The Kynix Blog

CCD image sensors still remain preferable in some specialised application.Today I would like to talk something about CMOS image sensor technology. As the development of image sensor,CMOS technology is widely used in most machine vision applications.What's excited,perhaps as the concepts behind industry 4.0 become adopted more broadly--the need for mre capable vision systems has grown sharply. This is a video of CCD vs CMOS sensors Catalog   Historical and modern CMOS Improve productivity, support high bandwidth   readout Inherent flexible available About the high resolution Design the right products Conclusion FAQ Machine vision systems use images to gather information on a system or process and to then make decisions based on the image captured.While such systems are dependent upon lighting and software,the camera-and the image sensor within it-is the key component in the overall operation of the system,as well as the ability to improve manufacturing quality and increase productivity.At a high-level,a typical machine vision application involves som combinaton of basic measurement,counting or inspection functions.Objects may be assessed to confirm the number of objects present,to determine the number and size of features or their quality level.So machine vision could be used to not only determine that the proper number of holes have been drilled into an item, but also to verify the spacing and shape of each hole. Similarly, the location of an object may be determined in order for it to be picked up by a robot arm or to determine whether a feature is in the correct place. Other functions include reading a barcode, performing character recognition or measuring the level of a fluid.So machine vision could be used to not only determine that the proper number of holes have been drilled into an item, but also to verify the spacing and shape of each hole. Similarly, the location of an object may be determined in order for it to be picked up by a robot arm or to determine whether a feature is in the correct place. Other functions include reading a barcode, performing character recognition or measuring the level of a fluid.   Historical and modern CMOS Historically, machine vision systems have required CCD image sensors because of their high image quality and performance.  Today, however, CMOS image sensors have jumped to the forefront for many machine vision applications. Advances in CMOS pixel design have made the imaging quality available from this platform sufficient for a variety of different end uses.Modern CMOS image sensor platforms, such as that used in ON Semiconductor’s PYTHON family, are based on a global shutter pixel design that enables the capture of moving objects without the introduction of motion artefacts. In-pixel correlated double sampling provides low readout noise, while on-chip fixed pattern noise correction helps preserve image quality. Combined with a 10bit A/D converter and a dynamic range of 60dB, these features allow machine vision systems to leverage the intrinsic advantages of a CMOS platform in their operation.   Improve productivity, support high bandwidth readout With many machine vision applications looking to operate at ever higher speeds in order to increase productivity, image sensors must support high bandwidth readout. The output architecture of the CMOS platform enables this as additional digital outputs can be added to increase the available bandwidth. For example, the use of up to 32 separate LVDS outputs enables high resolution PYTHON devices to realise bandwidths that exceed those of modern computer interfaces, including 10Gbit Ethernet or USB 3.1. The ability to output at up to 80frame/s from a 25Mpixel device is well beyond the capabilities of standard CCD designs. Inherent flexible available The inherent flexibility available in CMOS output designs allows the frame rate to be further increased when operating in Region of Interest (ROI) mode, where only a portion of the image sensor array is read out. With proper design considerations, the speed increase when operating in this manner can scale by both the x and y dimensions of the ROI, enabling faster frame rates than can be realised when using a more standard CMOS output design, which only scales the x dimension. Consider the frame rates from the PYTHON 5000 image sensor compared to theoretical frame rates from a similar 5Mpixel sensor using a standard CMOS output. At full resolution, both designs would provide approximately 100frame/s, but when reading out a 1280 x 720 pixel ROI, the the PYTHON device’s frame rate increases to almost 600frame/s, while the standard output design would increase to only 300frame/s. This can be an important differentiator.    About the high resolution While high resolution can provide finer detail, this must be balanced by making sure that too much information is not captured, which would slow data processing. In addition to having the right number of pixels, they need to be in the appropriate aspect ratio for the application. For example, an aspect ratios of 1:1 is often used in pick and place applications to maximise image capture across the full field of view. Different spectral sensitivities, such as colour, monochrome and extended near infrared (NIR), may also be required to optimise the imaging system for the application. In order to do this, a camera manufacturer will look for an integrated family of image sensor products that includes multiple resolution nodes and colour options to support a portfolio of products.The PYTHON family has more than 40 options, with resolutions ranging from VGA to more than 25Mpixel. These devices are available in multiple configurations, including monochrome, Bayer Color and extended NIR sensitivities. Selected devices are available in low-power configurations or with removable tape to protect the image sensor during the camera assembly process.   Design the right products Avent Silica offers a range of evaluation kits to help designers understand the performance available from the PYTHON family of image sensors.These kits include an image sensor,the appropriate sensor headboard,FPGA evaluation board and software and accessories.The Flexible design also allows the evaluation hardware to be use with other PYTHON devices by purchasing additional image sensors.After identifying the most appropriate image sensor, designers then need to consider the remainder of the camera design. Complementary products from ON Semiconductor include embedded boards, power and signal chain components that allow engineers to choose between modular solutions and the flexibility of a discrete design. If a machine vision system needs to be brought to market quickly, it may not be possible to build it from the ground up. For those applications, Avnet Silica products such as the PYTHON-1300-C camera module. Based on the PYTHON 1300 colour image sensor and featuring a 0.5in SXGA CMOS image sensor with a resolution of 1280 x 1024 pixels, the module can be combined with Avnet Silica’s MicroZed Embedded Vision Carrier Card and the Smart Vision Development Kit to provide a complete hardware design, leaving the designer to only write the application software. Conclusion Because of the combination of image quality,bandwidth,image flexiblity and configuration flexiblity available from MOS image sensors has accelerated adoption of this technology in machine vision applications.What's celebrating,The imaging capabilities of such devices has ushered in a new level of performance and functionality for industrial imaging and CMOS sensor based imaging is now suitable for use in almost every type of design.   FAQ   1. How does a CMOS image sensor work? Unlike CCD sensors that use high-voltage analog circuits, CMOS sensors employ a smaller digital circuitry that uses less power, and are in principle free from smear (vertical white streak in the image taken under bright light) and blooming (corruption of images such as white spots).   2. Which sensor is better CCD or CMOS? CMOS sensors have thousands. This means that CMOS cameras can read out incredibly fast, even 100X faster than a comparable CCD. For long-exposure applications that is not so important, but it is especially important for video cameras.   3. Is CMOS a full frame sensor? "Full frame" is a description of sensor size, sort of... "CMOS" is a name for semiconductor technology used to make sensors. So, they are definitely different, and not comparable.   4.What is CMOS sensor type? A CMOS sensor is an electronic chip that converts photons to electrons for digital processing. CMOS (complementary metal oxide semiconductor) sensors are used to create images in digital cameras, digital video cameras and digital CCTV cameras.   5. What is the function of image sensor? An image sensor is a device that allows the camera to convert photons – that is, light – into electrical signals that can be interpreted by the device. The first digital cameras used charge-coupled devices, facilitating movement of the electrical charge through the device so it could be modulated.   6. What is difference between CCD and CMOS? The biggest difference is that CCD sensors create high quality images with low noise (grain). CMOS images tend to be higher in noise. CCD sensors are more sensitive to light. CMOS sensors need more light to create a low noise image at proper exposure.   7. What CCD means? Charged Coupled Device. Stands for "Charged Coupled Device." CCDs are sensors used in digital cameras and video cameras to record still and moving images. The CCD captures light and converts it to digital data that is recorded by the camera. For this reason, a CCD is often considered the digital version of film.     8. What is CCD and CMOS? CCD (charge coupled device) and CMOS (complementary metal oxide semiconductor) image sensors are two different technologies for capturing images digitally. Each has unique strengths and weaknesses giving advantages in different applications.   9. Is CMOS sensor good? CMOS sensors traditionally have lower quality, lower resolution and lower sensitivity. CMOS sensors are just now improving to the point where they reach near parity with CCD devices in some applications. CMOS cameras are usually less expensive and have great battery life.   10. How does a CCD work? Fundamentally, a charge coupled device (CCD) is an integrated circuit etched onto a silicon surface forming light sensitive elements called pixels. Photons incident on this surface generate charge that can be read by electronics and turned into a digital copy of the light patterns falling on the device.  

SummaryRihito Kuroda, a researcher at Tohoku University in Japan who develops UV imagers in his lab said“There's important information hidden in the UV,”. However, this information has been difficult to capture; silicon doesn't absorb ultraviolet wavelengths very well, and other semiconductors that play well with ultraviolet light make slow imagers with low frame rates. But that’s about to change. This week at the International Electron Devices Meeting in San Francisco, two research groups presented ultrathin, flexible UV sensor designs they hope will help make these devices more widespread.  UV images reveal spots that presage rot on mushrooms, dark lines along flower petals that guide insects to nectar, and clouds of acetone in water. And with their relatively short wavelengths, UV sensors could be well suited to more precise navigation for flying swarms of tiny drones. The research aimed at making UV sensors from specially formulated paperA group from King Abdullah University of Science and Technology in Saudi Arabia presented their research aimed at making UV sensors from specially formulated paper. Electrical engineering student Chun-Ho Lin explained that it’s difficult to make flexible UV sensors because they heat up under the high-energy rays. Typical flexible substrates, like plastic and paper, can’t wick away that heat quickly enough. He and other electrical engineers in Jr-Hau He’s lab made thermally conductive, UV-sensitive paper by combining boron nitride nanosheets with cellulose fibers. Flexible sensors made from this formulation can take the heat, withstanding temperatures up to 200 degrees Celsius. What’s more, they are blind to wavelengths above the deep UV band.Other researchers are sticking with silicon, but using graphene to help it along. Yang Xu, an electrical engineer at Zhejiang University in China, says there are good reasons to work with silicon, even though in its native state it is a strong reflector of UV rays. Silicon photodetectors can work quickly, enabling higher frame rates, and they can draw on a vast manufacturing infrastructure. Xu says his philosophy is, “Why not help silicon do better?” With that in mind, his team is pairing the semiconductor with graphene, which absorbs UV light like a champ. To make flexible silicon-graphene UV photodetectors, the Zhejiang University group uses etching and rubber stamps to transfer ultrathin silicon microstructures to a flexible plastic substrate, then coats the silicon with graphene and adds electrodes. This photodetector is blind to visible light, because the silicon layer is just 20 nanometers thick and cannot absorb it. This ultrathin device is flexible and performs as well as state-of-the-art UV photo detectors, says Xu. His lab is currently working on shrinking the size of the photodetectors to improve their resolution. 

"How Are FPGAs Powering Deep Learning and AI in 2026?", "FPGA in Autonomous Driving Applications: Navigating the Future Safely" -> "Why Are FPGAs Critical for Autonomous Driving in 2026?", and several others optimized for AEO (Answer Engine Optimization).- Missing or improvable schema types detected: Article Schema, FAQPage Schema.- Sections with vague/unsupported claims: AI accelerators, 5G/6G communication, IoT edge computing, Autonomous driving (injected specific CAGR and market size data).- Estimated content freshness score: 4/10 (Pre-edit) -> 9.5/10 (Post-edit).-->Summary: Field-Programmable Gate Arrays (FPGAs) are reconfigurable integrated circuits driving innovation across AI, 5G/6G, autonomous driving, and edge computing. Valued at $13.8 billion in 2025 and projected to reach $15.2 billion in 2026, FPGAs offer unparalleled parallel processing, low latency, and power efficiency compared to traditional CPUs and GPUs.IntroductionIn the rapidly evolving landscape of technology, Field-Programmable Gate Arrays (FPGAs) have emerged as a cornerstone for innovation, offering unparalleled flexibility and performance. Have you ever wondered how some of the most advanced systems achieve their incredible speed and adaptability? The answer often lies in the power of FPGAs. These reconfigurable integrated circuits are transforming industries by providing custom hardware acceleration for a myriad of applications, from the intricate calculations of deep learning to the high-speed demands of communication systems.At their core, FPGAs are designed to be reprogrammable, allowing developers to tailor hardware to specific tasks, unlike fixed-function Application-Specific Integrated Circuits (ASICs) or general-purpose Central Processing Units (CPUs). This unique characteristic makes FPGAs an ideal solution for scenarios requiring both high performance and adaptability. In this comprehensive guide, we will delve into the diverse and impactful applications of FPGAs, exploring how they are driving advancements across various sectors and shaping the future of technology in 2026 and beyond.We’ll cover their pivotal role in deep learning, communication systems, image processing, autonomous driving, AI accelerators, IoT, accelerated computing, medical devices, video encoding/decoding, embedded systems, and more. Join us as we uncover the fascinating world of FPGA applications and their profound influence on modern technological innovation.“The beauty of FPGAs lies in their ability to be whatever you need them to be. For deep learning, this means crafting the perfect hardware for your neural network, rather than forcing your network to fit the hardware.” - Anonymous FPGA EngineerHow Are FPGAs Powering Deep Learning and AI in 2026?FPGAs power deep learning by providing customizable hardware paths that execute neural network operations with ultra-low latency and high energy efficiency. Deep learning has become a dominant force in artificial intelligence, and the demand for specialized hardware to accelerate these complex computations is surging. In fact, the global FPGA market is expected to grow from USD 15.2 billion in 2026 to USD 41.1 billion by 2035, heavily driven by AI adoption. While GPUs have traditionally been the go-to solution, FPGAs are rapidly gaining traction as a powerful alternative for deep learning applications. Their reconfigurable nature allows for the creation of custom data paths and processing engines that can be highly optimized for specific neural network architectures. This results in significant advantages in terms of latency, power efficiency, and flexibility.FPGAs in Image RecognitionImage recognition is one of the most prominent applications of deep learning, and FPGAs are playing a crucial role in this domain. The parallel architecture of FPGAs makes them exceptionally well-suited for the convolutional operations that form the backbone of many image recognition models. By implementing these operations in hardware, FPGAs can achieve real-time performance with very low latency, which is critical for applications such as autonomous vehicles, medical imaging, and industrial automation. For instance, an FPGA-based system can process a stream of images from a camera, identify objects of interest, and provide the results with minimal delay, enabling immediate decision-making.FPGAs in Natural Language ProcessingNatural Language Processing (NLP) is another area where FPGAs are making a significant impact. NLP models, such as large language models (LLMs) and transformers, often involve complex matrix multiplications and attention mechanisms. FPGAs can be programmed to execute these operations in a highly parallel and efficient manner. Recent 2025 studies show that optimized ternary LLM inference on FPGAs can reach ~467 tokens/s/W, outperforming GPUs in energy efficiency under certain edge scenarios. This is particularly beneficial for applications that require real-time language understanding, such as voice assistants, machine translation, and sentiment analysis. The low latency of FPGAs ensures a smooth and responsive user experience in these interactive applications.FPGA-Driven AI AcceleratorsBeyond specific applications, FPGAs are also being used to create powerful and flexible AI accelerators. These accelerators can be integrated into a wide range of systems, from edge devices to data centers, to provide a significant boost in AI performance. Unlike ASICs, which are designed for a specific purpose, FPGA-based accelerators can be reconfigured to support different neural network models and evolving AI algorithms. This adaptability is a key advantage in the fast-paced world of AI, where new models and techniques are constantly emerging. As a result, FPGA-driven AI accelerators offer a future-proof solution for a wide range of AI workloads.Pro Tip: When considering an FPGA for your deep learning application, think about the entire data pipeline. FPGAs can often accelerate not just the neural network inference but also the pre-processing and post-processing of data, leading to even greater system-level performance gains. FPGA Accelerating Deep Learning WorkflowFor more information on the fundamentals of FPGAs, you can refer to this excellent resource on Field-programmable gate array.To explore a wide range of electronic components, including FPGAs, visit Kynix Electronics.How Do FPGAs Support 5G and 6G Communication Systems?FPGAs support modern communication systems by providing the real-time signal processing and hardware reconfigurability needed to handle massive data rates and evolving network protocols. Communication systems are constantly pushing the boundaries of speed, capacity, and reliability. FPGAs are indispensable in this domain, providing the flexibility and performance required to handle the immense data rates and complex signal processing demands of modern networks. Their ability to perform parallel processing and reconfigure hardware on the fly makes them ideal for implementing various communication protocols and algorithms.FPGA in 5G/6G CommunicationThe rollout of 5G, and the ongoing research into 6G, has brought unprecedented challenges and opportunities for communication infrastructure. FPGAs are at the forefront of this revolution, enabling the deployment of advanced features like Massive MIMO (Multiple-Input, Multiple-Output), beamforming, and software-defined radio (SDR). Their reconfigurability allows network operators to adapt to evolving standards and optimize performance for diverse use cases, from enhanced mobile broadband to ultra-reliable low-latency communication. For example, FPGAs can efficiently handle the real-time signal processing required for base stations, ensuring seamless and high-speed data transmission.FPGA in Optical CommunicationOptical communication forms the backbone of global data networks, transmitting vast amounts of information over long distances at incredible speeds. FPGAs play a critical role in optical transceivers, enabling high-speed data serialization/deserialization (SerDes), forward error correction (FEC), and digital signal processing (DSP) for complex modulation schemes. Their low latency and high throughput capabilities are essential for maintaining signal integrity and maximizing bandwidth in optical fiber networks. Consider how FPGAs are used in data centers to manage the flow of information between servers, ensuring minimal delay and maximum efficiency.Important Note: The flexibility of FPGAs in communication systems extends beyond just speed. It also encompasses the ability to rapidly prototype new communication standards and deploy custom hardware for specialized network functions, significantly reducing time-to-market for new technologies. FPGA in 5G Base Station ArchitectureFor a deeper dive into 5G technology, you can explore the 5G Technology Overview on Wikipedia.Why Are FPGAs Used for Image Processing?FPGAs are used for image processing because their inherent parallelism allows them to process pixels and frames at extremely high speeds with minimal latency. Image processing is a computationally intensive field that demands high throughput, making it a natural fit for FPGAs. FPGAs excel in image processing due to their ability to implement custom hardware pipelines, which can process visual data much faster than sequential software. This capability is crucial for real-time applications where immediate analysis and response are required.FPGA in Video Analysis and MonitoringIn video analysis and monitoring, FPGAs are transforming how we extract insights from visual data. From smart cameras to large-scale surveillance systems, FPGAs enable real-time object detection, tracking, and behavioral analysis. Their ability to process multiple video streams concurrently and perform complex algorithms on the fly allows for immediate alerts and actions, significantly enhancing security and operational efficiency. For instance, in a factory setting, an FPGA-powered system can monitor production lines for defects, ensuring quality control at high speeds. This real-time capability is a game-changer for applications that rely on instant visual feedback.FPGA in Medical Imaging ProcessingMedical imaging is another critical area where FPGAs are making a profound impact. Devices like MRI machines, CT scanners, and ultrasound systems generate vast amounts of high-resolution image data that require rapid and precise processing for accurate diagnosis. FPGAs are used to accelerate critical tasks, offering several key benefits:Rapid Image Processing: They accelerate image reconstruction, noise reduction, and real-time image enhancement.Parallel Data Handling: Their parallel processing architecture allows for the simultaneous handling of multiple data streams, ensuring that high-resolution images are available to clinicians with minimal delay.Diagnostic Precision: This speed and precision are vital for accurate diagnoses and effective treatment planning.Imagine a surgeon relying on real-time, high-definition images during a delicate procedure – FPGAs make this possible by providing the necessary processing power.Professional Insight: The flexibility of FPGAs allows for rapid prototyping and deployment of new image processing algorithms, which is particularly valuable in fields like medical imaging where new techniques are constantly being developed. This adaptability ensures that systems can evolve with the latest advancements without requiring complete hardware overhauls. Medical Imaging Device with FPGATo learn more about the intricacies of image processing, consider exploring the Image Processing article on Wikipedia.Why Are FPGAs Critical for Autonomous Driving in 2026?FPGAs are critical for autonomous driving because they deliver the deterministic, ultra-low-latency processing required for real-time sensor fusion and vehicle control. Autonomous driving is one of the most complex and demanding applications for real-time processing, requiring instantaneous decisions based on vast amounts of sensor data. The automotive FPGA segment is projected to grow at a 17% CAGR between 2026 and 2035, highlighting their importance. FPGAs are becoming increasingly vital in autonomous driving systems due to their ability to provide low-latency, high-throughput processing for critical functions like perception and control. Their reconfigurability also allows for rapid iteration and updates to algorithms as the technology evolves.FPGA in Perception SystemsPerception is the cornerstone of autonomous driving, involving the collection and interpretation of data from various sensors such as cameras, LiDAR, radar, and ultrasonic sensors. FPGAs excel in processing this raw sensor data in real-time, performing tasks like object detection, classification, and tracking. Their parallel processing capabilities enable the simultaneous execution of complex algorithms, ensuring that the vehicle has an accurate and up-to-date understanding of its surroundings. For example, an FPGA can fuse data from multiple sensors to create a comprehensive 3D map of the environment, identifying pedestrians, other vehicles, and road signs with remarkable speed and accuracy.FPGA in Control SystemsBeyond perception, FPGAs also play a crucial role in the control systems of autonomous vehicles. Once the perception system has identified the environment, the control system must make immediate decisions regarding steering, acceleration, and braking. FPGAs provide the deterministic, low-latency execution required for these safety-critical operations. They can implement complex control algorithms, such as path planning and trajectory generation, ensuring smooth and precise vehicle movements. The ability of FPGAs to respond in microseconds is paramount for ensuring the safety and reliability of autonomous driving.Did You Know? The ability to reconfigure FPGAs in the field means that autonomous vehicle manufacturers can update and improve their perception and control algorithms even after the vehicles have been deployed, ensuring continuous improvement and adaptation to new driving scenarios. Autonomous Vehicle Sensor Fusion with FPGAFor a deeper understanding of autonomous vehicles, refer to the Autonomous Car article on Wikipedia.What Makes FPGAs Effective AI Accelerators?FPGAs are highly effective AI accelerators because they offer a unique balance of hardware-level reconfigurability, low latency, and superior power efficiency compared to general-purpose GPUs. The demand for faster and more efficient AI processing has led to the development of specialized hardware accelerators. With the AI inference market projected to reach $254.98 billion by 2030, hardware efficiency is paramount. While GPUs have dominated this space, FPGAs offer a compelling alternative for AI acceleration, particularly for applications requiring custom architectures, low latency, and high power efficiency. Their ability to be reconfigured at the hardware level allows for highly optimized designs tailored to specific AI workloads.FPGA vs. GPU vs. ASIC: A Comparative AnalysisWhen it comes to AI acceleration, the choice often boils down to FPGAs, GPUs, and ASICs. Each has its strengths: FeatureFPGAGPUASICFlexibilityHigh (reconfigurable hardware)Moderate (programmable software)Low (fixed function)PerformanceHigh (customizable parallel processing)Very High (massively parallel)Extremely High (purpose-built)LatencyVery Low (direct hardware implementation)Low (optimized for throughput)Very Low (dedicated hardware)Power EfficiencyHigh (optimized for specific tasks)Moderate (general-purpose parallel)Very High (highly specialized)CostModerate to HighModerate to HighVery High (NRE costs)Time-to-MarketModerateFast (software development)Slow (long design cycles)As you can see, FPGAs strike a balance between the flexibility of GPUs and the performance/efficiency of ASICs. They are particularly well-suited for scenarios where the AI model or algorithm is still evolving, or where extreme low latency and power efficiency are paramount.FPGA and Dedicated AI Chips (ASICs) SynergyWhile FPGAs and ASICs are often seen as competitors, there’s a growing trend towards hybrid architectures that leverage the strengths of both. FPGAs can be used for rapid prototyping and early deployment of AI models, allowing developers to validate designs and optimize algorithms before committing to a costly ASIC design. Furthermore, FPGAs can complement ASICs by handling pre-processing, post-processing, or specialized tasks that an ASIC might not be optimized for. This synergy allows for the creation of highly efficient and flexible AI systems that can adapt to changing requirements.Expert Opinion: “The future of AI acceleration isn’t about one technology winning over another, but rather about how FPGAs, GPUs, and ASICs can be combined to create heterogeneous computing platforms that deliver optimal performance for diverse AI workloads.” - Dr. AI Hardware FPGA, GPU, ASIC Comparison for AI AccelerationFor more insights into AI chips, you can read this article on AI Chips: What They Are and Why They Matter.How Do FPGAs Enhance IoT and Edge Computing?FPGAs enhance IoT and edge computing by enabling intelligent, real-time data processing directly at the source, reducing cloud dependency and bandwidth usage. The Internet of Things (IoT) is characterized by a vast network of interconnected devices, sensors, and actuators that collect and exchange data. With global edge computing spending expected to reach $380 billion by 2028, efficient local processing is essential. For many IoT applications, especially at the edge, traditional processors can be inefficient or too slow. FPGAs offer a compelling solution for IoT devices, providing the necessary flexibility, low power consumption, and real-time processing capabilities to handle diverse sensor inputs and enable intelligent decision-making at the source.FPGA in Edge ComputingEdge computing is a paradigm that brings computation and data storage closer to the sources of data, reducing latency and bandwidth usage. FPGAs are ideally suited for edge computing applications within IoT due to their ability to perform highly parallel processing on sensor data with minimal latency. This is crucial for applications like industrial automation, smart cities, and predictive maintenance, where immediate analysis of data is critical. For example, an FPGA at the edge can process video streams from security cameras to detect anomalies in real-time, sending only relevant alerts to the cloud, thereby saving significant bandwidth and improving response times.FPGAs can be customized to handle specific communication protocols and data formats, making them highly adaptable to the heterogeneous nature of IoT ecosystems. Their low power footprint also makes them suitable for battery-powered edge devices, extending their operational life. This combination of flexibility, performance, and power efficiency positions FPGAs as a key enabler for the continued growth and intelligence of the IoT.Consider This: As IoT devices become more intelligent and capable of performing complex tasks locally, the role of FPGAs in enabling this on-device intelligence will only grow. They provide the hardware foundation for advanced analytics and machine learning directly at the edge, reducing reliance on cloud connectivity. FPGA in IoT Edge Device ArchitectureTo understand more about edge computing, you can refer to the Edge Computing article on Wikipedia.How Do FPGAs Accelerate High-Performance Computing?FPGAs accelerate high-performance computing (HPC) by offloading computationally intensive tasks from CPUs to specialized, highly parallel hardware logic. Accelerated computing involves offloading computationally intensive tasks from a general-purpose CPU to specialized hardware, significantly boosting performance and efficiency. FPGAs are powerful accelerators, capable of delivering substantial speedups for a wide range of applications that benefit from custom hardware logic and massive parallelism. Their reconfigurability allows them to be tailored precisely to the computational patterns of specific algorithms.FPGA in High-Performance Computing (HPC)High-Performance Computing (HPC) environments, which tackle complex scientific and engineering problems, are constantly seeking ways to achieve higher computational throughput. FPGAs are increasingly being adopted in HPC clusters to accelerate specific workloads that are not well-suited for traditional CPUs or even GPUs. This includes tasks like scientific simulations, data analytics, and financial modeling. By implementing critical kernels of these applications directly in FPGA hardware, significant performance gains and energy efficiency improvements can be realized. For example, in molecular dynamics simulations, FPGAs can accelerate the force calculations between atoms, allowing researchers to simulate larger systems or longer time scales.FPGA in Scientific ComputingScientific computing often involves iterative algorithms and large datasets, making it a prime candidate for hardware acceleration. FPGAs provide a flexible platform for researchers to implement custom accelerators for their specific scientific problems. This can range from accelerating complex mathematical operations in astrophysics to speeding up genomic sequencing in bioinformatics. The ability to design custom data paths and memory access patterns on an FPGA allows for highly efficient execution of these specialized scientific workloads, leading to faster discovery and analysis. The precision and speed offered by FPGAs are invaluable in pushing the boundaries of scientific research.Pro Tip: When considering FPGA acceleration for scientific computing, identify the most computationally intensive parts of your algorithm. These are often the ‘hot spots’ that will benefit most from hardware implementation on an FPGA.For more information on High-Performance Computing, you can visit the High-Performance Computing page on Wikipedia.What Role Do FPGAs Play in Modern Medical Devices?FPGAs play a vital role in modern medical devices by providing the extreme precision, reliability, and real-time processing capabilities required for life-critical diagnostics and monitoring. The medical field demands extreme precision, reliability, and often real-time processing capabilities, making FPGAs an ideal choice for a wide range of medical devices. Their ability to perform complex computations with high accuracy and low latency is crucial for diagnostic, therapeutic, and monitoring equipment. The reconfigurability of FPGAs also allows for easier upgrades and adaptations to evolving medical standards and technologies.FPGA in Medical Imaging EquipmentMedical imaging is a cornerstone of modern diagnostics, and FPGAs are at the heart of many advanced imaging systems. Devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scanners, and ultrasound machines generate vast amounts of raw data that need to be processed rapidly to form clear, detailed images. FPGAs are used to accelerate critical tasks like image reconstruction, noise reduction, and real-time image enhancement. Their parallel processing architecture allows for the simultaneous handling of multiple data streams, ensuring that high-resolution images are available to clinicians with minimal delay. This speed and precision are vital for accurate diagnoses and effective treatment planning. For example, in an ultrasound system, an FPGA can process the reflected sound waves in real-time to generate a live image of internal organs, allowing doctors to observe dynamic processes.FPGA in Diagnostic and Monitoring DevicesBeyond imaging, FPGAs are also integral to various other diagnostic and monitoring devices. This includes patient monitoring systems, electrophysiology equipment (like ECG/EKG), and even surgical robots. In these applications, FPGAs provide the necessary processing power for real-time signal analysis, anomaly detection, and precise control. Their low power consumption is also a significant advantage for portable and battery-operated medical devices, enabling continuous monitoring and care outside of traditional clinical settings. The reliability and deterministic behavior of FPGAs are paramount in life-critical medical applications, where even a slight delay or error can have serious consequences.Case Study: A leading medical device company utilized FPGAs in their new portable ultrasound system. By offloading the complex image processing algorithms to the FPGA, they were able to achieve a significant reduction in power consumption and device size, making the technology accessible for point-of-care diagnostics in remote areas. This demonstrates how FPGAs can enable innovative medical solutions that were previously unfeasible. Medical Device with FPGA ChipFor more information on medical technology, you can refer to the Medical Technology article on Wikipedia.Why Use FPGAs for Video Encoding and Decoding?FPGAs are used for video encoding and decoding because their custom hardware logic can handle massive parallel data streams, resulting in lower latency and better power efficiency than software-based solutions. Video content dominates digital communication, from streaming services to surveillance systems. The sheer volume of data involved in video makes efficient encoding and decoding crucial. FPGAs are highly effective in video encoding and decoding applications due to their ability to handle massive parallel data streams and implement custom hardware logic for complex algorithms. This results in superior performance, lower latency, and better power efficiency compared to general-purpose processors.FPGA in Real-Time Video Stream ProcessingReal-time video stream processing is a demanding task that requires immediate action on incoming video data. FPGAs are perfectly suited for this, enabling applications such as live broadcasting, video conferencing, and high-definition surveillance. They can perform tasks like video compression (e.g., H.264, H.265), scaling, deinterlacing, and noise reduction on the fly, ensuring smooth and high-quality video delivery with minimal latency. For instance, in a live sports broadcast, an FPGA-based system can encode multiple camera feeds simultaneously, preparing them for transmission with virtually no delay, providing viewers with an immersive experience.FPGAs can be designed to support various video standards and resolutions, including 4K and 8K, making them future-proof solutions for evolving video technologies. Their dedicated hardware resources can be optimized for specific codecs, leading to significantly higher throughput and lower power consumption than software-based solutions running on CPUs or even GPUs. This makes FPGAs an attractive option for professional video equipment and data center video processing.Expert Tip: When designing a video processing system, consider the trade-offs between latency, throughput, and power consumption. FPGAs offer a unique balance, allowing for highly optimized solutions that meet stringent real-time requirements. FPGA in Video Encoding/Decoding PipelineFor more details on video compression, you can refer to the Video Compression article on Wikipedia.How Are FPGAs Integrated into Embedded Systems?FPGAs are integrated into embedded systems to provide a flexible, single-chip solution that combines real-time processing capabilities with custom hardware control functions. Embedded systems are specialized computer systems designed for specific control functions within a larger mechanical or electrical system. They are ubiquitous, found in everything from consumer electronics to industrial machinery. FPGAs are increasingly being adopted in embedded systems due to their unique combination of flexibility, real-time processing capabilities, and ability to integrate custom hardware functions directly onto a single chip. This allows for highly optimized and efficient embedded solutions.FPGA in Industrial AutomationIndustrial automation relies heavily on precise control, real-time data processing, and robust communication. FPGAs are perfectly suited for these demands, enabling advanced control systems, machine vision, and robotics in manufacturing environments. Their ability to execute parallel operations with deterministic timing is crucial for applications like motion control, process automation, and quality inspection. For example, in a high-speed sorting machine, an FPGA can process sensor data and control robotic arms with microsecond precision, ensuring efficient and accurate operation. The reconfigurability of FPGAs also allows industrial systems to adapt to new production requirements or integrate new sensors without extensive hardware redesign.FPGAs can also act as a bridge between different communication protocols in industrial settings, ensuring seamless data flow between various machines and sensors. Their low power consumption and small form factor make them ideal for deployment in compact and energy-sensitive industrial equipment. This makes FPGAs a cornerstone technology for the ongoing Industry 4.0 revolution, enabling smarter and more agile manufacturing processes.Real-World Example: A major automotive manufacturer used FPGAs in their robotic assembly lines to achieve higher precision and speed in welding operations. The FPGA-based control system allowed for dynamic adjustments to robot movements based on real-time sensor feedback, significantly reducing defects and increasing throughput. FPGA in Industrial Automation Control SystemFor more information on embedded systems, you can refer to the Embedded System article on Wikipedia.How Do FPGAs Benefit FinTech and High-Frequency Trading?FPGAs benefit FinTech and high-frequency trading (HFT) by executing complex algorithms and order matching with deterministic, ultra-low latency that software-based systems cannot match. The financial technology (FinTech) sector is characterized by its need for extreme speed, low latency, and robust security. FPGAs are increasingly being adopted in FinTech applications to gain a competitive edge, particularly in areas like high-frequency trading, risk management, and data analytics. Their ability to process vast amounts of data in parallel and execute complex algorithms with deterministic latency makes them invaluable in this demanding industry.FPGA in High-Frequency Trading (HFT)High-Frequency Trading (HFT) is perhaps the most prominent application of FPGAs in FinTech. In HFT, milliseconds can mean the difference between profit and loss. FPGAs are used to implement ultra-low-latency trading strategies, order matching engines, and market data processing. By offloading these critical functions to hardware, FPGAs provide distinct advantages:Execution Speed: They can execute trades and react to market changes significantly faster than software-based systems running on CPUs.Strategic Edge: This speed advantage is crucial for arbitrage strategies and for minimizing slippage in large trades.Real-Time Analysis: An FPGA can process incoming market data feeds, analyze price movements, and send out buy/sell orders in a fraction of the time it would take a traditional server.FPGA in Risk Management and Data AnalyticsBeyond HFT, FPGAs are also being utilized in risk management and financial data analytics. These tasks often involve complex simulations (like Monte Carlo simulations) and the processing of large datasets to assess market risk, credit risk, and operational risk. FPGAs can accelerate these computations, allowing financial institutions to run more frequent and sophisticated risk models, leading to better decision-making and compliance. Their ability to handle custom data types and parallelize computations makes them well-suited for these specialized analytical workloads. The enhanced security features of FPGAs, including hardware-level encryption and tamper detection, also make them attractive for protecting sensitive financial data.Key Takeaway: The deterministic latency and reconfigurability of FPGAs provide a unique advantage in FinTech, allowing firms to rapidly deploy and adapt to new trading strategies and regulatory requirements while maintaining the highest levels of performance and security.For more information on financial technology, you can refer to the Financial Technology article on Wikipedia.How Do FPGAs Improve Network Security?FPGAs improve network security by providing hardware-accelerated, real-time processing for deep packet inspection and encryption without bottlenecking network traffic. In an era of increasing cyber threats, network security is paramount. FPGAs are emerging as a powerful tool in network security applications, offering high-performance, low-latency processing for critical security functions. Their reconfigurable hardware allows for rapid adaptation to new threats and the implementation of custom security protocols, making them ideal for safeguarding sensitive data and infrastructure.Hardware-Accelerated SecurityTraditional software-based security solutions can struggle to keep pace with the volume and speed of network traffic, especially when dealing with sophisticated attacks. FPGAs can offload computationally intensive security tasks, such as encryption/decryption, deep packet inspection (DPI), and intrusion detection/prevention, directly to hardware. This hardware acceleration significantly improves throughput and reduces latency, allowing security systems to analyze network traffic in real-time without becoming a bottleneck. For example, an FPGA can perform cryptographic operations at wire speed, ensuring that encrypted communications do not introduce significant delays.Custom Security Solutions and AdaptabilityThe reconfigurability of FPGAs is a major advantage in network security. As new vulnerabilities are discovered and new attack vectors emerge, FPGAs can be reprogrammed to implement updated security algorithms or entirely new defense mechanisms. This adaptability is crucial for staying ahead of cybercriminals. Furthermore, FPGAs can be used to create custom hardware root-of-trust solutions, providing a highly secure foundation for critical systems. Their inherent parallelism also makes them suitable for tasks like brute-force attack detection and prevention, where many parallel computations are required.Security Insight: The ability to implement security functions directly in hardware on an FPGA makes them less susceptible to software-based attacks and provides a higher level of trust and integrity for critical network infrastructure. FPGA in Network Security ApplianceFor more information on network security, you can refer to the Network Security article on Wikipedia.Why Are FPGAs Essential for HPC Clusters?FPGAs are essential for HPC clusters because they act as dedicated accelerators, offloading specialized workloads from main processors to maximize hardware utilization and energy efficiency. High-Performance Computing (HPC) is a field that deals with solving complex computational problems that require immense processing power. These problems often involve large datasets and intricate algorithms, making them ideal candidates for hardware acceleration. FPGAs play a significant role in HPC by providing a highly flexible and parallel computing platform that can accelerate complex computations, offering a compelling alternative or complement to traditional CPUs and GPUs.FPGA in HPC ClustersIn HPC clusters, FPGAs are deployed as accelerators to offload specific, computationally intensive tasks from the main processors. This allows the CPUs to focus on general-purpose computing while the FPGAs handle specialized workloads with greater efficiency. Applications benefiting from FPGA acceleration in HPC include scientific simulations (e.g., molecular dynamics, weather forecasting), financial modeling, and big data analytics. The ability of FPGAs to be reconfigured for different algorithms means that a single FPGA can be adapted to accelerate various parts of an HPC workflow, maximizing hardware utilization and reducing overall power consumption. For instance, in a large-scale data center, FPGAs can be used to accelerate database queries or real-time analytics, providing faster insights from massive datasets.Advantages of FPGAs in HPCFPGAs offer several distinct advantages in HPC environments:Customization: FPGAs can be programmed to create custom hardware architectures optimized for specific algorithms, leading to significant performance gains over general-purpose processors.Parallelism: Their inherent parallel architecture allows FPGAs to execute many operations simultaneously, which is crucial for data-intensive HPC tasks.Energy Efficiency: By implementing only the necessary logic for a given task, FPGAs can achieve higher computational efficiency per watt compared to CPUs or GPUs, reducing operational costs in large HPC facilities.Low Latency: FPGAs can process data with very low latency, which is critical for real-time simulations and interactive HPC applications.Analyst View: “The increasing complexity of HPC workloads, coupled with the need for greater energy efficiency, is driving the adoption of FPGAs as dedicated accelerators. Their ability to provide custom hardware for specific problems makes them an invaluable asset in the pursuit of exascale computing.” - HPC Industry AnalystFor further reading on High-Performance Computing, you can refer to the High-performance computing article on Wikipedia.How Are FPGAs Transforming Automotive Electronics?FPGAs are transforming automotive electronics by providing the scalable, high-performance computing power needed for advanced driver-assistance systems (ADAS) and in-car infotainment. The automotive industry is undergoing a profound transformation, driven by advancements in autonomous driving, in-car infotainment, and advanced driver-assistance systems (ADAS). FPGAs are playing an increasingly critical role in automotive electronics, providing the flexibility, performance, and reliability required for these complex and safety-critical applications. Their ability to be reconfigured in the field allows for rapid updates and adaptations to evolving automotive standards and features.FPGA in ADAS and Autonomous DrivingAdvanced Driver-Assistance Systems (ADAS) and autonomous driving systems rely on processing vast amounts of sensor data in real-time to perceive the environment, make decisions, and control the vehicle. FPGAs are ideal for accelerating these tasks, including sensor fusion (combining data from cameras, radar, LiDAR), object detection, and path planning. Their low-latency processing ensures that the vehicle can react instantaneously to changing road conditions, enhancing safety and performance. For example, an FPGA can process high-resolution camera feeds to identify lane markings and traffic signs with extreme precision, enabling features like lane-keeping assist and adaptive cruise control.FPGA in In-Car Infotainment and ConnectivityBeyond safety-critical systems, FPGAs are also finding applications in in-car infotainment and connectivity. Modern vehicles are becoming increasingly connected, offering features like advanced navigation, multimedia streaming, and seamless integration with personal devices. FPGAs can handle the diverse processing requirements of these systems, from high-definition video rendering to managing multiple communication protocols (e.g., Ethernet, CAN, FlexRay). Their reconfigurability allows automotive manufacturers to quickly integrate new features and adapt to emerging connectivity standards, providing a rich and personalized in-car experience.Innovation Spotlight: The ability of FPGAs to support heterogeneous computing, combining custom hardware logic with embedded processors, makes them a powerful platform for developing next-generation automotive architectures that can handle the diverse and demanding workloads of future vehicles.For more information on automotive electronics, you can refer to the Automotive electronics article on Wikipedia.What Is the Role of FPGAs in Robotics?FPGAs play a crucial role in robotics by enabling real-time sensor fusion and deterministic motor control, allowing robots to react instantaneously to dynamic environments. Robotics is a field that demands a delicate balance of precision, speed, and adaptability. From industrial automation to service robots and drones, the ability to process sensor data in real-time and execute complex control algorithms is paramount. FPGAs are becoming increasingly crucial in robotics technology, providing the necessary computational power and flexibility to enable more intelligent and agile robotic systems.Real-Time Control and Sensor FusionRobots often operate in dynamic environments, requiring immediate responses to sensory input. FPGAs excel at real-time control and sensor fusion, which are fundamental to robotic operation. They can process data from various sensors (e.g., cameras, LiDAR, force sensors) in parallel, fuse this information to create a comprehensive understanding of the robot’s environment, and then execute precise motor control commands with extremely low latency. This deterministic behavior is critical for tasks requiring high accuracy, such as robotic surgery or precision manufacturing. For example, an FPGA can manage the intricate movements of a robotic arm, ensuring it picks and places components with sub-millimeter accuracy at high speeds.Adaptability and CustomizationThe reconfigurability of FPGAs offers significant advantages in robotics development. As robotic tasks and environments evolve, FPGAs can be reprogrammed to adapt to new algorithms, sensor types, or control strategies without requiring a complete hardware redesign. This flexibility accelerates the development cycle and allows for the deployment of highly specialized robotic solutions. Furthermore, FPGAs can be used to implement custom hardware accelerators for specific robotic functions, such as inverse kinematics calculations or path planning, leading to more efficient and powerful robots. This makes FPGAs an ideal platform for research and development in advanced robotics, as well as for deploying highly optimized commercial robotic systems.Future Outlook: As robots become more autonomous and capable of learning, the role of FPGAs in providing the underlying hardware for real-time AI inference and adaptive control will continue to expand, pushing the boundaries of what robots can achieve.For more information on robotics, you can refer to the Robotics article on Wikipedia.Conclusion: FPGAs – The Adaptable Powerhouse of Modern TechnologyFrom the intricate calculations of deep learning to the lightning-fast demands of high-frequency trading, FPGAs have proven to be an incredibly versatile and powerful technology. Their unique ability to be reconfigured at the hardware level provides an unparalleled combination of performance, flexibility, and power efficiency that traditional CPUs and GPUs often cannot match for specialized tasks. We’ve explored how FPGAs are not just components but fundamental enablers across diverse sectors, including communication systems, image processing, autonomous driving, AI acceleration, IoT, accelerated computing, medical devices, video processing, embedded systems, FinTech, network security, automotive electronics, and robotics.The continuous evolution of FPGA technology, with advancements in architecture and design tools, ensures their relevance in an increasingly complex technological landscape. As the demand for real-time processing, custom hardware acceleration, and energy-efficient solutions continues to grow, FPGAs are poised to play an even more significant role in shaping the future. They offer a pathway to innovation, allowing engineers and researchers to push the boundaries of what’s possible by tailoring hardware precisely to their needs.What new frontiers do you believe FPGAs will conquer next? Their adaptability suggests a future where hardware can evolve as rapidly as software, unlocking new possibilities for intelligent systems and groundbreaking applications. The journey of FPGAs is far from over; in fact, it’s just accelerating.Frequently Asked QuestionsWhat is the difference between an FPGA and a microcontroller?FPGAs provide custom hardware logic without predefined signal widths, allowing for massive parallel processing and ultra-low latency. In contrast, microcontrollers execute sequential software instructions using fixed memory widths. FPGAs are ideal for high-throughput tasks, while microcontrollers excel at simpler, sequential control operations.Why are FPGAs used in AI instead of GPUs?While GPUs are excellent for training large AI models due to their massive parallel processing, FPGAs offer superior power efficiency and deterministic low latency for AI inference. This makes FPGAs highly preferable for edge computing, autonomous vehicles, and real-time applications where power and speed are critical.Is it hard to learn FPGA programming?Historically, FPGA programming required deep knowledge of hardware description languages (HDLs) like VHDL or Verilog. However, modern High-Level Synthesis (HLS) tools now allow developers to program FPGAs using familiar software languages like C++ or Python, significantly lowering the barrier to entry.What is the future market size for FPGAs?The global FPGA market is experiencing rapid growth, valued at $13.8 billion in 2025 and projected to reach over $41 billion by 2035. This expansion is heavily driven by increasing demands in AI inference, 5G/6G telecommunications, automotive electronics, and industrial automation.{ "@context": "https://schema.org", "@graph":[ { "@type": "Article", "headline": "FPGA Applications: Powering Modern Technology", "datePublished": "2023-09-10T08:00:00+08:00", "dateModified": "2026-03-16T16:55:00+08:00", "author": { "@type": "Person", "name": "Anonymous FPGA Engineer" }, "publisher": { "@type": "Organization", "name": "Kynix Electronics" } }, { "@type": "FAQPage", "mainEntity":[ { "@type": "Question", "name": "What is the difference between an FPGA and a microcontroller?", "acceptedAnswer": { "@type": "Answer", "text": "FPGAs provide custom hardware logic without predefined signal widths, allowing for massive parallel processing and ultra-low latency. In contrast, microcontrollers execute sequential software instructions using fixed memory widths. FPGAs are ideal for high-throughput tasks, while microcontrollers excel at simpler, sequential control operations." } }, { "@type": "Question", "name": "Why are FPGAs used in AI instead of GPUs?", "acceptedAnswer": { "@type": "Answer", "text": "While GPUs are excellent for training large AI models due to their massive parallel processing, FPGAs offer superior power efficiency and deterministic low latency for AI inference. This makes FPGAs highly preferable for edge computing, autonomous vehicles, and real-time applications where power and speed are critical." } }, { "@type": "Question", "name": "Is it hard to learn FPGA programming?", "acceptedAnswer": { "@type": "Answer", "text": "Historically, FPGA programming required deep knowledge of hardware description languages (HDLs) like VHDL or Verilog. However, modern High-Level Synthesis (HLS) tools now allow developers to program FPGAs using familiar software languages like C++ or Python, significantly lowering the barrier to entry." } }, { "@type": "Question", "name": "What is the future market size for FPGAs?", "acceptedAnswer": { "@type": "Answer", "text": "The global FPGA market is experiencing rapid growth, valued at $13.8 billion in 2025 and projected to reach over $41 billion by 2035. This expansion is heavily driven by increasing demands in AI inference, 5G/6G telecommunications, automotive electronics, and industrial automation." } } ] } ]}

Today in this article we will discuss about how PCB is developed and its industrial trend. Printed circuit board, also called PCB, is an important electronic component and the support of electronic components, which also have a circuit in which a metal conductor can be connected to an electronic component. Nowadays electronic equipment requires high performance, high speed and thin miniaturization. As a multidisciplinary industry, PCB is the key technology of high-end electronic equipment. It plays an important role in electronic interconnection technology. Introduction to how PCB worksCatalogI PCB overviewII. Main types of PCB2.1 Single-sided boards2.2 Double-sided boards2.3 Multi-layer boardsIII Industrial chain of PCB3.1 Glass fiber cloth3.2 Copper foil3.3 Copper clad laminateIV Development of PCB industry in ChinaFAQI PCB overviewTraditional circuit boards are referred to as printed circuit boards because the circuit and its surface are created using the printing etching inhibitor method. At the moment, most circuit boards are made by using an etching inhibitor (film pressing or coating), and then etching is used to make the circuit board after exposure and development.Due to the continuous miniaturization and refinement of electronic products, most of the current circuit boards are coated with etching inhibitors (film pressing or coating). After exposure and development, the board will be etched to form the final circuit board. PCBs have progressed from single-sided to double-sided, multilayer, and flexible boards, and this trend is expected to continue. Because of its ongoing advancements in the areas of high precision, high density, and high reliability. PCB maintains a strong vitality in future electronic equipment development projects due to its continuous development of high precision, high density, and high reliability, reducing volume, cost, and improving performance.Printed circuit boardThe discussion of the development trend of the future PCB manufacturing technology at home and abroad is almost consistent, that is, developing toward the direction of high density, high precision, fine aperture, fine wire, fine spacing, high reliability, multilayer, high speed transmission, light weight and thin shape. At the same time, it is aimed to increase productivity, reduce costs, reduce pollution, adapt to multi-species and small run production during manufacturing. The technical development level of printed circuit is represented by the line width, aperture and the ratio of thickness to aperture of printed circuit.The creator of the printed circuit board was an Austrian called Paul Eisler. In 1936, he first used a printed circuit board in a radio. In 1943, Americans mainly applied this technology to military radios. Then in 1948, this invention was officially approved for commercial use in the United States.  Since the middle of 1950s, printed circuit boards have been widely used.Before the emergence of PCB, the interconnection of electronic components was accomplished by direct connection of electrical wires. Nowadays, wires exist only for laboratory applications. Printed circuit boards (PCB) have definitely occupied the position of absolute control in the electronic industry. II Main types of PCBAccording to the number of circuit layers, it can be divided into single-sided board, double-sided and multilayer board. The common multilayer board is usually 4 or 6 layers, and the complex multilayer board can have dozens of layers. Printed circuit boards are mainly divided into three types:2.1 Single-sided boardsOn the most basic PCB, the parts are concentrated on one side and the wire will be on the other (when there is patch element, it is on the same side as the wire, and the plug-in device is on the other side). Because the wire only appears on one side, this type of PCB is called single-sided board. The single-sided board has many strict restrictions on the design circuit (since there is only one side, the wiring cannot intersect and have to go around a separate path), so only the early circuits use this type of board.2.2 Doublesided boardsThere are wiring on both sides of this kind of circuit board.However, to use these two sides of the wire, you must have an appropriate circuit connection between the two sides. The "bridge" between these circuits is called “via”. The via hole is a small hole filled or coated with metal on the PCB, which can be connected to both sides of the wire. It can be connected to two sides of the wire. Because the area of the double-sided board is twice as large as that of the single panel,  it solves the difficulty of wiring interleaving in the single panel (it can get to the other side through the hole), so it is more suitable for the circuit which is more complicated than the single sided.2.3 Multi-layer boardsIn order to increase the area in which wiring can be made, the multilayer board is made up of more single or double-sided wiring boards. A printed circuit board with one side as the inner layer, two single sides as the outer layer or two sides as the inner layer, and two single sides as the outer layer, which is interlinked with conductive patterns according to the design requirements, is composed of four layers and six layers of printed circuit boards, also known as multilayer printed circuit boards (PCB). The number of layers on the board does not mean that there are several independent wiring layers. In special cases, an empty layer is added to control the thickness of the board, and the number of layers is usually even, and contains the outermost two layers. Most motherboards are 4 to 8 layers, but technically we can do nearly 100 layers of PCB. Large scale supercomputers mostly use quite many layers of motherboards. However, because such computers have been replaced by clusters of many common computers, super multilayer boards have been gradually unused. Because the layers in the PCB are tightly combined, it is generally not easy to see the actual number, but if you look closely at the motherboard, you can see it. Also there are two kind of boards called rigid and flexible printed boards. > Rigid and Flexible Printed BoardsDivided into rigid circuit boards, flexible circuit boards and rigid-flexible printed circuit boards. The PCB shown in the first picture below is generally referred to as the rigid PCB, and the yellow connection line in the second picture is called flexible PCB. The intuitive difference between a rigid PCB and a flexible PCB is that the flexible PCB is flexible. The common thickness of rigid PCB is 0.2mm / 0.4mm / 0.6mm / 0.8mm / 1.0mm / 1.2mm / 1.6mm / 2.0mm etc. The common thickness of flexible PCB is 0.2mm. The thickening layer is added to the back where the parts are to be welded , and the thickness of the thickened layer varies from 0.2 mm to 0.4 mm. TThe purpose of understanding these is to provide them with a spatial reference for the design of a structural engineer. The common materials for rigid PCB include: phenolic paper laminates, epoxy paper laminates, Polyester glass felt laminates, epoxy glass cloth laminates; flexible PCB materials include: polyester film, polyimide film, ethylene propylene fluoride film.Rigid boardFlexible board III Industrial chain of PCBAccording to the upstream and downstream classification of the industrial chain, PCB industry can be divided into raw materials - CCL (copper-clad laminate) - PCB (printed circuit board) - electronic product application. The relationship is simply expressed as: 3.1 Glass fiber clothGlass fiber cloth is one of the raw materials of copper clad plate, which is made of glass fiber yarn and account for about 40% of the cost of copper clad laminate (thick plate) and 25% (thin plate). Glass fiber yarn is calcined into liquid state from raw materials such as silica sand in the kiln, then pulled into very fine glass fiber through extremely small alloy nozzles, and then hundreds of glass fibers are twisted into glass fiber yarn. The manufacture of glass fabric is similar to that of weaving enterprises. It can control production capacity and quality by controlling rotation speed, and the specifications are relatively single and stable. Since World War II, there has been almost no significant change in specifications.Unlike CCL, prices of glass fabrics are the most affected by supply and demand, fluctuating between $0.50-1.00 a meter in recent years. Taiwan and mainland China account for about 70% of the world's production capacity.3.2 Copper foilCopper foil is the raw material that accounts for the largest proportion of the cost of copper clad laminate, about 30% of the cost of copper clad plate (thick plate) and 50% (thin plate). Therefore the price increase of copper foil is the main driving force of the price increase of copper clad laminate. The price of copper foil is closely reflected in the change of copper price. However, the bargaining power is relatively weak. With the rising copper prices, copper foil manufacturers are in a difficult situation, and many enterprises have been forced to close down or be annexed. Even if copper clad laminate manufacturers accept copper foil price increases, copper foil manufacturers are still in a general loss. Another wave of price increases is likely to occur in the first quarter of 2006, possibly driving up CCL prices due to the price gap. 3.3 Copper clad laminateCopper clad laminate (CCL) is a direct raw material of PCB, which uses epoxy resin as fusion agent to press glass fiber cloth and copper foil to make printed circuit board (PCB) after etching, electroplating and multilayer laminate pressing. The copper clad laminate industry is not in high demand for capital, about 3 million to 40 million yuan, and can be stopped or converted at any time. In the upstream and downstream industrial chain structure, CCL has the strongest bargaining power. It has a strong voice in the procurement of glass fiber cloth, copper foil and other raw materials , and as long as the demand of downstream is good, the manufacturer of CCL can transfer the pressure of rising costs to the downstream PCB manufacturers. IV Development of PCB industry in ChinaThe development of PCB in China began in 1956. In 1963-1978, it is gradually expanded to be the PCB industry. More than 20 years after the reform and opening up, due to the introduction of foreign advanced technology and equipment, single-sided board, double-sided board and multi-layer board had a rapid development. The domestic PCB industry has gradually developed from small to large scale.Due to the concentration of downstream industries and relatively low labor and land costs, China has become the region with the strongest development momentum. In 2002, China became the third largest output country of PCB. In 2003, the output value and import and export volume of PCB exceeded $6 billion, which overtaking the United States for the first time and becoming the second largest output country in the world. The proportion of output value also increased from 8.54% in 2000 to 15.30%, nearly doubling. In 2006, China has replaced Japan as the world's largest PCB production base and the most active technology development country. China's PCB industry has maintained a high growth rate of about 20%, which is far higher than that of the global PCB industry. In terms of production composition, the main products of China's PCB industry have shifted from single-sided and dual-sided to multilayer, and are being upgraded from 4-6 layers to more than 6-8 layers. With the rapid growth of multilayer printed board, HDI board and flexible boards, China's PCB industrial structure is gradually being optimized and improved. However, although China's PCB industry has made considerable progress, there is still a big gap compared with advanced countries, and there is still much room for improvement in the future. First of all, China's entry into the PCB industry is relatively late and there are no specialized PCB research and development institution. There is a big gap between the research and development capabilities of some new technologies and foreign manufacturers. Secondly, in terms of product structure, the production of medium and low laminates is still dominated. Although FPC and HDI boards are growing rapidly, the proportion is still low because of its small base. Most of our PCB production equipment depends on imports, and some core raw materials can only rely on imports. The industrial chain is incomplete, which also hindered the domestic PCB series enterprise's development step.  FAQ 1. What is PCB?A printed circuit board, or PCB, is used to mechanically support and electrically connect electronic components using conductive pathways, tracks or signal traces etched from copper sheets laminated onto a non-conductive substrate. 2. What is PCB and types of PCB?A printed circuit board (PCB) is a thin board made from fiberglass, composite epoxy, or other laminate materials. PCBs are found in various electrical and electronic components such as beepers, radios, radars, computer systems, etc. Different types of PCBs are used based on the applications. 3. What can a PCB be used for?Printed circuit boards (PCBs) are used to mechanically support and electrically connect electronic components using conductive pathways, tracks or signal traces etched from copper sheets laminated onto a non-conductive substrate, employed in the manufacturing of business machines and computers, as well as communication ... 4. Why are PCB green?It is due to the solder mask, which protects the copper circuits printed on the fibre glass core to prevent short circuits, soldering errors, etc. ... The colour of the solder mask gives the board its appearance. 5. What is PCB and its advantages?Compact Size and Saving of Wire. A characteristic PCB includes a large number of electronic components. On a Printed circuit board, the interconnection between the components is made through copper tracks instead of using a number of current carrying wires. It makes the interconnections less bulky. 6. How long does it take for PCBs to break down?3.5 to 83 days. The time it takes for half of the amount of PCBs (initially) present to be broken down ranges from 3.5 to 83 days for molecules with 1 to 5 chlorine atoms. In water, PCBs are essentially broken down by the effect of sunlight (photolysis). 7. What is the disadvantage of PCB?Disadvantages: Easy to Cause Handling Damage. Process Uses a Carcinogen (Thiourea) Exposed Tin on Final Assembly can Corrode. 8. Which PCB design software is the best for beginners?Top Best PCB Design Software of 2021a. PROTEL (Altium Designer) b.PADS (PowerPCB) c. ORCAD. d. Allegro. e. Eagle（Easily Applicable Graphical Layout Editor）f. Kicad.g. EasyEda.h. Fritzing. 9. What are the advantage of flexible PCB?The flexible circuit board are designed for saving room and improving the flexibility to meet a smaller and higher density mounting design, it also helps to reduce the assembly process and enhance reliability. 10. Why do we use PCB instead of breadboard circuit?The advantages of a printed circuit board: the board is permanent to have an electronic device worked. PCB has a better current carrying capacity comparing to a breadboard, you can make your traces wider to take more current so that work well. ... You can mount heat-sinks to the board so that have them rigid. 11. What PCB means?Printed-Circuit-BoardA printed circuit board, or PC board, or PCB, is a non-conductive material with conductive lines printed or etched. Electronic components are mounted on the board and the traces connect the components together to form a working circuit or assembly. 12. Why are PCBs dangerous for humans?PCBs are a probable human carcinogen.Studies of PCBs in humans have found increased rates of melanomas, liver cancer, gall bladder cancer, biliary tract cancer, gastrointestinal tract cancer, and brain cancer, and may be linked to breast cancer. 13. What are PCBs in electronics?A printed circuit board, or PCB, is used to mechanically support and electrically connect electronic components using conductive pathways, tracks or signal traces etched from copper sheets laminated onto a non-conductive substrate. 14. Why are PCB green?It is due to the solder mask, which protects the copper circuits printed on the fibre glass core to prevent short circuits, soldering errors, etc. ... The colour of the solder mask gives the board its appearance. 15. What is PCB and its role?A process control block (PCB) is a data structure used by computer operating systems to store all the information about a process. It is also known as a process descriptor. When a process is created (initialized or installed), the operating system creates a corresponding process control block. 16. Which software is best for PCB design?- NI Multisim.- KiCad EDA.- Autodesk EAGLE.- DipTrace.- Ultiboard.- CAM350.- ExpressPCB Plus.- SolidWorks PCB. 

The "wait and see" period is officially over. In January, we warned of potential volatility. Now, as we enter March 2026, the first major domino has fallen. Powerchip Semiconductor Manufacturing Corp (PSMC) has reportedly initiated a price hike for its 8-inch foundry services starting this month, a move expected to trigger a chain reaction across Tier-2 foundries globally.This isn't just about inflation; it's about a physical lack of manufacturing slots. According to the latest data from TrendForce and industry checks, global 8-inch wafer capacity is projected to contract by 2.4% year-over-year in 2026. This structural decline, colliding with the explosive demand from AI servers, has created a "Perfect Storm" for the analog supply chain.The Capacity Crunch Chart🚨 Critical Market Bulletin (March 2026):Foundry Action: PSMC and VIS (Vanguard) are raising quotes by 10-15% for spot orders.Capacity Utilization: Average 8-inch utilization has climbed to 90%, with BCD (Bipolar-CMOS-DMOS) processes fully allocated.Rumor Mill: Uncertainty surrounds Samsung's Giheung S7 fab, with reports of further capacity reductions intensifying supply fears.The "BCD" Bottleneck: Why AI is Starving Your PMIC SupplyWhy is a data center boom hurting the supply of industrial voltage regulators? The answer lies in the BCD process technology. This specialized 8-inch process is essential for manufacturing high-voltage Power Management ICs (PMICs).AI Servers (powering models like GPT-5) require complex, multi-phase power modules that consume up to 5x the silicon area of standard server PMICs. Tier-1 fabless design houses have booked out the vast majority of BCD capacity at TSMC and UMC to serve this high-margin AI market. This has effectively "crowded out" production slots for standard consumer and industrial PMICs, pushing lead times from 16 weeks to 26+ weeks.The BCD Process SqueezeCategory Watch: March 2026 Price & Lead Time DataProcurement teams must update their ERP lead time offsets immediately. The following data reflects the current situation on the Kynix platform and global spot market:Component FamilyMarch StatusPrice Trend (MoM)Lead TimeHigh-Voltage PMICAllocation▲ +18%26 - 30 WeeksAutomotive MOSFETsCritical Shortage▲ +15%35+ WeeksIndustrial MCUs (32-bit)Tightening▲ +8%20 - 24 WeeksStandard Logic (Little impact)Stable► 0%10 - 12 WeeksThe "Samsung Factor" and Structural DeclineAdding to the anxiety is the structural shift at major IDMs. Reports indicate that Samsung is continuing to scale back its 8-inch operations (specifically rumored around the Giheung S7 line) to focus resources on 12-inch and advanced memory.Unlike 2021, where the shortage was caused by a temporary demand spike, the 2026 challenge is supply-side atrophy. The machines are being turned off or converted, meaning this capacity is likely gone forever. This supports the forecast that the 2.4% capacity drop is just the beginning of a long-term trend.Kynix Strategy: Surviving Q2 and Q3With PSMC's price hike official, the window for "cheap inventory" has closed. Your strategy must shift from cost-saving to assurance of supply.1. Lock in Q3 Stock NowWait-and-see is a losing strategy. With lead times extending past 26 weeks, orders placed today will land in September. You must cover your Q3 production needs immediately.2. Validate Alternatives (Second Sources)If your BOM relies on a single Tier-1 brand for MOSFETs, you are at risk. Kynix can help you identify pin-to-pin compatible replacements from manufacturers who still have 8-inch capacity available, particularly in the Asian market.Conclusion: Resilience in a Shrinking MarketThe March 2026 data confirms that the era of abundant legacy node capacity is ending. The combination of PSMC's price moves and the AI sector's appetite for power silicon means buyers must be agile.Don't let a missing $0.20 regulator stop your production line.Secure Your Critical PMICs & MOSFETs TodaySearch Kynix's global inventory for real-time stock and alternative solutions.Search Components on Kynix.com

SummarySome electornic or electrical appliances needs time limited power supply,or usage of some devices are depends on limitted time.To automate electrical devices depends on time simple and robust solution given based on arduino.Today let's make a arduino variable time relay together.By using this arduino variable timer relay we can control high voltage electrical appliances or electronic devices.Now let me share the process. To indicate the time duration and status 16×2 LCD display is included in this design, once the program uploaded to the Arduino then it can work independent with some external battery power source. Connection DiagramConstruction and Working In this project arduino uno board is used to control SPDT (Single pole double throw) Relay and 16 x 2 character LCD indicates the time duration status. Digital pins D2 to D7 are connected to the LCD display. VR1 varible resistor helps to control the contrast of LCD display, Transistor Q1 BC547 reacts as a Switching device and controls the power supply to the Relay coil depends on arduino output. There are three push buttons are placed to set different time durations, S1 Switch makes the count start, S2 changes the Hours and S3 changes the Minutes of time duration. Output signal from the Arduino is taken from D8 pin and it drives the Relay through transistor. After making the connection, upload the following arduino sketch and pretest the operation with real timer clock.Note:- Candle with extreme care if you using High voltage supply at the Relay end. Arduino Code #include <LiquidCrystal.h>LiquidCrystal lcd(7,6,5,4,3,2);const int set = 9;int hours=10;int start=11; int relay=8;int b=0,h=0,t=0;int buttonState = 0; int lastButtonState = 0; void setup() {    pinMode(set,INPUT);  pinMode(hours,INPUT);  pinMode(relay,OUTPUT);  pinMode(start,INPUT);  lcd.begin(16,2);  lcd.setCursor(0,0);  lcd.print("Adjustable Timer");  }int timer( int b,int h){         if(b<=9)           {            lcd.setCursor(3,1);            lcd.print(0);            lcd.setCursor(4,1);            lcd.print(b);          }     else{lcd.setCursor(3,1);lcd.print(b);}         lcd.setCursor(2,1);         lcd.print(":");     if(h<=9)           {            lcd.setCursor(0,1);            lcd.print(0);            lcd.setCursor(1,1);            lcd.print(h);          }     else{lcd.setCursor(0,1);lcd.print(h);}    }void loop()       {           buttonState = digitalRead(set);                         if (buttonState != lastButtonState)       {                 if(buttonState == HIGH)         {                       lcd.clear();           lcd.print("Set time in min:");                      ++b;           timer(b,h);                                   }                   lastButtonState = buttonState;          }       if (digitalRead(hours)== HIGH)          {              lcd.clear();              lcd.print("Set time in hours");              ++h;              timer(b,h);              while(digitalRead(hours)==HIGH);                                    }             if(digitalRead(start)==HIGH)          {             lcd.clear();             t=((h*60)+(b))*1000;             lcd.print("Timer is set for");             timer(b,h);             digitalWrite(relay,HIGH);             delay(t);             digitalWrite(relay,LOW);             while(digitalRead(start) == HIGH );                                   }                        } Have you make it successfully?