Збирач потоків

yeah see i'm new to this electronics stuff i've only had this kit for like a day or two, like a dumbass i fried the E segment on that little 7 seg but i really wanted to still use it for something, so i grabbed some LEDs and made one on a mini breadboard that even shares the same pinout as a normal 7 seg (see those 5 empty holes on the edge of the bottom left of the board, those are the bottom pins on a normal 7 seg and the top ones are of course on the other side of the board, and since there's no DP i just made it a grounding pin) i wanted to make it all the same color LEDs but my little dinky starter kit here only has 5 of each color so i just did what i could i've got a video of me testing it, i wanted to post that too but the sub wouldn't let meeeee submitted by /u/AftonsAssCheeks [link] [comments]

submitted by /u/nexo420x [link] [comments]

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

After using square grid papers and Figma projects I remembered we can create web apps with the help of AI assistants and I created a web app to help with coming up neatly organized breadboard wiring. submitted by /u/Big-Rent6905 [link] [comments]

📰 Газета "Київський політехнік" № 19-20 за 2026 (.pdf) Інформація КП пт, 05/22/2026 - 20:33

Medical imaging has become one of the most critical pillars of modern healthcare to provide insights into diagnosis, treatment planning, and disease management. However, the very success of imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) has created a growing challenge of data and decision-making. As imaging generates more information to interpret, artificial intelligence helps to improve these systems by supporting faster, smarter workflows for higher-accuracy diagnoses.

The volume of imaging studies has increased substantially over the past decade, putting additional pressure on the shortage of radiologists. At the same time, technological advances in scanner hardware have enabled the acquisition of thinner slices and higher-resolution images, with a single CT or MRI exam consisting of hundreds or thousands of images.

In clinical settings, the challenge is not whether scans have sufficient data but whether the health system can reconstruct, review, quantify, and interpret the data fast enough to support timely clinical decisions. We look at the use of AI and some of the popular deep-learning models in medical imaging and diagnostics while also examining how AI is being integrated across the imaging workflow.

AI enhances medical imaging systems such as CT or MRI scanners by supporting faster, smarter workflows for higher-accuracy diagnoses. (Source: Adobe Stock) AI across the medical imaging pipeline

AI is moving medical imaging and diagnostics from early-generation concepts and narrow automation toward broader integration across the imaging pipeline. The integration of AI is augmenting a wide range of tasks, from the moment an exam is ordered to the final clinical interpretation, to improve speed, accuracy, consistency, and efficiency. This approach addresses the critical bottleneck in the modern imaging workflow, turning a linear and often manual process into a more optimized, data-driven, and intelligent system.

The influence of AI begins even before a single image is acquired. This includes administrative and logistical steps that are important for optimization. For example, natural-language-processing models can analyze a patient’s clinical history and the reason for an exam within the electronic health record to help automate the selection of the most appropriate imaging protocol.

During the acquisition stage, AI contributes to image quality and efficiency. In CT, AI can automate and optimize scan ranges and radiation dose parameters based on the patient’s specific anatomy to ensure diagnostic-quality images are obtained at the lowest possible radiation exposure.

Image reconstruction is another impactful application of AI. Deep-learning reconstruction has changed this process. These models are trained on a large dataset of high-quality images to produce images with significantly lower noise and higher signal-to-noise ratio from under-sampled or low-dose raw data. For MRI, this means scan times can be reduced by up to 75% in some cases, without sacrificing image quality.

Once the images are created, AI is used for analysis and interpretation. In this phase, it helps radiologists in extracting clinically relevant information. Automated segmentation is the key task in which AI algorithms delineate anatomical structures, organs, or pathologies with high precision. This is an important prerequisite for quantitative analysis and is used to accelerate standardized assessment workflows, such as for the prostate imaging reporting and data system.

After the segmentation, AI tools for detection and triage can screen images for critical findings, such as intracranial hemorrhage, pulmonary embolism, or large vessel occlusions in stroke patients. However, AI in this setting is changing the order, speed, and consistency of review. A triage algorithm can bring a suspected emergency case to the top of the queue, while the radiologist remains responsible for confirming the findings, considering clinical context, and issuing the final report.

AI models in modern imaging diagnostics

The growth in powerful deep-learning architectures today serves as the engine for modern medical AI to perform complex tasks such as detecting minute pathological changes, precisely segmenting anatomical structures, and fusing information from different clinical sources.

Convolutional neural networks (CNNs) have become the go-to architecture for most AI medical imaging applications, especially in radiology. Their design is inspired by the human visual cortex and is well-suited for processing grid-patterned data such as images.

While CNNs are useful for classification tasks, medical imaging requires a more granular understanding of spatial information, such as tracing the boundaries of an organ or tumor. This task involves assigning a class label to every pixel in an image. For this purpose, encoder-decoder architectures, most popularly the U-Net, have become the de facto standard.

The U-Net design addresses this challenge by combining semantic context with low-level, high-resolution spatial information. The architecture has two main components: the encoder and the decoder. As the image data goes deeper into the encoder, the spatial resolution decreases, but the number of feature channels increases. This allows the architecture to capture context-rich information from the image.

The decoder’s role is to take the compressed, high-level feature representation from the encoder and progressively up-sample it back to the original image resolution to generate a pixel-wise segmentation map. It achieves this by using a learned transposed convolution to increase spatial dimensions.

The U-Net architecture uses skip connections that create a pathway for information to flow from the encoder to the decoder at corresponding levels of resolution. This fusion provides the decoder with the fine-grained spatial details that were lost during the down-sampling.

This is necessary in many diagnostic cases that are not a simple classification problem. The model not only needs to identify that a tumor, lesion, or abnormality is present, but it also outlines the boundary, calculates volume, compares change over time, or separates healthy tissue from pathology. This pixel-level requirement is why encoder-decoder architectures have become key to segmentation workflows.

The success of this concept has led to variants designed to further improve performance. U-Net++, for example, introduces nested and dense skip pathways to reduce the semantic gap between the encoder and the decoder feature maps, while Attention U-Net integrates attention mechanisms that allow the model to focus on the most relevant image regions. Other advanced versions, such as nnU-Net, provide a self-configuring framework that automatically adapts the network architecture and preprocessing steps for any given segmentation task.

However, CNNs have limitations in modeling long-range dependencies and global context within an image. This led to the exploration of Vision Transformers in medical imaging. Transformers can model relationships across wider image regions, which is useful for tasks in which pathology, anatomy, and clinical context are distributed across a larger field of view.

But at the same time, they face a domain gap between the natural images on which many of these models are pretrained and the unique characteristics of medical images. The black-box nature of these models raises concerns about interpretability, which is important for clinical trust and high-stakes decision-making.

How AI improves medical insights

Integrating AI into medical imaging brings enormous improvements in speed and operational efficiency. By automating time-consuming tasks at multiple stages, AI targets the workload pressures and delays that modern radiology faces. This results in faster diagnoses and more timely patient intervention.

AI is also increasing the accuracy of the diagnostic quality of medical imaging by reducing variability. Human interpretation is always subject to limitations because of fatigue, perceptual errors, and inter-reader variability, whereby different radiologists may interpret the same image differently. AI provides a more powerful set of tools to augment human perception.

AI systems are particularly strong in pattern-recognition tasks and have demonstrated the ability to detect subtle abnormalities that may be missed by the human eye. In lung cancer screening with CT, for example, studies have shown that AI algorithms can achieve a nodule-detection sensitivity exceeding 95% for nodules of 4 mm or larger.

In lung cancer screening with CT scans, AI algorithms can improve nodule-detection sensitivity and reduce the risk of a missed diagnosis. (Source: Adobe Stock)

A research study shows that AI detected 8.4% more lung nodules in patients with complex lung diseases. Similarly, in mammography, AI models have performed comparably to human experts in detecting breast cancer in certain validation studies. These systems function as a highly effective second reader that can help radiologists focus on potential concerns and reduce the risk of a missed diagnosis.

In addition, radiomics is built upon the foundation of AI-driven quantification. For example, radiomic features extracted from pre-treatment CT images have been used to predict survival in lung cancer patients, while signatures from MRI scans have shown a correlation with recurrence risk in glioblastoma patients.

What’s next

The current advancements are setting the stage for a future in which AI will be deeply integrated into diagnostics and patient care. One of the most important future directions is the maturation of multimodal AI and foundation models for a wider range of data types, including imaging, genomics, proteomics, digital pathology, clinical notes, and even real-time physiological data from wearable sensors.

The future of AI is likely to be one of human-AI collaboration. AI will handle the data-intensive tasks of detection, measurement, and quantification, while radiologists focus on higher-order tasks of complex synthesis and clinical correlation.

The post AI-powered medical imaging: Turning data into faster diagnoses appeared first on EDN.

Surface-mount polymer positive temperature coefficient (PPTC) devices are widely used resettable protection components in modern electronic systems. These devices are commonly selected when designers require automatic recovery after a fault, which makes them useful in consumer electronics, telecommunications equipment, industrial systems, and medical devices.

Despite their widespread use, detailed SPICE models for these components remain uncommon. Datasheets typically provide static electrical characteristics, but dynamic models that allow designers to simulate real operating conditions are rarely available.

Yet many practical operating scenarios can be reproduced effectively through simulation. Examples include overcurrent or short to ground events on a USB 5-V supply line, short circuit faults on a lithium-ion battery, or motor stall protection under varying ambient temperatures.

The value of such simulations is clear: they illustrate the consequences of complex overcurrent conditions or potential failure scenarios before hardware is built. In particular, modern simulation tools such as QSPICE make it possible to evaluate large parameter sets quickly, allowing designers to explore how protection devices behave under a wide range of electrical and thermal conditions.

However, modeling these devices accurately raises important questions. Designers must consider whether the model includes the large tolerances inherent to these components, whether ambient temperature influences the results, whether device aging after a trip event is represented, and how trip-time curves versus fault current are incorporated.

All these concerns are valid. If models are to be used effectively, these factors must be addressed so that simulations produce results that reflect real-world behavior.

PPTC SPICE model genesis and thermal fundamentals

From a functional perspective, a PPTC device can be approximated as a thermal mass with a baseline electrical resistance at ambient temperature. When a fault current flows through the device, resistive heating raises the internal temperature. Once the temperature reaches the trip temperature (Ttrip), the device undergoes a rapid increase in resistance. This sharply reduces the current and stabilizes the device at an elevated temperature.

The heat balance of the system can be described by the following equation:

In this equation:

• Cth represents thermal capacitance
• dT/dt represents rate of temperature change
• D represents thermal conductivity constant
• Tamb and T represent ambient and device temperature
• R(T) represents temperature-dependent resistance
• I represents fault current

Before tripping, the resistance can be approximated as a linear function of temperature using a temperature coefficient:

From equations (1) and (2), we derive and simplify:

In fact, here 𝑇𝑎𝑚𝑏 = T25 = 25°C

By integrating equation (3) from t = o to ttrip and from T = 25°C to T= Ttrip, we get:

When the theoretical curve derived from this model is plotted and compared with measured device data, differences become apparent, as shown in Figure 1.

Figure 1 The theoretical trip-time versus fault-current curve is compared with representative measured data. Source: Vishay

The simplified single thermal block model reveals clear limitations at both low and high fault currents. At high currents, the ideal slope of ln (trip time) versus ln (fault current) should be −2, because trip time is inversely proportional to dissipated power, which scales with the square of current.

In practice, the observed slope is significantly flatter. This indicates that additional physical mechanisms influence the thermal response at high current levels. At low fault currents, the theoretical curve rises more steeply than measured results. This discrepancy arises because a PPTC cannot be modeled as a single thermal mass dissipating heat through a single thermal resistance.

Additional factors include:

• Heat dissipation through solder joints and PCB copper
• Delayed thermal propagation in the polymer structure
• Resistance changes caused by device aging

Practical SPICE modeling of PPTC devices

Under lower current conditions, the thermal behavior of a PPTC device can be represented more accurately using a multi-stage thermal network. An example of this approach is shown in Figure 2, where a three-stage thermal Cauer network represents heat flow inside the device.

Figure 2 Here is an example of a three-stage thermal Cauer network representing heat flow within a PPTC device installed in an application circuit. Source: Vishay

In this model:

• Tpptc represents temperature of internal polymer material
• Telectrode represents temperature of device electrodes
• Tpcb represents temperature at PCB solder joint
• Tambient represents far-field ambient temperature

Each stage includes thermal resistance and thermal capacitance elements that model heat transfer between nodes.

The simulations described in this work were implemented in QSPICE, which provides powerful parameter-sweep capabilities. This allows multiple device and environmental parameters to be varied simultaneously, enabling thousands of simulations to be performed in a short time.

Once the thermal parameters are adjusted to match measured trip-time behavior, the simulated results closely reproduce measured data. The comparison between simulation and measurement is shown in Figure 3.

Figure 3 Simulated trip-time versus fault-current characteristics is compared with measured data. Source: Vishay

Remaining deviations are largely attributed to measurement conditions and inherent device tolerances. Nevertheless, the multi-stage thermal model provides a substantial improvement over the simplified single-block model.

Additional model features can also be incorporated, including:

• Resistance aging following a trip event
• Failure behavior when applied voltage exceeds rated limits
• Ambient temperature dependence

Influence of ambient temperature

Ambient temperature has a strong influence on PPTC device behavior. The simulation circuit used to investigate this behavior is shown in Figure 4:

Figure 4 An example application circuit is used to evaluate PPTC behavior under surge conditions at different ambient temperatures. Source: Vishay

Two successive voltage pulses are applied under varying temperature conditions. The resulting waveforms are shown in Figure 5.

Figure 5 Simulation results display device response to successive voltage pulses at different ambient temperatures. Source: Vishay

Here, several effects can be observed:

• Trip time varies with ambient temperature
• Device resistance increases following the first trip event
• Excessive voltage may force the device into a short circuit state

Because of this temperature dependence, datasheets typically provide thermal derating curves describing how allowable current varies with ambient temperature. An example of such a curve is shown in Figure 6.

Figure 6 A typical thermal derating curve illustrates how allowable hold current decreases as ambient temperature increases. Source: Vishay

The behavior behind this curve can also be reproduced through simulation. The circuit used for this analysis is shown in Figure 7, and the corresponding simulation results appear in Figure 8.

Figure 7 A simulation circuit is used to evaluate device behavior under varying load resistance and ambient temperature conditions. Source: Vishay

Figure 8 Simulation results display trip-time behavior as temperature and load conditions change. Source: Vishay

From these results, the variation of fault current as a function of ambient temperature can be extracted. The resulting simulated derating curve is shown in Figure 9.

Figure 9 The simulated thermal derating curve is derived from the SPICE model. Source: Vishay

Application simulations

Once realistic device models are available, they can be used to simulate complete application circuits.

USB power supply protection

A typical USB protection circuit is shown in Figure 10.

Figure 10 The application circuit illustrates a USB power supply protected by a PPTC device. Source: Vishay

Simulation results for plug-in disturbances and short circuit events are shown in Figure 11.

Figure 11 Simulation results show device response during plug-in events and short circuit disturbances. Source: Vishay

At 100 ms, a load short circuit is introduced. The PPTC device transitions from a low resistance state to a high resistance state, limiting the current.

Motor stall protection

An example motor protection circuit is shown in Figure 12.

Figure 12 The circuit example of a small electric motor is protected against stall conditions using a PPTC device. Source: Vishay

Simulation results demonstrating the stall event appear in Figure 13.

Figure 13 Simulation results show motor current increase and protection device trip during a stall event. Source: Vishay

When the motor stalls, I(Vspeed) drops to 0, and the current rises sharply. The protection device heats and transitions to a high resistance state, limiting current and preventing thermal damage.

Lithium-ion battery short circuit protection

The simulated battery protection circuit is shown in Figure 14, with results presented in Figure 15.

Figure 14 The simulation circuit represents a lithium-ion battery system with short circuit protection. Source: Vishay

Figure 15 Simulation results display surge current and resulting resistance increase after a load short circuit. Source: Vishay

At lower ambient temperatures, the device responds more slowly, allowing higher surge currents before tripping.

QSPICE modeling upside

The SPICE modeling approach described here demonstrates that realistic PPTC device behavior can be reproduced by fitting model parameters to datasheet characteristics and incorporating accurate thermal representations.

Using a simulation environment such as QSPICE enables designers to explore these behaviors efficiently through extensive parameter sweeps and transient analysis. The models can account for:

• Resistance tolerance
• Aging effects
• Ambient temperature dependence
• Overvoltage behavior

By incorporating these models into full application simulations, designers gain insights that cannot be obtained from datasheets alone. Nevertheless, simulation should always be complemented by laboratory validation before releasing a design into production.

Alain Stas is head of product marketing for non-linear resistors at Vishay.

Related Content

• QSPICE: Transient Domain Analysis
• QSPICE Simulator for Electronic Circuits
• QSPICE: A Simulation Tool for Power Circuits
• QSPICE: Introduction to the Simulation Environment
• QSPICE Revolutionizes Power, Analog Device Circuit Simulation

The post From specification to simulation: Modeling PPTC devices in QSPICE appeared first on EDN.

Вітаємо КПІшників — переможців Міжнародного конкурсу студентських наукових робіт «Black Sea Science 2026» kpi пт, 05/22/2026 - 16:50

КПІ ім. Ігоря Сікорського та ENSTA Paris розширюють партнерство kpi пт, 05/22/2026 - 16:47

❤️ Запрошуємо на онлайн лекцію “ШІ та академічна доброчесність: воля та свідомість” kpi пт, 05/22/2026 - 16:00

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

Aeluma Inc of Goleta, CA, USA says that, on 25 May at the Compound Semiconductor Week (CSW2026) conference in Kumamoto, Japan (24-28 May), its president & CEO Jonathan Klamkin Ph.D. will receive the Indium Phosphide and Related Materials (IPRM) Award for pioneering contributions to indium phosphide (InP) and gallium arsenide (GaAs) photonic and electronic materials and devices, and their heterogenous integration on mismatched substrates...

Aeluma Inc of Goleta, CA, USA says that, on 25 May at the Compound Semiconductor Week (CSW2026) conference in Kumamoto, Japan (24-28 May), its president & CEO Jonathan Klamkin Ph.D. will receive the Indium Phosphide and Related Materials (IPRM) Award for pioneering contributions to indium phosphide (InP) and gallium arsenide (GaAs) photonic and electronic materials and devices, and their heterogenous integration on mismatched substrates...

A clever millimeter-wave lens enables a high-speed, backscatter-powered GHz-band link.

Wireless system designers are often asked to deliver on seemingly incompatible and contradictory goals such as supporting ultrahigh wireless data rates, and do so at ultralow power. Accomplishing this, even if possible, is a challenge which may require lots of technical “tricks”  including advanced techniques, custom components, and more.

Now, a team at Georgia Institute of Technology (better known as Georgia Tech) has demonstrated a what they call a first-of-its-kind lens-enabled backscatter system capable of multi-gigabit data rates. At the same time, this backscatter-powered system operates using only a fraction of the power required by conventional wireless devices, therefore bringing high-speed connectivity to disbursed systems.

In general, due to power constraints, backscatter has typically been used only to send small amounts of data, most often in simple identification and sensing systems. However, the researchers say that backscatter doesn’t have to be slow and can operate at gigabit‑per‑second speeds while remaining ultra‑low power—with the right architecture. They foresee applications such as battery-free sensors embedded throughout smart cities and with digital infrastructure for a localized IoT arrangement.

Their lens-enabled backscatter system is capable of multi-gigabit data rates, reaching up to 4 gigabits per second (Gbps) (Figure 1). This dielectric lens focuses incoming millimeter-wave energy (such as from 5G systems) onto an array of tiny antenna elements, allowing both wireless energy capture and high‑speed backscatter communication within the same system.

Figure 1 A close‑up view of the device displays an array of tiny antenna elements positioned behind the lens, each modulating reflected wireless signals to enable high‑speed communication with minimal energy use. (Image source: Georgia Tech School of Electrical and Computer Engineering)

Signals at these frequencies are highly directional and sensitive to alignment; even a small misalignment can break the link. Their lens overcomes that constraint by enabling high gain and wide angular coverage simultaneously, without the need for active beam steering.

The system that can communicate over a ±55-degree field. In their tests, the researchers achieved data rates of up to 4 Gbps with sustained gigabit communication at distances of up to 20 meters, using high-order modulation schemes like those used in modern cellular networks. The system is extremely efficient and requires just 0.08 picojoules per bit. The link-budget analysis projects 1 Gbps back-scatter ranges up to 2.6 km under the 75 dBm effective Isotropic radiated power (EIRP) that is permitted in 5G millimeter-wave systems.

At the core of the millimeter-wave identification (mmID) is a broadband, cross-polarized antenna designed to operate across the full 26–30 GHz band. A broadband element is essential to sustain gigabit-level backscatter, since narrow- band operation would constrain throughput and increase distortion under high-order modulation. Cross-polarization is critical at mmWave, as a co-polarized backscatter would be masked by strong transmitter-receiving coupling from the reader.

To meet these requirements, they implemented a single-layer, capacitively coupled patch antenna designed in CST Microwave Studio and fabricated on Rogers 3003 (εr = 3:00, tan δ = 0:0013), with thickness of 0.254 mm (Figure 2).

Figure 2 a) Layout of the cross-polarized capacitive-coupled patch antenna with dimensions W = 2.85 mm, LS = 1.1 mm, and GC = 0.12 mm. b) Measured vs. Simulated S11 results of the broadband antenna. c) Layout of the FET-based mmWave modulator with dimensions R1 = 1.11 mm and R2 = 1.24 mm. d) Measured vs. Simulated S21 results of the mmWave modulator network. e) Layout of the pixel backscatter element, comprised of the broadband antenna and FET-based wireless mixer. (Image source: Nature Communications)

Gigabit backscatter at mmWave frequencies requires an antenna module that delivers both high gain and wide angular coverage. A dielectric lens provides an efficient solution, acting as a passive focusing element that concentrates incident energy onto the pixel. A key contributor to this long-range performance is the PTFE dielectric lens, which passively concentrates incident mm-wave energy onto the pixel element in a manner analogous to an optical lens. To extend the single pixel design into a practical mmID with wide angular coverage, a 25-element broadband cross-polarized pixel array was implemented, arranged in three concentric rings with a central element (Figure 3).

Figure 3 a) Proposed broadband cross-polarized mmID featuring 25 antenna elements with dimensions L1 = 26 mm, L2 = 52 mm, L3 = 78 mm, W = 90 mm, S = 13 mm, and R3 = 1.35 mm. b) Proposed PTFE lens with dimensions labeled D1 = 74 mm, D2 = 120 mm, and h = 25 mm. (Image source: Nature Communications)

The team performed extensive tests spanning a range of frequency bands, data formats, modulation types, and more, with detailed quantitative results summarized in various tables (Figure 4). They have shown that it is possible to extract GHz-range ambient-RF energy effectively using a printed lens-like antenna.

Figure 4 a) Experimental setup of the proposed lens-based mmID at incidence angles of 0∘ and 55∘ from the PoC reader. b) Block diagram of the PoC reader transmitting and receiving chain to interrogate the lens-based mmID and demodulate the gigabit per second data-rate backscatter. (Image source: Nature Communications)

The detailed project is a fascinating investigation and exploration into RF-based energy harvesting and ultralow-power systems design, without speed compromise. It is described in detail in their readable paper “Broadband multi-beam lens-assisted mmID enabling multi-gigabit backscatter data rates for next-generation wireless networks” published in Nature Communications.

What’s your view on the innovation and cleverness of this project? Is it as impressive as they maintain, or just a well-crafted and analyzed implementation of existing ideas? Is it yet another attention-getting energy-harvesting scheme with added gigahertz connectivity, or does it represent a genuine advance?

—Bill Schweber is a degreed senior EE who has written three textbooks, hundreds of technical articles, opinion columns, and product features. Prior to becoming an author and editor, he spent his entire hands-on career on the analog side by working on power supplies, sensors and signal conditioning, and wired and wireless communication links. His work experience includes many years at Analog Devices in applications and marketing, and he also developed significant mechanical-engineering insight while designing control electronics for large materials-testing systems.

Related Content

1. Nothing new about energy harvesting
2. An energy-harvesting scheme that is nearly useless?
3. Is triboelectricity the next harvesting source?
4. Tilting at MEMS Windmills for Energy Harvesting?

The post GHz-harvested power for Gbps wireless link is a double win appeared first on EDN.

In booth#544 (Hall 9) at PCIM 2026 (Expo & Conference on Power Electronics, Intelligent Motion, Renewable Energy and Energy Management) in Nuremberg, Germany (9–11 June), Navitas Semiconductor Corp of Torrance, CA, USA is showcasing its latest gallium nitride (GaN) and silicon carbide (SiC) products for AI data-center, energy and grid infrastructure, and industrial electrification...

In booth#544 (Hall 9) at PCIM 2026 (Expo & Conference on Power Electronics, Intelligent Motion, Renewable Energy and Energy Management) in Nuremberg, Germany (9–11 June), Navitas Semiconductor Corp of Torrance, CA, USA is showcasing its latest gallium nitride (GaN) and silicon carbide (SiC) products for AI data-center, energy and grid infrastructure, and industrial electrification...

On stand 304 (Hall 9) at the Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM 2026) Expo & Conference in Nuremberg, Germany (9–11 June), 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 — is showcasing its newest-generation GaN technology for humanoid robotics, drones and compact electrified motion systems...

On stand 304 (Hall 9) at the Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM 2026) Expo & Conference in Nuremberg, Germany (9–11 June), 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 — is showcasing its newest-generation GaN technology for humanoid robotics, drones and compact electrified motion systems...

Infineon Technologies AG of Munich, Germany has expanded its CoolGaN BDS 40V G3 bidirectional switch (BDS) family with two new devices, the IGK048B041S and IGK120B041S...

Infineon Technologies AG of Munich, Germany has expanded its CoolGaN BDS 40V G3 bidirectional switch (BDS) family with two new devices, the IGK048B041S and IGK120B041S...