm at 90°” is -90°, or in the opposite directions.

The quadrature phase shifter for the carrier signal only needs to operate at one frequency, which is that of the carrier itself and which we have called “Fc”. The quadrature phase shifter for the modulating signal however has to operate over a range of frequencies. That device has to develop 90° phase shifts for all the frequency components of that modulating signal and therein lies a challenge.

90° phase shifts for all frequency components

There is a mathematical operator called the Hilbert transform which is described here. There, we find an illustration of the Hilbert transformation of a square wave. From that page, we present the sketch in Figure 6.

Figure 6 A square wave and its Hilbert transform, bringing about a 90° phase shift of each frequency component of the input signal in its own time base.

The underlying mathematics of the Hilbert transform is described in terms of a convolution integral but in another sense, you can look at the result as bringing about a 90° phase shift of each frequency component of the input signal in its own time base, in the above case, of a square wave. This phase shift property is the very thing we want for our modulating signal in SSB generation.

In the case of Figure 7, I took each frequency component of a square wave—by which I mean the fundamental frequency plus a large number of properly scaled odd harmonics—and phase shifted each of them by 90° in their respective time frames. I then added up those phase-shifted terms.

Figure 7 A square wave and the result of 90° phase shifts of each harmonic component in that square wave.

Please compare Figure 6 to the result in Figure 5. They look very much the same. The finite number of 90° phase shift and summing steps very nicely approximate the Hilbert transform. 

The ideal case for SSB generation can be expressed as starting with a carrier signal, you create a second carrier signal at the same frequency as the first, but phase shifted by 90°. Putting this another way, the first carrier signal and the second carrier signal are in quadrature with respect to one another.

You then take your modulating signal and generate its Hilbert transform. You now have two modulating signals in which each frequency component of the one is in quadrature with the corresponding frequency component of the other.

Using two balanced modulators, you apply one carrier and one modulating signal to one balanced modulator and apply the other carrier and the other modulating signal to the other balanced modulator. The outputs of the two balanced modulators are then either added to each other or subtracted from each other. Based on the four mathematical examples above, you end up with either an upper sideband SSB signal or a lower sideband SSB signal.

This offers high performance and thus the costly filters described in the first paragraph above are not needed.

Practically applying a Hilbert transform

As a practical matter however, instead of actually making a true Hilbert transformer (I have no idea how or even if that could be done.), we can make a variety of different circuits which will give us the 90° phase shifts we need for our modulating signals over some range of operating frequencies with each frequency component 90° shifted in its own time frame.

One of the earliest purchasable devices for doing this over the range of speech frequencies was a resistor-capacitor network called the 2Q4 which was made by a company called Barker and Williamson. The 2Q4 came in a metal can with a vacuum-tube-like octal base. Its dimensions were very close to that of a 6J5 vacuum tube, but the can of the 2Q4 was painted grey instead of black. (Yes, I know that I’m getting old.)

Another approach to obtaining the needed 90° phase relationships of the modulating signals is by using cascaded sets of all-pass filters. That technique is described in “All-pass filter phase shifters.”

One thing to note is that the Hilbert transformation itself and our approximation of it can lead to some really spiky signals. The spikiness we see for the square wave arises for speech waveforms too. This fact has an important practical implication.

SSB transmitters tend to have high peak output powers versus their average output power levels. This is why in amateur radio, while there is an FCC-imposed operating power limit of 1000 watts, the limit for SSB transmission is 2000 watts peak power.

John Dunn is an electronics consultant, and a graduate of The Polytechnic Institute of Brooklyn (BSEE) and of New York University (MSEE).

Related Content

• All-pass filter phase shifters
• Spectral analysis and modulation, part 5: Phase shift keying
• Single-sideband demodulator covers the HF band
• SSB modulator covers HF band
• Impact of phase noise in signal generators
• Choosing a waveform generator: The devil is in the details
• Modulation basics, part 1: Amplitude and frequency modulation

The post Single sideband generation appeared first on EDN.

I've only asked from the internet, lately I realized I must also share. This will be the first piece of information I share, that I would've found valuable if I'd came upon. I was making an LED stroboscope, to make it work, it felt right to overdrive an LED since the on time would be very very short(under 1ms) and a bigger LED would just be a waste. So, I needed information on what would happen if an LED was driven way above the rated forward voltage. Datasheets provide a graph up to 42V for 36V leds, but nothing beyond. There are some written information here and there on the internet that the LEDs are basically thermally limited, but no experiment results. So I improvised an experimental setup and got the data myself. Experimental setup is a modified XL6009 dc-dc step up supply that is adjustable up to 62 Volts, a 1000uf 100V electrolytic capacitor for high voltage storage, a simple optocoupler driven mosfet module available on maker stores, a series shunt resistor of value 0.1 ohms, a digital oscilloscope and a 36V COB LED array SDW01F1C DB3E-V0 made by Seoul. Also a current limiting resistor right after the XL6009 to prevent it from overloading during pulses, as the capacitor is the main LED power supply. A stm32f103 bluepill board triggers the optocoupler-mosfet switch once a second, for 500us. Mosfet switches the bare high DC voltage on the capacitor to the LED. XL6009 output voltage is adjusted in 1 volt steps and resulting voltage drop on the shunt resistor during the LED on time is measured through the oscilloscope. This experimental setup is limited by the XL6009 ic which normally has its output pin voltage listed as 60V in absolute maximum ratings, this setup goes 2 volts above that. I didn't wanna try more. I want to take it further with a higher votlage DC power supply. Findings: As you can see from the graph, the I-V relation is pretty linear, with a slight curve visible. with almost double the voltage, current increases tenfold. Forward current at a certain forward voltage is temperature dependent, I've observed it during the experiment but did not record. The LED only heats up almost as if the average power it's being driven with that average power continuously. Of course, the LED light efficiency drops as the forward current increases, but not by orders. I got the LED pretty hot with extended pulses(60ms at 50V), and the LED was not measurably damaged. It really seems the LED drive current is indeed limited by the junction temperature, and drive conditions way above maximum ratings don't just magically burn things without heating them up first. I reckon you can extrapolate other LEDs I-V graphs upto double the rated forward voltage and be safe, provided that you don't exceed rated power in average. I've also tested a 5mm THT white LED with the same setup and it behaved pretty much in a similiar way. I hope you find it useful. submitted by /u/klazera [link] [comments]

I’ve been getting a new email like this from my preferred PCB vendor almost daily. submitted by /u/fritoburritobandito [link] [comments]

All my capacitors have linked in to a ball. Guessing all the vibrations from shipping did this. submitted by /u/Whyjustwhydothat [link] [comments]

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

Serial EEPROMs from ST contain a unique 128-bit read-only ID for product recognition and tracking without requiring an extra component. Preprogrammed and permanently locked at the factory, the unique ID (UID) enables basic product identification and clone detection as an alternative to an entry-level secure element.

Initially available in 64-kbit and 128-kbit versions, the M24xxx-U series spans storage densities from 32 kbits to 2 Mbits. Each device retains its UID throughout the end-product lifecycle—from sourcing and manufacturing to deployment, maintenance, and disposal. The UID ensures seamless traceability, aiding reliability analysis and simplifying equipment repair.

These CMOS EEPROMs endure 4 million write cycles and retain data for 200 years. They operate from 1.7 V to 5.5 V and support 100-kHz, 400-kHz, and 1-MHz I2C bus speeds. The devices offer random and sequential read access, along with a write-protect feature for the entire memory array.

The 64-kbit M24C64-UFMN6TP is available now, priced from $0.13, while the 128-kbit M24128-UFMN6TP starts at $0.15 for orders of 10,000 units. Additional densities will be released during the second quarter of 2025.

STMicroelectronics

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

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Diodes’ AH4930Q sensor detects magnetic fields along the X, Y, and Z axes for contactless rotary motion and proximity sensing. As the company’s first automotive-compliant 3D linear Hall effect sensor, the AH4930Q is well-suited for rotary and push selectors in infotainment systems, stalk gear shifters, door handles and locks, and power seat adjusters.

Qualified to AEC-Q100 Grade 1, the AH4930Q operates over a temperature range of -40°C to +125°C and integrates a 12-bit temperature sensor for accurate on-chip compensation. It also features a 12-bit ADC, delivering high resolution in each measurement direction, down to 1 Gauss per bit (0.1 mT) for precise positional accuracy. An I2C interface supports data reading and runtime programming with host systems up to 1 Mbps, enabling real-time adjustments.

The sensor features three operating modes and a power-down mode with a consumption of just 9 nA. Its modes balance power and data acquisition, ranging from a low-power mode at 13 µA (10 Hz) to a fast-sampling mode at 3.8 mA (3.3 kHz) for continuous measurement. Operating with supply voltages from 2.8 V to 5.5 V, the AH4930Q offers a 10-µs wake-up time, 4-µs response time, and wide bandwidth for fast data acquisition in demanding applications.

Supplied in a 6-pin SOT26 package, the AH4930Q costs $0.50 each in lots of 1000 units.

AH4930Q product page

Diodes

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

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Keysight AI (KAI) Data Center Builder emulates AI workloads without requiring large GPU clusters, enabling evaluation of how new algorithms, components, and protocols affect AI training. The software suite integrates large language model (LLM) and other AI model workloads into the design and validation of AI infrastructure components, including networks, hosts, and accelerators.

KAI Data Center Builder simulates real-world AI training network patterns to speed experimentation, reduce the learning curve, and identify performance degradation causes that real jobs may not reveal. Keysight customers can access LLM workloads like GPT and Llama, along with popular model partitioning schemas, such as Data Parallel (DP), Fully Sharded Data Parallel (FSDP), and 3D parallelism.

The KAI Data Center Builder workload emulation application allows AI operators to:

• Experiment with parallelism parameters, including partition sizes and distribution across AI infrastructure (scheduling)
• Assess the impact of communications within and between partitions on overall job completion time (JCT)
• Identify low-performing collective operations and pinpoint bottlenecks
• Analyze network utilization, tail latency, and congestion to understand their effect on JCT

For more information on the KAI Data Center Builder, or to request a demo or price quote, click the product page link below.

KAI Data Center Builder product page

Keysight Technologies 

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Leveraging Menlo’s Ideal Switch technology, the MM5230 RF switch minimizes insertion loss and provides high power handling in a chip-scale package. The device is a SP4T switch that operates from DC to 18 GHz, which extends to 26 GHz in SPST Super-Port mode. Designed for high-power applications, it supports up to 25 W continuous and 150 W pulsed power.

The MM5230 is well-suited for defense and aerospace, medical equipment, test and measurement, and wireless infrastructure applications. With an on-state insertion loss of just 0.3 dB at 6 GHz, it minimizes signal degradation, ensuring high performance in sensitive systems, low-loss switch matrices, switched filter banks, and tunable filters. Additionally, the MM5230 provides high linearity with a typical IIP3 of 95 dBm, preserving signal integrity for smooth communication or data transfer.

The switch’s 2.5×2.5-mm chip-scale package eases integration into a wide range of systems and conserves valuable board space. Additionally, the Ideal Switch fabrication process enhances reliability and endurance. 

The MM5230 RF switch is available for purchase through Menlo Microsystems’ distributor network.

MM5230 product page 

Menlo Microsystems 

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Kioxia, AIO Core, and Kyocera have prototyped a PCIe 5.0-compatible broadband SSD with an optical interface. The trio is developing broadband optical SSD technology for advanced applications requiring high-speed, large-volume data transfer, such as generative AI. They will also conduct proof-of-concept testing to support real-world adoption and integration.

Combining AIO Core’s IOCore optical transceiver and Kyocera’s OPTINITY optoelectronic integration module, Kioxia’s prototype delivers twice the bandwidth of the PCIe 4.0 optical SSD demonstrated in August 2024. Replacing electrical wiring with an optical interface increases the allowable distance between compute and storage devices in next-generation green data centers while preserving energy efficiency and signal integrity. 

The prototype was developed under Japan’s “Next Generation Green Data Center Technology Development” project (JPNP21029), part of NEDO’s Green Innovation Fund initiative. The project aims to reduce data center energy consumption by over 40% through next-generation technologies. Kioxia is developing optical SSDs, AIO Core is working on optoelectronic fusion devices, and Kyocera is creating optoelectronic packaging.

No timeline for commercialization has been announced.

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

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and develops and manufactures high-power industrial blue lasers — is to unwind its $2m share exchange agreement and partnership announced on 28 February with HUMBL Inc (a strategic holding company with a focus on Brazil). After a thorough strategic review, NUBURU’s management has determined that continuing the partnership no longer aligns with its core business objectives...

First time soldering on copper clad. Negative feedback configured 10 V/V OpAmp submitted by /u/WirelessEthernett [link] [comments]

Figure 1’s negative constant current source has been a textbook application for the LM337 regulator forever (or thereabouts). It precisely maintains a constant output current (Iout) by forcing the OUTPUT pin to be the negative Vadj relative to the ADJ pin. Thus, Iout = Vadj/Rs. 

Figure 1 Classic LM337 constant negative current source where Iout ≃ Vadj/Rs = 1.25/Rs.

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

It has worked well for half a century despite its inflexibility. I say it’s inflexible because the way you program Iout is by changing Rs. It may be hard to believe that a part so mature (okay old) as the 337 might have any new tricks left to learn, but Figure 2 teaches it one anyway. It’s a novel topology with better agility. It leaves the resistors constant and instead programs Iout with the (much smaller) control current (Ic). 

 

Figure 2 Rc typically >100Rs, therefore Ic < Iout/100 and Iout ≃ -(1.25 – (IcRc))/Rs.

Rc > 100Rs allows control of current of Iout with only milliamps of Ic. Figure 3 shows the idea fleshed out into a complete PWM-controlled 18 V, 1 A grounded-load negative current source.

Figure 3 An 18 V, 1 A, PWM-programmed grounded load negative current source with a novel LM337 topology. With this topology, accuracy is insensitive to supply rail tolerance. The asterisked resistors are 1% or better and Rs = 1.25 Ω.

The PWM frequency, Fpwm, is assumed to be 10 kHz or thereabouts, if it isn’t, scale C1 and C3 appropriately with:

C1 = 0.5µF*10kHz/Fpwm and,

C3 = 2µF*10kHz/Fpwm.

The resulting 5-Vpp PWM switching by Q1 creates a variable resistance averaged by C1 to R4/Df, where Df = the 0 to 1 PWM duty factor. Thus, at Z1’s Adj point:

Ic = 0 to 1.24V/R4 = 3.1 mA,

The second-order PWM ripple filtering gives a respectable 8-bit settling time of 6 ms with Fpwm = 10 kHz.

Z1 servos the V1 gate drive of Q3 to hold the FET’s source at its precision 1.24-V reference and then level shift the resulting Ic to track U1’s ADJ pin. Also summed with Ic is Iadj bias compensation (1.24V/20k = 62µA) provided by R2.

This term zeros out U1’s typical Iadj and cuts its max 100 µA error by 60%. Meanwhile, D1 insures that Iout is forced to zero when 5 V drops by saturating Q2 and making Ic large enough to turn U1 completely off, thus protecting the load.

About the 1N4001 daisy chain: There’s a possibility of Iout > 0 at Ic = max and a resulting reverse bias of the load; some loads might not tolerate this. The 1N4001s block that, and also provide bias for the power-down cutoff of Iout when +5-V rail shuts down.

Note that the accuracy of IcRc = Vadj is assured by the match of the Rc resistors and precision of the Z1 and U1 internal references. It’s therefore independent of the tolerance of the +5-V rail, although it should be accurate to ± 5% for best PWM ripple suppression. Iout is linear with PWM duty factor Df = 0 to 1:

Iout = -1.25 Df/Rs

If Rs = 1.25 Ω, then Iout(max) = 1 A. 

Note that U1 may have to dissipate as much as 23 W if Iout(max) = 1 A and the load voltage is low. Moral of the story: Be generous with the heatsink area! Also, Rs should be rated for a wattage of 1.252/Rs.

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

• 0 V to -10 V, 1.5 A LM337 PWM power DAC
• Classic 3-leg adjustable regulators have a shunt mode? Who knew?
• 1-A, 20-V, PWM-controlled current source
• Symmetrical 10 V, 1.5 A PWM-programmed power supply
• A safe adjustable regulator

The post A negative current source with PWM input and LM337 output appeared first on EDN.

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

Courtesy: Arrow Electronics

Artificial Intelligence has rapidly become an innovative driver across industries, enabling everything from autonomous vehicle development to real-time healthcare diagnostics. However, as AI models grow in both complexity and scale, power and thermal management concerns are also rising. Companies must meet and overcome these challenges to help ensure sustainable and efficient AI operations.

Why Power and Thermal Management Matter in AI

AI systems are, at their core, computationally intensive and require large amounts of processing power to train and deploy models effectively. This intense compute power results in increasing amounts of energy consumption and heat. Without addressing these issues, organizations are at risk of:

1. System Overheating: Excessive heat can degrade hardware performance, cause unexpected failures, and shorten the lifespan of critical infrastructure.
2. Operational Inefficiencies: Ineffective cooling strategies lead to higher energy costs, increased maintenance needs, and reduced system reliability.
3. Environmental Impact: Escalating energy consumption increases carbon footprints, counteracting sustainability goals and regulatory requirements.

The Scope of Power and Thermal Challenges

While AI is fundamentally a compute-heavy task, recent trends exacerbate heat and thermal concerns for artificial intelligence systems. Some of these trends include:

• Growing Compute Density: As AI models become larger and more complex, data centers must meet rack densities exceeding 50kW—a significant jump from traditional capacities.
• Edge Deployments: Deploying AI at the edge requires compact, energy-efficient systems that can handle extreme environmental conditions while still performing at high levels.
• Diverse Workloads: AI includes applications such as computer vision, NLP, and generative models, each with its own unique performance and cooling needs.

These challenges require a combination of advanced technologies and strategic planning to maintain performance and sustainability.

Strategies for Addressing Thermal Challenges

Liquid Cooling

While liquid cooling is not a new concept, it has seen rapid growth and adoption to combat heat and thermal issues in AI systems, especially at the edge. Unlike traditional air-based systems, liquid cooling directly removes heat from critical components, offering:

• Improved Efficiency: Direct-to-chip cooling systems enhance heat dissipation, allowing servers to handle workloads exceeding 50kW per rack without compromising reliability.
• Scalability: Liquid cooling is suitable for data centers, edge deployments, and hybrid environments and supports the growing compute density required for AI applications.
• Sustainability: Reduced reliance on energy-intensive air-cooling systems contributes to lower carbon emissions and aligns with environmental regulations.

Arrow’s Intelligent Solutions business works with leading vendors and leverages advanced liquid cooling technologies, such as rear-door heat exchangers and immersion cooling, to provide tailored solutions that address the specific needs of OEMs and ISVs. These solutions enhance system stability, extend lifespan, and significantly lower energy consumption.

Innovations in Passive Cooling

In addition to active cooling systems, advancements in passive cooling techniques, such as optimized airflow management and heat pipe technology, are becoming increasingly relevant. Heat pipe cooling, in particular, offers numerous advantages for AI systems, including exceptional thermal efficiency, uniform heat distribution across the system, minimal maintenance needs, a lightweight design, and effective cooling for high-density computing components.

The Role of Right-Sized Computing

As seen in Ampere’s innovative GPU-free AI inference solutions, right-sized computing aligns hardware capabilities with workload requirements. This approach minimizes energy waste and reduces costs and operational complexity. Ampere’s cloud-native processors, for instance, deliver:

• Enhanced Efficiency: Up to 6.4x greater AI inference performance compared to traditional systems.
• Lower Power Consumption: Optimized for sustainability, these processors allow organizations to achieve more with less energy.
• Broad Application Support: Ampere’s solutions excel across diverse AI workloads from computer vision to natural language processing.

Integrating Ampere’s technology with Arrow’s thermal management expertise helps ensure that customers receive end-to-end solutions optimized for performance, cost, and sustainability.

Holistic Approaches to AI Deployment

In addition to hardware choice and usage strategies, more comprehensive approaches to AI deployment can help mitigate concerns over these systems’ significant energy usage and heat generation and their general sustainability.

Predictive Maintenance

Predictive maintenance tools can monitor system performance, identify potential thermal issues before they escalate, and reduce downtime. Our engineering team can help develop comprehensive maintenance frameworks that leverage machine learning for operational continuity.

Energy-Efficient Architectures

Transitioning to energy-efficient architectures, such as those based on ARM or custom-designed accelerators, can significantly reduce power consumption. Our ecosystem of cutting-edge suppliers enables OEMs to access these transformative technologies.

Lifecycle Management

Lifecycle management is critical for achieving sustainable AI deployments. Strategies such as hardware recycling, second-life battery integration, and modular system upgrades can extend the usability of AI infrastructure while minimizing waste.

Moving Towards Sustainable AI Deployment

Beyond addressing immediate thermal and power challenges, OEMs must focus on long-term sustainability. Strategies include:

• Integrated Design Approaches: Collaborating across hardware, software, and cooling technology providers to create cohesive systems that meet evolving demands.
• Regulatory Compliance: Adhering to emerging global standards for energy efficiency and environmental responsibility.
• Customer Education: Empowering end-users with tools and knowledge to optimize their AI deployments sustainably.

Arrow is at the forefront of these efforts, providing OEMs with the tools and expertise to navigate the complexities of power and thermal management in AI. By leveraging our network of robust technology collaborations, engineering expertise, and a commitment to innovation, Arrow’s Intelligent Solutions business helps organizations stay ahead in the race for sustainable AI solutions.

Conclusion

The demands of AI are pushing the boundaries of power and thermal management, but solutions like liquid cooling, passive cooling innovations, and right-sized computing are paving the way for a more sustainable future.

In collaboration with cutting-edge technology providers, Arrow helps you build a comprehensive strategy that balances performance, cost, and environmental responsibility. With these tactics, organizations can deploy their AI solutions in an efficient, reliable, and scalable way.

The post Power and Thermal Management Concerns in AI: Challenges and Solutions appeared first on ELE Times.

For full-year 2024, molecular beam epitaxy (MBE) system maker Riber S.A. of Bezons, France has reported revenue of €41.2m, up 4.8% on 2013’s €39.3m...

Infineon Technologies AG bolsters its global and regional market leadership positions in automotive semiconductors, including its very strong position in microcontrollers. According to the latest market research from TechInsights , Infineon achieved a market share of 13.5 percent in the global automotive semiconductor market in 2024. In Europe, the company climbed to the top spot with a 14.1 percent market share, up from second in 2023.

Infineon also strengthened its presence in North America to the second largest market participant with a 10.4 percent share, rising from last year’s number three position. The global market share in microcontrollers rose again, to 32.0 percent, increasing the lead over the second-placed competitor by 2.7 percentage points.

Furthermore, Infineon maintained its leading market positions in the largest market for automotive semiconductors, China, with a 13.9 percent market share as well as in South Korea with a 17.7 percent market share. In Japan, the company confirmed its strong second place with a share of 13.2 percent. In total, the global automotive semiconductor market accounted for US$ 68.4 billion in 2024 – a slight decline of 1.2 percent compared to US$ 69.2 billion in 2023.

“We are the global number one in automotive semiconductors for the fifth consecutive year and we are equally successful across the world. For the first time in our history, Infineon is among the top two automotive semiconductor companies in every region,” said Peter Schaefer, Executive Vice President and Chief Sales Officer Automotive at Infineon. “This global success is a token of our strong product portfolio, outstanding customer support and our dedication to the specific needs of our customers.”

Infineon’s semiconductors are essential in driving the digitalization and decarbonization of vehicles to make them clean, safe and smart. They serve all major automotive applications such as driver assistance and safety systems, powertrain and battery management as well as comfort and infotainment features. A key focus is to support the evolution of electrical/electronic vehicle architectures towards more centralized zonal designs as the basis for software-defined vehicles. This requires state-of-the-art connectivity and data security, smart power distribution and real-time computing power.

“It is the fifth time in a row that the ‘TechInsights Automotive Semiconductor Vendor Market Share Ranking’ confirms the Infineon lead, with microcontrollers largely contributing to this success,” said Asif Anwar, Executive Director of Automotive End Market Research at TechInsights. “Semiconductors for advanced driver assistance systems, especially SoCs and memories, were among the best performing product categories. Infineon did exceptionally well in microcontrollers used in advanced driver assistance systems and many other applications. With an increase of 3.6 percentage points to a 32.0 percent market share, Infineon has held up well in the automotive microcontroller market, which decreased by 8.2 percent year-over-year.” TechInsights, “2024 Automotive Semiconductor Vendor Market Share”, March 2025.

The post Infineon bolsters global lead in automotive semiconductors with number one position in microcontrollers driving this success appeared first on ELE Times.