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Link: https://www.fujifilm.com/pl/en/news/hq/13164

Fujifilm Launches World’s First Color Filter Material for Image Sensors Compatible with KrF Lithography “WAVE CONTROL MOSAIC™”
PFAS-Free, Contributing to Higher Image Quality in Smartphone Cameras

TOKYO, December 9, 2025 – FUJIFILM Corporation announced the launch of a new color filter material for image sensors, “WAVE CONTROL MOSAIC™*1”, compatible with KrF*2 lithography. This innovative product is the world’s first color filter material for image sensors that supports KrF exposure, and is entirely PFAS-free, addressing environmental and ecological concerns. The new material is designed for use in cutting-edge image sensors requiring ultra-miniaturization and high sensitivity, contributing to higher image quality in smartphone cameras.

Image sensors are semiconductors that convert light into electrical signals to produce images, and are incorporated into devices such as smartphones and digital cameras. In recent years, the range of applications for image sensors has expanded to include automobiles, security equipment such as surveillance cameras, and AR/VR devices. As a result, the image sensor market is expected to grow at an annual rate of approximately 6%*3. With the increasing opportunities for photo and video capture—such as taking pictures and streaming videos shot on smartphones—there is a growing demand for capturing bright and smooth images and videos in any scene, as well as for editing and cropping images after shooting. These trends are driving the need for even higher image quality in image sensors. To achieve higher image quality, it is necessary to miniaturize sensor pixels to create more detailed and high-resolution images. However, as pixels become smaller, the amount of light that can be captured decreases, resulting in lower sensitivity—a key challenge in image sensor development.

The newly launched product in Fujifilm’s WAVE CONTROL MOSAIC™ is the world’s first color filter material for image sensors compatible with KrF lithography, enabling the formation of finer pixels that was previously unattainable with conventional i-line*4 exposure. Building on its expertise in functional molecule design and organic synthesis cultivated through silver halide photographic R&D, Fujifilm has developed new additives optimized for KrF exposure and a proprietary dye with outstanding heat and light resistance. In addition, through our unique formulation technology, the company combined this newly developed dye with conventional pigments to increase light transmittance and compensate for the reduction in light caused by pixel miniaturization, resulting in a color filter material that achieves both miniaturization and high sensitivity. With this new product, users can capture bright, smooth images and videos in various scenes.

Furthermore, the product is PFAS-free*5, containing no per- or polyfluoroalkyl substances, which are of increasing environmental concern. Fujifilm has long been committed to reducing and replacing substances that pose potential risks to human health and the environment, having previously developed PFAS-free negative-tone ArF immersion photoresists and nanoimprint resists. Building on the PFAS-free technology established through this product, Fujifilm will extend these efforts to all WAVE CONTROL MOSAIC™ materials and photoresists*6, accelerating the transition of its semiconductor materials portfolio to PFAS-free solutions.

As a leading manufacturer of color filter materials for image sensors, Fujifilm will continue to develop materials that not only enhance image quality but also enable applications such as infrared photography for low-light environments. Under the concept of “Transforming the invisible world into the visible, delivering new vision and value to society,” Fujifilm remains committed to contributing to the expansion of the image sensor market. 

*1 General term referring to a group of functional materials for controlling electromagnetic light waves in a broad range of wavelengths, including photosensitive color materials for manufacturing color filters for image sensors such as CMOS sensors, used in digital cameras and smartphones. WAVE CONTROL MOSAIC is a registered trademark or trademark of FUJIFILM Corporation.
*2 KrF (Krypton Fluoride): A 248nm wavelength laser light source used in the photolithography process for semiconductor manufacturing.
*3 Source: Techno System Research, “2025 First Half Edition CCD & CMOS Market Marketing Analysis.”
*4 i-line: A mercury spectral line with a wavelength of 365nm, also used as a light source in photolithography processes.
*5 PFAS refers to a collective term for perfluoroalkyl compounds, polyfluoroalkyl compounds, and their salts, as defined in the OECD's 2021 report “Reconciling Terminology of the Universe of Per- and Polyfluoroalkyl Substances: Recommendations and Practical Guidance.” Accordingly, the claim ‘PFAS-Free’ denotes the absence of substances falling within this defined group.
*6 Material used to coat wafer substrate when circuit patterns are drawn in the process of semiconductor manufacturing. 

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Thesis title: "SPAD Image Sensors with Embedded Intelligence"

Yang Lin, EPFL (2025)

Abstract: Single-photon avalanche diodes (SPADs) are solid-state photodetectors that can detect individual photons with picosecond timing precision, enabling powerful time-resolved imaging across scientific, industrial, and biomedical applications. Despite their unique sensitivity, conventional SPAD imaging workflows passively collect photons, transfer large volumes of raw data off-chip, and reconstruct results through offline post-processing, leading to inefficiencies in photon usage, high latency, and limited adaptability. This thesis explores the potential of embedded artificial intelligence (AI) for efficient, real-time, intelligent processing in SPAD imaging through hardware-software co-design, bringing computation directly to the sensor to process photon data in its native form. Two general frameworks are proposed, each representing a paradigm shift from the conventional process. The first framework is inspired by the power of artificial neural networks (ANNs) in computer vision. It employs recurrent neural networks (RNNs) that operate directly on timestamps of photon arrival, extracting temporal information in an event-driven manner. The RNN is trained and evaluated for fluorescence lifetime estimation, achieving high precision and robustness. Quantization and approximation techniques are explored to enable FPGA implementation. Based on this, an imaging system integrating a SPAD image sensor with an on-FPGA RNN is developed, enabling real-time fluorescence lifetime imaging and demonstrating generalizability to other time-resolved tasks. The second framework is inspired by the human visual system, employing spiking neural networks (SNNs) that operate directly on the asynchronous pulses generated by SPAD avalanche breakdown upon photon arrival, thereby enabling temporal analysis with ultra-low latency and energy-efficient computation. Two hardware-friendly SNN architectures, Transporter SNN and Reversed start-stop SNN are proposed, which transform the phase-coded spike trains into density-coded and inter-spike-interval-coded representations, enabling more efficient training and processing. Dedicated training methods are explored, and both architectures are validated through fluorescence lifetime imaging. Based on the Transporter SNN architecture, the first SPAD image sensor with on-chip spike encoder for active time-resolved imaging is developed. This thesis encompasses a full-stack imaging workflow, spanning SPAD image sensor design, FPGA implementation, software development, neural network training and evaluation, mathematical modeling, fluorescence lifetime imaging, and optical system setup. Together, these contributions establish new paradigms of intelligent SPAD imaging, where sensing and computation are deeply integrated. The proposed frameworks demonstrate significant gains in photon efficiency, processing speed, robustness, and adaptability, illustrating how embedded AI can transform SPAD systems from passive detectors into intelligent, adaptive, and autonomous imaging platforms for next-generation applications.

Full thesis is available for download at this link: https://infoscience.epfl.ch/entities/publication/c6ecfd11-dd30-4693-a104-c27c66aecad9 

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Part 2 of the blog is here: https://www.teradyne.com/2025/12/08/high-throughput-image-sensors-smart-testing-powers-progress/ 

High-Throughput Image Sensors: Smart Testing Powers Progress

As data streams grow, modern ATE ensures flexible, scalable accuracy

In the race to produce higher resolution image sensors—now pushing beyond 500 megapixels—the industry faces significant challenges. These sensors aren’t just capturing more pixels; they’re handling massive streams of data, validating intricate on-chip AI functions, and doing it all at breakneck speeds. For manufacturers, the challenge is as unforgiving as it is critical: test more complex devices, in less time, all while maintaining or even reducing costs.

Today’s high-resolution sensors must deliver more than just pixel perfection. They must demonstrate pixel uniformity, identify and compensate for defective pixels, verify electrical performance under varying conditions, and prove their resilience under strict power efficiency requirements. As AI functionality becomes integrated with image sensors, testing must also account for new processing capabilities and system interactions.   

The move toward higher resolutions introduces not only more data but significant production constraints as well. As sensors grow larger to accommodate more pixels, fewer can be tested simultaneously under the illumination field of automated test equipment (ATE). This site count constraint can be well-handled with strategies for faster data processing and smarter testing. This is where Teradyne plays an important industry role, moving beyond supplier, and stepping in as a strategic partner to help manufacturers redefine what’s possible.  

Why High Resolution Means High Stakes  
As image sensor resolutions soar—from smartphones to cars to industrial systems—so do data demands. Each leap in resolution extends the volume of data that must be captured and processed, including during testing. For example, a single 50-megapixel image sensor produces around 100 megabytes of data per image. Multiple images, as many as 25 or more, must be captured under different lighting conditions to validate pixel response, uniformity, and defect detection. When multiplied across millions of units, data quickly scales into terabytes for every production batch.  

Without innovation, this increase in data threatens to overwhelm production lines. Test times can double or even quadruple, slashing throughput and driving up costs. Manufacturers are left with a critical challenge: how to test sensors faster, without sacrificing accuracy or profitability.  

Teradyne Delivers High-throughput, Scalable Solutions  
Teradyne addresses these high-stakes dynamics with a combination of powerful, modular hardware and flexible software tools. At the heart of Teradyne’s approach is the belief that high performance and flexibility are essential. The company’s UltraSerial20G capture instrument for Teradyne UltraFLEXplus was built precisely for this moment and is designed to handle the enormous data loads of modern image sensors. It offers a modular architecture that enables manufacturers to respond to new interface protocols without expensive, time-consuming hardware redesigns.  

At the same time, Teradyne’s engineers kept the future in focus, looking beyond meeting current requirements by adding future capacity in the UltraSerial20G. Essentially, this provides customers with room to grow without replacing critical hardware. When new protocols emerge or higher data rates become the standard, manufacturers can count on the capabilities of their capture platform. While competitors scramble to keep pace, Teradyne customers are already testing the next generation of devices. 

Teradyne also recognizes that hardware is just part of the story. The company’s IG-XL software platform is where this flexibility comes to life. It gives engineers the tools to write custom test strategies at the pin level, controlling everything from voltage and timing to the finest adjustments of signal slopes. Importantly, this software environment allows manufacturers to build and refine their test programs without exposing sensitive intellectual property, a crucial advantage in an industry where secrecy is a competitive necessity.  

Overall, Teradyne’s flexible hardware and software architecture offers an integrated approach that enables manufacturers to manage increasing data volumes while maintaining production schedules.  Teradyne’s Alexander Metzdorf describes it as giving customers the tools to write their own destiny: “Our role is to provide a toolbox that’s flexible and powerful enough for our customers to test whatever they need—when they need it—without being held back by fixed systems.” 

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MRAM applications to image sensors may be of interest to our readers.

Masanori Hosomi, Sony - eMRAM for image sensor applications

eMRAM for Image Sensor Applications  

Since logic chip size of Sony's image sensor, bonding of pixel wafers and logic wafers, are limited to be equal to or smaller than that of the pixel chip, we have been using DRAM and SRAM while battling the enhancement of image sensor functions and area limitations. To incorporate further functionalities, there is a demand for better performance memory that can suppress the memory macro area with small bit cells, as well as non-volatile memory to keep a code or an administration data without external memories. Embedded STT-MRAM has a bit cell size that is less than one-third of that of SRAM and possesses non-volatile characteristics, and eMRAM is suitable for small-scale systems such as smart MCUs that do not have external memory. Therefore, at this point, eMRAM has been commercialized in GNSS (GPS), smart watch and wireless communication systems. In the same way, it is presumed to be suitable for frame buffer memory and data memory in image sensor systems. This talk will present how the device can enhance its functionality with the assumption of application in image sensors.

The full program is available here: https://sites.google.com/view/mramforum/program-booklet 

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Program link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?ScheduleID=36&

Advanced Image Sensors
Wednesday, December 10
1:30 PM - 5:20 PM PST

This session includes 8 papers on the latest in image sensor technology. The first is an invited paper on progress in flash LiDAR using heterodyne detection. The next two papers present HDR imagers using LOFIC pixels that reach or exceed 120dB. This is followed by papers on specialty imagers, the first one describing an all-organic flexible imager, followed by an extremely high frame-rate burst CIS sensor. The final three papers of the session cover the latest technologies for shrinking pixels to sub-micron dimensions. Of special note is the last paper, which shrinks the dual-photodiode pixel to 0.7um.

3D FMCW Wide-angle Flash Lidar: Towards System Integration and In-pixel Frequency Measurement (Invited)

The Frequency Modulated Continuous Wave Light detection and ranging (FMCW Lidar) usually scans the scene point by point, and measures the distance by a Fourier Transform (FT) of the heterodyne signal. A promising solution for higher frame rate with larger image resolution is the flash version of the FMCW Lidar, using floodlight illumination and an image sensor. We first review our recent achievements of FMCW flash Lidar system developments with commercially available components and post-processing. Because FT is difficult to implement in small pixels, we then introduce the principle of a heterodyne image sensor with in-pixel frequency measurement combined with a multi-chirp laser modulation strategy, targeting video rate real-time measurements. 

A 120 dB Dynamic Range 3D Stacked 2-Stage LOFIC CMOS Image Sensor with Illuminance-Adaptive Signal Selection Function

This work presents a 3D stacked 2-stage lateral overflow integration capacitor (LOFIC) CMOS image sensor with illumination adaptive signal selection function. To reduce the high data rates of conventional wide dynamic range sensors, this work proposes an illuminance-adaptive signal selection circuit that non-destructively determines the light intensity level using electrons accumulated in the first stage of LOFIC. This allows the developed sensor to selectively output one or two most appropriate signals out of three, reducing the data rate while maintaining a wide dynamic range. Furthermore, a 3D-stacked Si trench capacitor is employed to achieve over 8.6Me- FWC with 5.6 µm pixel pitch. The fabricated chip demonstrates a dynamic range of 120 dB with selected signal readout and a maximum SNR of 67.5 dB. 

A 129 dB Dynamic Range Triple Readout CMOS Image Sensor with FWC Enhancement Technology

We present a 2.1 μm pixel CMOS image sensor for automotive applications achieving 129 dB single exposure dynamic range with triple readout. The advanced sub-pixel architecture incorporates FDTI, conformal doping and 3D-MIM technologies, significantly enhancing full-well capacity. The sensor enables a seamless triple-image composition with 29 dB SNR at connection points, suitable for high-temperature automotive environments. 

Flexible 256×256 All-Organic-Transistor Active-Matrix Optical Imager with Integrated Gate Driver

Solution-processed organic thin film transistors (OTFTs) provide a promising platform for truly flexible, large-area integrated sensor systems. Here, an all-organic-transistor active-matrix imager using OTFTs for both the backplane and optical sensing layer is developed. Through reducing the density of states at the channel interface for a steep subthreshold swing and low dark current, the resulting organic phototransistor (OPT) presents a high detectivity of 2.2×1016 Jones. The OPT is stacked on top of an OTFT switch with a high ON/OFF ratio of 4.7×1010 to form the active matrix and the gate driver is also integrated. Finally, a 256 × 256 (213 PPI) flexible active-matrix imager is demonstrated for fingerprint and low-distortion imaging with the constructed real-time imaging system. 

A Global Shutter Burst CMOS Image Sensor with 6-Tpixel/s Readout Speed, 256-recording Frames and -170dB Parasitic Light Sensitivity

This paper presents an ultra-high-speed (UHS) global shutter burst CMOS image sensor (CIS) featuring pixelwis analog memory arrays. The developed CIS with 628H x 480V pixels achieves a maximum frame rate of 20 Mfps and a readout speed of 6.03 Tpixel/s. A recording length of 256 frames and a parasitic light sensitivity (PLS) of -170 dB were also achieved simultaneously in a UHS camera. This low PLS is achieved through comprehensive metal shielding of the pixel circuit and memory regions, and by the spatial separation between the photodiode and memory regions, implemented using Si trench capacitors. The introduced bias adjustment circuit compensates for voltage variations among pixel positions due to the ground resistance and pixel circuit current during the pixel driving period, enabling high-resolution video recording with an effective 628H x 480V pixels and a 48 μm pitch. 

Silicon-on-Insulator Pixel FinFET Technology for a High Conversion Gain and Low Dark Noise 2-Layer Transistor Pixel Stacked CIS

This study presents a 2-Layer transistor pixel stacked 0.8-µm dual-pixel (DP) CIS with silicon-on-insulator (SOI) fin field-effect transistor (FinFET) technology. The application of SOI FinFETs as pixel transistors, featuring a body-less configuration on buried oxide, reduces parasitic capacitance at the floating diffusion node, thereby enhancing conversion gain and noise characteristics. The SOI FinFET achieves improved transconductance and source follower gain compared to a previous pixel FinFET. The resolution of challenges associated with the SOI structure is demonstrated through a 0.8μm DP CIS with SOI FinFETs. 

A 0.43µm Quad-Photodiode CMOS Image Sensor by 3-Wafer-Stacking and Dual-Backside Deep Trench Isolation Technologies

Scaling pixel pitch below 0.5um has become highly challenging in conventional 2-wafer stacked CMOS image sensors due to the limited silicon area shared among photodiodes, photodiode-photodiode isolation, and the associated functional transistors, while maintaining the excellent pixel performance. In this work, several advanced pixel technologies, including 3-wafer stacking, dual-backside deep trench isolation, and enhanced composite metal grid, were proposed and employed, to realize the world's smallest 0.43 µm pitch quad-photodiode pixel, achieving exceptional performance metrics of full well capacity of 6000 e-, dark current of 1.3 e-/s, and read noise of 1.5 e-rms, without degradation in conversion gain. 

A 2-layer, 0.7μm-pitch Dual Photodiode Pixel CMOS Image Sensors with Metaphotonic Color Router

In this article, a world’s smallest 0.7μm-pitch dual photodiode pixel is presented. We integrated 2-layer pixel with hybrid Cu-Cu bonding process only, without introducing pixel-level deep contacts. By optimizing layout of Cu pad layer, we suppressed capacitive coupling between neighboring floating diffusion nodes, still achieved similar conversion gain compared to that of 0.7μm-pitch, 1-layer single photodiode pixel. We overcome the degradation of the auto-focus (AF) separation ratio by incorporating multi-focal, metaphotonic color routers (MPCR).

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Link: https://www.sony-semicon.com/en/info/2025/2025112701.html

Sony Semiconductor Solutions to Release Approx. 200-Effective Megapixel Image Sensor for Mobile Applications with Built-in AI Technology Achieving high definition and high image quality for high-powered zooming on monocular cameras

Atsugi, Japan — Sony Semiconductor Solutions Corporation (Sony) today announced the upcoming release of the 1/1.12-type large-format LYTIA 901 mobile image sensor with a high resolution of approximately 200-effective megapixels.*1 This product uses a pixel array format that delivers both high resolution and high sensitivity, further incorporates an image processing circuit utilizing AI technology within the sensor. It achieves high-definition image quality even with high-powered zooming of up to 4x on monocular cameras and offers new experiential value when shooting on mobile cameras.

Main Features
■Approximately 200-effective megapixels and Quad-Quad Bayer Coding (QQBC) array deliver both high resolution and high sensitivity
 The new sensor uses a pixel pitch of 0.7 μm for an approximately 200-effective megapixel resolution on a 1/1.12 large-format sensor. Advances in pixel structure and color filter design increase the saturation signal level, contributing to improved dynamic range.
 To leverage the high resolution of approximately 200-effective megapixels, the new product employs a Quad-Quad Bayer Coding (QQBC) array in which 16 (4×4) adjacent pixels are clustered with filters of the same color. During normal shooting, the signals of the 16 clustered pixels are processed as a single pixel unit, allowing the camera to maintain high sensitivity even at night and in dim indoor shooting conditions. On the other hand, during zoom shooting, a form of array conversion processing known as remosaicing reverts the clustered pixels to a normal pixel array, to deliver high-resolution imaging. 

 

■Equipped with an AI learning-based remosaicing function for high quality imaging while zooming
Array conversion processing (remosaicing), which reverts the QQBC array to a normal pixel array, requires extremely advanced calculation processes. For this product, Sony has developed a new AI learning-based remosaicing for the QQBC array and mounted the processing circuit inside the sensor, for another Sony industry-first.*2 This new technology makes it possible to process high-frequency component signals, which are generally difficult to reproduce, offering superior reproduction of details such as fine patterns and letters. Furthermore,incorporating AI learning-based remosaicing directly in the sensor enables high-speed processing and up to 30 fps high- quality video capture when shooting with up to 4x zoom in 4K resolution.

■High dynamic range and rich tonal expression enabled by various HDR technologies
 DCG-HDR and Fine12bit ADC technologies deliver high dynamic range and tonal expressions across the entire zoom range up to 4x
 In addition to Dual Conversion Gain‐HDR (DCG-HDR) technology, which composites data read at different gain settings in a single frame, the new sensor is equipped with Fine12bit ADC (AD converter) technology that improves the quantization bit depth from the conventional 10 bits to 12. These features deliver a high dynamic range and rich tonal expression across the entire zoom range up to 4x.
 HF-HDR technology delivers over 100 dB*3 high dynamic range performance
 Hybrid Frame-HDR (HF-HDR) is an HDR technology that composites frames captured in short exposures with DCG data on a post-processing application processor. HF-HDR significantly improves the dynamic range compared to conventional HDR technology, delivering performance of over 100 dB.*3 This significantly suppresses highlight blowout in bright areas, as well as blackout in dark areas, delivering images that more closely resemble what the human eye actually sees. 

 

 

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Link: https://www.teradyne.com/2025/11/11/invisible-interfaces/

Invisible Interfaces: The Hidden Challenge Behind Every Great Image Sensor

Flexible, future-ready test strategies are crucial to the irregular cycle of sensor design and standards development.

Alexander Metzdorf, Teradyne Inc. 

When you snap a photo on your phone or rely on a car’s camera for lane detection, you’re trusting an unseen network of technologies to deliver or interpret image data flawlessly. But behind the scenes, the interface between the image sensor and its processor is doing the heavy lifting, moving megabtyes of data without error or delay.

While much of the industry conversation focuses on advances in resolution and sensor technology, another challenging aspect of modern imaging innovation is the interfaces—the invisible pathways that connect these sensors to the systems around them, including the processors tasked with interpreting their data. One of the most pressing and underappreciated imaging challenges lies in the ability of the interfaces to handle growing demands for speed, bandwidth, and reliability. The challenge isn’t one-size-fits-all. Smartphone cameras may need ultra-high resolution over short distances, while automotive sensors prioritize robustness and wider areas.

As image sensors and the technologies used to interpret the data evolve to deliver higher resolutions and even integrate artificial intelligence directly onto the chip, these interfaces are under more pressure than ever before. The challenge is both technical and practical: how do you design and test interfaces that must support vastly different applications, from the low-power demands of smartphones to the rugged, long-distance requirements of automotive systems?

And even more critically, how do you keep up when the rules change every few months?

The Growing Challenge in Image Sensor Development

The industry’s insatiable appetite for higher resolutions is well known, but what often goes unnoticed is the corresponding explosion in data traffic. A single image sensor on a smartphone might capture 500 megabytes of data in one shot. In automotive systems, that sensor could be sending critical visual information across several meters of cabling to a centralized processor, where decisions like emergency braking or obstacle detection happen in real-time. Industrial imaging is pushing resolutions even higher (up to 500 megapixels in some cases) to support inspection and automation systems, creating enormous data handling and processing demands.

Each of these scenarios represents wildly different demands on the interfaces connecting sensors to the rest of the system. In smartphones, the processor is typically located just millimeters away from the image sensor. Power efficiency is paramount, and interfaces must support blisteringly fast data rates to process high-resolution images without draining the battery. In an automotive application, a vehicle’s safety system might require those same sensors to transmit data over longer distances, and deliver real-time information and decision-making in harsh environments, while meeting stringent reliability and safety standards.

It’s a challenge compounded by the fact that image sensor manufacturers rarely control these interface requirements. Industry-wide, sensor manufacturers are generally forced to adopt a growing variety of interface standards and proprietary solutions, each with unique requirements for bandwidth, distance, latency, and power consumption.

This creates a relentless cycle of adaptation, where manufacturers are forced to develop and validate new interfaces almost as quickly as they can design the sensors themselves. It’s not uncommon for entirely new interface requirements to be handed down with lead times as short as six months. Unpredictability follows for both image sensor designers and the teams responsible for testing these devices.

The Shift Toward Proprietary Interfaces

While MIPI remains the dominant open standard for image sensor interfaces, proprietary protocols are growing. These custom protocols are typically developed privately by major technology companies to support their unique product requirements, for example, to achieve specific performance advantages. These custom interfaces are closely guarded secrets and often remain entirely undocumented outside of the companies that develop them, making it extremely difficult for test equipment vendors to keep pace.
Even a full teardown of a high-end smartphone won’t reveal how its camera interfaces are engineered. Yet, despite having no access to these underlying specifications, test teams are still expected to validate sensor performance against them.

For manufacturers and test engineers, this creates a near-constant state of uncertainty. New protocols can emerge rapidly and without warning, and must be supported almost immediately, which can cause test equipment providers to scramble to retool systems.

Teradyne’s Approach: Flexibility as a Strategic Imperative

Teradyne has set out to solve this challenge, developing a modular, future-ready approach that gives manufacturers the flexibility they need to thrive in unpredictable environments.

At the hardware level, Teradyne’s UltraSerial20G capture instrument for the UltraFLEXplus is designed for adaptability. Its modular architecture allows changes in key components and software to quickly accommodate new protocols.

Additional flexibility is added with Teradyne’s IG-XL software. Customers are empowered to develop highly customized test strategies, controlling every detail of the testing process, from voltage and timing to signal slopes and data handling.

The Path Ahead: Staying Competitive in a Fragmented, Fast-moving Market

For image sensor makers, the message is clear: choose test platforms that are prepared for proprietary protocols, evolving standards, and ever-tighter time-to-market demands.

In this landscape, Teradyne’s modular hardware and powerful, agile software ensure that manufacturers are meeting current demands and are prepared for whatever comes next. With early interface testing capabilities and scalable solutions that can adapt on the fly, Teradyne customers stay ahead of integration risks, control costs, and accelerate time-to-market.

In an industry where speed, innovation, and reliability are everything, that kind of flexibility is more than just a technical feature. It’s a strategic necessity that offers manufacturers the freedom to innovate, knowing they have the flexibility they need in their test solutions.

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A-SSCC 2025 - Circuit Insights #4: Introduction to CMOS Image Sensors - Prof. Chih-Cheng Hsieh

About Circuit Insights: Circuit Insights features internationally renowned researchers in circuit design, who will deliver engaging and accessible lectures on fundamental circuit concepts and diverse application areas, tailored to a level suitable for senior undergraduate students and early graduate students. The event will provide a valuable and inspiring opportunity for those who are considering or pursuing a career in circuit design.

About the Presenter: Chih-Cheng Hsieh received the B.S., M.S., and Ph.D. degrees from the Department of Electronics Engineering, National Chiao Tung University, Hsinchu, Taiwan, in 1990, 1991, and 1997, respectively.,From 1999 to 2007, he was with an IC Design House, Pixart Imaging Inc., Hsinchu. He led the Mixed-Mode IC Department, as a Senior Manager and was involved in the development of CMOS image sensor ICs for PC, consumer, and mobile phone applications. In 2007, he joined the Department of Electrical Engineering, National Tsing Hua University, Hsinchu, where he is currently a Full Professor. His current research interests include low-voltage low-power smart CMOS image sensor IC, ADC, and mixed-mode IC development for artificial intelligence (AI), internet of things (IoT), biomedical, space, robot, and customized applications.,Dr. Hsieh serves as a TPC member of ISSCC and A-SSCC, and an Associate Editor of IEEE Solid–State Circuit Letters (SSC-L) and IEEE Circuits and Systems Magazine (CASM). He was the SSCS Taipei Chapter Chair and the Student Branch Counselor of NTHU, Taiwan.

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In a recent paper titled "An Extended Time-Mode Digital Pixel CMOS Image Sensor for IoT Applications" Kim et al from Yonsei University write:

Time-mode digital pixel sensors have several advantages in Internet-of-Things applications, which require a compact circuit and low-power operation under poorly illuminated environments. Although the time-mode digitization technique can theoretically achieve a wide dynamic range by overcoming the supply voltage limitation, its practical dynamic range is limited by the maximum clock frequency and device leakage. This study proposes an extended time-mode digitization technique and a low-leakage pixel circuit to accommodate a wide range of light intensities with a small number of digital bits. The prototype sensor was fabricated in a 0.18 μm standard CMOS process, and the measurement results demonstrate its capability to accommodate a 0.03 lx minimum light intensity, providing a dynamic range figure-of-merit of 1.6 and a power figure-of-merit of 37 pJ/frame·pixel. 

Sensors 2025, 25(23), 7228; https://doi.org/10.3390/s25237228

 

Figure 1. Operation principle of conventional CISs: (a) voltage mode; (b) fixed reference; and (c) ramp-down TMD.Figure 2. Theoretical photo-transfer curve of conventional 6-bit TMDs.Figure 3. The operation principle of the proposed E-TMD technique.Figure 4. Theoretical photo-transfer curve of the proposed E-TMD: (a) TS = TU = TD = 2000tCK, Δ = 0; (b) TS = TU = TD = 100tCK, Δ = 0; (c) TS = TU = 0, TD = 45tCK, Δ = 0; and (d) TS = 0, TU = 25tCK, TD = 45tCK, Δ = 0.7.Figure 5. The conventional time-mode digital pixel CIS adapted from [11]: (a) architecture; (b) pixel schematic diagram.Figure 6. Architecture and schematic diagram of the proposed time-mode digital pixel CIS.Figure 7. Operation of the proposed time-mode digital pixel CIS with α representing VDD-vREF-VT: (a) six operation phases and (b) timing diagram.Figure 8. Transistor-level simulated photo-transfer curve comparison.

Figure 9. Chip micrograph.

 

Figure 10. Captured sample images: (a) 190 lx, TS = 17 ms, tCK = 50 µs; (b) 1.9 lx, TS = 400 ms, tCK = 2 µs.Figure 11. Captured sample images and their histograms: (a) 20.5 lx, TS = 32.6 ms; (b) 200.6 lux, TS = 4.6 ms; (c) 2106 lux, TS = 0.64 ms; (d) 2106 lux, TS = 0.64 ms, TU = 0.74 ms, TD = 1.84 ms, Δ = 0.5.

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ISSCC 2026 will be held Feb 15-19, 2026 in San Francisco, CA.

The advance program is now available: https://submissions.mirasmart.com/ISSCC2026/PDF/ISSCC2026AdvanceProgram.pdf 

Session 7 Image Sensors and Ranging (Feb 16)

Session Chair: Augusto Ximenes, CogniSea, Seattle, WA
Session Co-Chair: Andreas Suess, Google, Mountain View, CA

54×42 LiDAR 3D-Stacked System-On-Chip with On-Chip Point
Cloud Processing and Hybrid On-Chip/Package-Embedded 25V
Boost Generation

VoxCAD: A 0.82-to-81.0mW Intelligent 3D-Perception dToF SoC
with Sector-Wise Voxelization and High-Density Tri-Mode eDRAM
CIM Macro

A Multi-Range, Multi-Resolution LiDAR Sensor with
2,880-Channel Modular Survival Histogramming TDC and Delay
Compensation Using Double Histogram Sampling

A 480×320 CMOS LiDAR Sensor with Tapering 1-Step
Histogramming TDCs and Sub-Pixel Echo Resolvers

A 26.0mW 30fps 400x300-pixel SWIR Ge-SPAD dToF Range
Sensor with Programmable Macro-Pixels and Integrated
Histogram Processing for Low-Power AR/VR Applications

A 128×96 Multimodal Flash LiDAR SPAD Imager with Object
Segmentation Latency of 18μs Based on Compute-Near-Sensor
Ising Annealing Machine

A Fully Reconfigurable Hybrid SPAD Vision Sensor with 134dB
Dynamic Range Using Time-Coded Dual Exposures

A 55nm Intelligent Vision SoC Achieving 346TOPS/W System
Efficiency via Fully Analog Sensing-to-Inference Pipeline

A 1.09e--Random-Noise 1.5μm-Pixel-Pitch 12MP Global-Shutter-
Equivalent CMOS Image Sensor with 3μm Digital Pixels Using
Quad-Phase-Staggered Zigzag Readout and Motion
Compensation

A 200MP 0.61μm-Pixel-Pitch CMOS Imager with Sub-1e- Readout
Noise Using Interlaced-Shared Transistor Architecture and
On-Chip Motion Artifact-Free HDR Synthesis for 8K Video
Applications

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Ubicept Extends Availability of Perception Technology to Make Autonomous Systems Using Conventional Cameras More Reliable

Computer vision processing unlocks higher quality, more trustworthy visual data for machines whether they use advanced sensors from Pi Imaging Technology or conventional vision systems

BOSTON--(BUSINESS WIRE)--Ubicept, the computer vision startup operating at the limits of physics, today announced the release of the Ubicept Toolkit, which will bring its physics-based imaging to any modern vision system. Whether for single-photon avalanche diode (SPAD) sensors in next-generation vision systems or immediate image quality improvements with existing hardware, Ubicept provides a unified, physics-based approach that delivers high quality, trustworthy data.

“Ubicept’s technology revolutionizes how machines see the world by unlocking the full potential of today's and tomorrow's image sensors. Our physics-based approach captures the full complexity of motion, even in low-light or high-dynamic-range conditions, providing more trustworthy data than AI-based video enhancement,” said Sebastian Bauer, CEO of Ubicept. “With the Ubicept Toolkit, we’re now making our advanced single-photon imaging more accessible for a broad range of applications from robotics to automotive to industrial sensing.”

Ubicept’s solution is designed for the most advanced sensors to maximize image data quality and reliability. Now, the Toolkit will support any widely available CMOS camera with raw uncompressed output, giving perception developers immediate quality gains.

“Autonomous systems need a better way to understand the world. Our mission is to turn raw photon data into outputs that are specifically designed for computer vision, not human consumption,” said Tristan Swedish, CTO of Ubicept. “By making our technology available for more conventional vision systems, we are giving engineers the opportunity to experience the boost in reliability now while creating an easier pathway to SPAD sensor adoption.”

SPAD sensors – traditionally used in 3D systems – are poised to reshape the image sensor and computer vision landscape. While the CMOS sensor market is projected to grow to $30B by 2029 at 7.5% CAGR, the SPAD market is growing nearly three times faster, expected to reach $2.55B by 2029 at 20.1% CAGR.

Pi Imaging Technology is a leader in the field with its SPAD Alpha, a next-generation 1-megapixel single-photon camera that delivers zero read noise, nanosecond-level exposure control, and frame rates up to 73,000 fps. Designed for demanding scientific applications, it offers researchers and developers extreme temporal precision and light sensitivity. The Ubicept Toolkit builds on these strengths by transforming the SPAD Alpha’s raw photon data into clear, ready-to-use imagery for perception and analysis.

“Ubicept shares our deep commitment to advancing perception technology,” said Michel Antolović, CEO of Pi Imaging Technology. “By combining our SPAD Alpha’s state-of-the-art hardware with Ubicept’s real-time processing, perception engineers can get the most from what single-photon imaging has to offer.”

The Toolkit provides engineering teams with everything they need to visualize, capture, and process video data efficiently with the Ubicept Photon Fusion (UPF) algorithm. The SPAD Toolkit also includes Ubicept’s FLARE (Flexible Light Acquisition and Representation Engine) firmware for optimized photon capture. In addition, the Toolkit includes white-glove support to early adopters for a highly personalized and premium experience.

The Ubicept Toolkit will be available in December 2025. To learn how it can elevate perception performance and integrate into existing workflows, contact Ubicept here.

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ByteDance Image Sensor Digital Design Lead- Pico	San Jose, California, USA	Link
ST Microelectronics Silicon Photonics Product Development Engineer	Grenoble, France	Link
DigitalFish Senior Systems Engineer, Cameras/Imaging	Sunnyvale, California, USA [Remote]	Link
Imasenic Digital IC Design Engineer	Barcelona, Spain	Link
Meta Technical Program Manager, Camera Systems	Sunnyvale, California, USA	Link
Westlake University Ph.D. Positions in Dark Matter & Neutrino Experiments	Hangzhou, Zhejiang, China	Link
General Motors Advanced Optical Sensor Test Engineer	Warren, Michigan, USA [Hybrid]	Link
INFN Post-Doc senior research grant in experimental physics	Frascati, italy	Link
Northrop Grumman Staff EO/IR Portfolio Technical Lead	Melbourne, Florida, USA	Link

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An avid reader of the blog shared this handy little search tool for image sensors: 

https://www.camemaker.com/shop

Although it isn't comprehensive (only covers a few companies), you can filter by various sensor specs. Try it out? 

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Gregor Lenz (roboticist, and cofounder of Open Neuromorphic and Neurobus) has written a two-part blogpost that readers of ISW might find enlightening:

https://lenzgregor.com/posts/event-cameras-2025-part1/

https://lenzgregor.com/posts/event-cameras-2025-part2/ 

Gregor goes into various application domains where event cameras have been tried, but faced challenges, technical and otherwise.

Wide adoption will depend less and less on technical merit and more on how well the new sensor modality will fit into existing pipelines for X where X can be supply chain, hardware, software, manufacturing, assembly, testing, ...  pick your favorite!

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Quantum Photonics Conference, Networking and Trade Exhibition - 5-6 May 2026 - Erfurt, Germany - Website

Sensors Converge - 5-7 May 2026 - Santa Clara, California, USA -  Website

LOPS 2026 - 8-9 May 2026 - Chicago, Illinois, USA - Website

Embedded Vision Summit - 11-13 May 2026 - Santa Clara, California, USA - Website

CLEO - Congress on Lasers and Electro-Optics - 17-20 May 2026 - Charlotte, North Carolina, USA 

IEEE International Symposium on Robotic and Sensors Environments - 18-19 May 2026 - Norfolk, Virginia, USA - Website

IEEE International Symposium on Integrated Circuits and Systems - 24-27 May 2026 - Shanghai, China - Website

ALLSENSORS 2026 - 24-28 May 2026 - Venice, Italy - Website

Robotics Summit and Expo - 27-28 May 2026 - Boston, Massachusetts, USA - Website

If you know about additional local conferences, please add them as comments.

Return to Conference List index

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Metalenz and UMC Bring Breakthrough Face Authentication Solution Polar ID to Mass Production

Boston, MA and Hsinchu, TAIWAN, November 12, 2025 - Metalenz, the leader in metasurface innovation and commercialization, and United Microelectronics Corporation (“UMC” NYSE: UMC, TWSW:2303), a leading global semiconductor foundry, today announced Metalenz’s its breakthrough face authentication solution, Polar ID, is now ready for mass production through UMC.

Polar ID is a compact, polarization-based biometric solution that leverages Metalenz’s metasurface technology to bring payment-grade security and advanced sensing capabilities to any device, even the most challenging of form factors. Using a polarization sensitive meta-optic and advanced algorithms, Polar ID extracts additional information sets such as material and contour information to provide secure face authentication in a single image, dramatically reducing cost and complexity over existing secure face unlock solutions.

Metalenz has already demonstrated the product, featuring a polarization sensitive meta-optic directly integrated onto an image sensor, on a smartphone reference platform powered by Snapdragon® mobile processors. UMC manufactures the meta-optic layer using its 40nm process and achieves sensor integration utilizing its wafer-on-wafer bonding technology. Leveraging UMC’s 300mm wafer manufacturing capabilities, as well as the qualification of this supply chain, Metalenz is ready to ramp into volume positioning Polar ID for widespread adoption across consumer electronics, mobile, and IoT platforms.

“By combining our metasurface innovation with UMC’s manufacturing scale and process maturity, Polar ID is ready to meet the demands of high-volume consumer electronics, and to bring secure, affordable face authentication to billions of devices,” said Rob Devlin, CEO and Co-Founder of Metalenz. “Metalenz is the critical enabler of the metasurface market. With the first generation of our technology already at work in the market replacing lens stacks in existing sensing solutions, we are now leveraging the unique capabilities of our technology to bring new forms of sensing to mass markets for the first time. With demand for secure and convenient biometrics rapidly expanding across consumer devices and IoT, Polar ID delivers secure face authentication in the smallest, simplest form factor, making advanced sensing accessible beyond premium tiers and in places it wasn’t previously possible.”

“Our state-of-the art 12-inch facilities and comprehensive portfolio of semiconductor manufacturing process technologies have made us the foundry partner of choice for some of the most advanced fabless semiconductor companies in the world. We have worked with Metalenz on commercializing their metasurface technology since 2021, and we are pleased to be their key manufacturing partner to support the high-volume production of next-generation polarization imaging modules,” said Steven Hsu, Vice President of Technology Development, UMC. “This collaboration will enable UMC to expand our offering into sensor integrated metasurfaces and play a pioneering role in delivering this disruptive imaging technology to market.”

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Unfortunately the registration deadline has already passed, but I'm posting the program here if it's of interest to the blog readers.

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Source: https://photorumors.com/2025/10/24/the-latest-updates-on-the-sigma-foveon-x3-sensor-with-111-technology/ 

Some updates from Foveon in a new video interview posted on YouTube:

• Sigma is “still working on the development of the sensor” [17:00].
• Current status: The project is still in the “technology development” stage [17:11]. They have not yet started the design of the actual, final sensor [17:11].
• Focus: The team is currently working on the “design of the pixel architecture” [17:20].
• Delays: The project has been “a little bit delayed” [17:30] because as they test prototype wafers, they encounter “technical issues” [17:53].
• Development team: The sensor development is now being handled primarily by the Sigma Japan engineering team [18:02].
• Path forward: Mr. Yamaki mentions that the technical problems “have been narrowing down” [18:12]. Once the team is confident that the technology is ready, they will start the final sensor design and move toward production [18:23]. 

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Zhang et al from the National University of Singapore published a paper titled "Flexible InGaAs/InAlAs avalanche photodiodes for short-wave infrared detection" in Nature Communications. 
a Comparison of the Jdark–V curves under flat and bent conditions where the bending radius range from 5 to 1 cm. b Comparison of the Jtotal–V characteristics with the incident power of −39.6 dBm at the same bending radius. c Comparison of the dark current at 95% of Vbr at the same bending conditions. d Comparison of the breakdown voltage at the same bending conditions. e Comparison of the responsivity at unity gain at 1550 nm at the same bending conditions. f The multiplication gain at 95% of Vbr. The J–V curves show no significant changes at all flat and bent conditions.

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https://imaging.org/IST/Conferences/CIC/CIC2025/CIC_Home.aspx

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https://newsroom.st.com/media-center/press-item.html/p4728.html

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 Smarter Cameras Imply Safer Streets? | Interview with Wade Appelman, Owl Autonomous Imaging 

 Pedestrian deaths at night are rising around the world — and traditional car sensors aren't enough to stop them. In this eye-opening interview, we speak with Wade Appelman, Chief Business Officer at Owl Autonomous Imaging, about how thermal imaging and AI are working together to change that.

We explore:

•     Why pedestrian safety at night is now a global concern
•     How thermal cameras and convolutional neural networks (CNNs) work in real-time to detect people in low light
•     How these smart systems integrate with vehicle software using ROS
•     The technology behind monocular vision and its use in autonomous driving
•     How advanced camera sensors are built — from microbolometers to full-scale production
•     Real-world testing results from Detroit and Las Vegas that show how thermal vision outperforms lidar, radar, and regular RGB cameras

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The Single-Photon Challenge announced yesterday (Oct 19) at ICCV 2025 is a first-of-its-kind benchmark and open competition for photon-level imaging and reconstruction.

The competition is now open! The submission deadline is April 1, 2026 (AOE) and winners will be announced in summer 2026. 

The challenge provides access to single-photon datasets and a public leaderboard to benchmark algorithms for photon-efficient vision: https://SinglePhotonChallenge.com

For this image reconstruction challenge you will need to come up with a novel and creative ways to transform many single-photon camera frames into a single high quality image. This setting is very similar to traditional burst imaging but taken to its extreme limit. Instead of a few burst images you have access to a thousand, but the catch is that each input frame is extremely noisy.  

There are thousands of dollars in prizes to win, thanks to the sponsors Ubicept and Singular Photonics.

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https://semiconductor.samsung.com/image-sensor/mobile-image-sensor/isocell-hp5/

Specifications:

Effective Resolution
 16,384 x 12,288 (200MP) 
Pixel Size
 0.5 μm 
Optical Format
 1/1.56" 
Color Filter
 Tetra²pixel RGB Bayer Pattern 
Normal Frame Rate
 7.5 fps @full, 30 fps @50MP, 90 fps @12.5MP 
Video Frame Rate
 30 fps @8K, 120 fps @4K, 480 fps @FHD (w/o AF) 
Shutter Type
 Electronic rolling shutter 
ADC Accuracy
 10-bit 
Supply Voltage
 2.2 V for analog, 1.8 V for I/O, 1.0 V for digital core supply 
Operating Temperature
 -20℃ to +85℃ 
Interface
 4 lanes (4.5 Gbps per lane) D-PHY / 3 trios (4 Gsps per trio) C-PHY 
Chroma
 Tetra²pixel 
Autofocus
 Super QPD (PDAF) 
HDR
 Smart-ISO Pro (iDCG), Staggered HDR 
Output Formats
 RAW8, RAW10, RAW12, RAW14 
Analog Gain
 16x @full, 256x @12.5MP

Excerpt from Baidu news (translated with Google translate): 

Samsung releases ISOCELL HP5, the world's first 0.5µm ultra-fine pixel 200 million image sensor

...  Samsung officially released the new 200-megapixel image sensor ISOCELL HP5, which is expected to be the first telephoto camera of the OPPO Find X9 Pro mobile phone.

... ISOCELL HP5 sensor is 1/1.56 inches in size, has an ultra-high resolution of 16384 x 12288, and compresses the unit pixel size to 0.5 microns. It is Samsung's first 200 million image sensor in the world equipped with 0.5µm ultra-micro pixels.

To overcome the challenges posed by small pixels, ISOCELL HP5 integrates multiple cutting-edge technologies. Among them, dual vertical transfer gate (D-VTG) and front deep trench isolation (FDTI) technologies work together to effectively increase the full well capacity of each pixel, or its ability to accommodate light signals. 

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Yokoyama et al. from Tower Semiconductor have posted a preprint titled "Charge Domain Type 2.2um BSI Global Shutter Pixel with Dual Depth DTI Produced by Thick-Film Epitaxial Process":

Abstract: We developed a 2.2um Backside Illuminated (BSI) Global Shutter (GS) pixel with true charge-domain Correlated Double Sampling (CDS). A thick-film epitaxial deep DTI (Deep Trench Isolation) process was implemented to enhance 1/PLS (Parasitic Light Sensitivity) using a dual depth DTI structure.
The thickness of the epitaxial substrate was 8.5 um. This structure was designed using optical simulation. By using a thick epitaxial substrate, it is possible to reduce the amount of light that reaches the memory node. Dual-depth DTI, which shallows the DTI depth on the readout side, makes it possible to read signals from the PD to the memory node smoothly. To achieve this structure, we developed a process for thick epitaxial substrate, and the dual-depth DTI can be fabricated with a single mask. This newly developed pixel represents the smallest ever charge-domain GS pixel to date. Despite its compact size, this pixel achieved high QE (83%) and 1/PLS of over 10,000. The pixel maintains 80% of its peak QE at ±15 degrees. 1/PLS is stable even when the F# is small.

Full paper: https://sciprofiles.com/publication/view/7ae02d55ce8f3721ebfc8c35fb871d97 

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IEEE 23rd International Symposium on Biomedical Imaging - 8-11 April 2026 - London, UK - Website

SPIE Photonics Europe - 12-16 April 2026 - Strasbourg, France - Website

IEEE Silicon Photonics Conference - 13-15 April 2026 - Ottawa, Ontario, Canada - Website

IEEE Custom Integrated Circuits Conference - 19-22 April 2026 - Seattle, Washington, USA - Website

Compound Semiconductor International Conference - 20-22 April 2026 - Brussels. Belgium - Website

SPIE Defense + Security - 26-30 April 2026 - National Harbor, Maryland, USA - Website

If you know about additional local conferences, please add them as comments.

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Recent Optical news article covers a publication by Yang et al. which presents a new technique for capturing high resolution microscopy images of curved surfaces. 

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Recent Optical news article covers a publication by Yang et al. which presents a new technique for capturing high resolution microscopy images of curved surfaces. 

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Optics has long played a central role in information processing, from early analog computing systems to modern optical imaging and communication platforms. Recent advancements in nanofabrication and wavefront control have enabled a new class of ultrathin optical elements known as metasurfaces, which significantly expand the design space for manipulating light. By tailoring local phase, amplitude, and polarization responses at subwavelength scales, metasurfaces offer a compact and highly controllable platform for performing complex transformations on optical wavefronts.

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Original article here: https://gori.me/iphone/iphone-news/161745

(Translated using google translate)

TDK's TMR Sensor is the Secret to iPhone Cameras, Tim Cook Praises About Japanese Technology

TDK reveals thirty years of technology accumulation and manufacturing process that competitors cannot imitate at the first release of Apple Yokohama Technology Center 

Apple CEO Tim Cook visited the Apple Yokohama Technology Center (YTC) in Tsunashima, Yokohama, during his visit to Japan. This is the first time that the facility has been opened to the public, and the reality of a state-of-the-art research and development center with about 6,000 square meters of lab space and a clean room has been revealed.

On the same day, YTC presented four of Apple's leading companies—TDK, AGC, Kyocera, and Sony Semiconductor Solutions— that support Apple’s innovation. Tim Cook told reporters, “Apple will never be happy with the situation. Continue to ask for something better. The same goes for Japanese companies. We will never be satisfied and will continue to develop with the aim of always further advancement,” he said, emphasizing the importance of collaborative relationships with Japanese companies. 

The partnership between TDK and Apple began before the first iPod and has been in a long-term relationship for more than three decades. Today, almost all Apple products use TDK technology, and contribute to a wide range of fields, including batteries, pass filters, inductors, microphones, and various sensors.

It’s worth noting that TDK uses 100% renewable energy in all of its products it manufactures for Apple products. In the background of the beautiful photo shoot of the iPhone, which is usually used casually, and the ultra-compact part called the TMR sensor developed by TDK functions as a technology that all iPhone users benefit from.

TMR sensor stands for “Tunnel Magnetoresistance Sensor” and is an ultra-small sensor that detects changes in magnetic fields with extremely high sensitivity. It is so small that it contains fifty thousand wine glasses in a glass of wine, and it is a size that is almost invisible to the naked eye.

The principle of operation of this sensor applies quantum mechanical phenomena. To put it simply, it is a structure in which an ultra-thin insulator is sandwiched between two magnetic materials , and the electrical resistance changes dramatically due to changes in the external magnetic field. Compared to conventional Hall elements, the TMR sensor has reached about a hundred times the sensitivity of the TMR sensor, and it is characterized by extremely clear reactions such as "zero or one". 

The experience of automatically focusing on the moment you launch the camera app on your iPhone and point the lens at the subject will be "natural" that many users feel on a daily basis. However, in this background, the TMR sensor accurately grasps the position of the lens in a thousandth of a second.

The specific mechanisms are as follows. When the lens moves back and forth, a small magnet moves with the lens. The TMR sensor detects the distance change with this magnet as a change in the magnetic field as a change in the magnetic field, and instantly grasps where the lens is now. The camera system makes appropriate focus adjustments by "detecting the position" rather than measuring the distance.

The TMR sensor, which was first used for autofocus applications on the iPhone X, has also been applied to the sensor shift image stabilization (OIS) from the iPhone 12 series. The minute movement due to the camera shake is also instantly detected, and the sensor itself is operated to correct it.

The latest iPhone 17 series also uses TMR sensors for the center stage function of the front camera, which detects the fine movement of the lens in real time with a sensitivity of 100 times more than general hall elements. 

An easy example of an easy-to-understand TMR sensor is the joystick of the game controller. In the conventional joystick, a mechanical part called a "potension meter" is used, and the angle is detected by physical contact.

On the other hand, joysticks using TMR sensors operate non-contact , which greatly improves response speed and accuracy. In addition, since there is no mechanical wear, it also realizes durability that does not deteriorate in accuracy even if it is used for a long time.

The structure of the TMR sensor itself can be understood by the competitors when it is disassembled. However, it is very difficult to actually produce an equivalent product. The reason is TDK’s proprietary manufacturing process technology.

Semiconductor-based equipment is used for manufacturing, but it is not the equipment itself that is important. Combining multiple specialized technologies such as TMR deposition, magnetic material plating, and dry etching, the process of creating a unique layered structure that detects the magnetic field from which direction it detects the magnetic field from and which direction does it not perceives it is at the core. 

Modern smartphones are becoming thinner, and many magnets are used inside. There may be concerns about whether the delicate TMR sensor will work properly in this environment.

TDK cooperates closely from the customer's design stage to propose optimal sensor placement and design . The influence of the magnetic field is rapidly weakened by simply securing 1cm of physical distance, so the interference problem can be solved with proper design. He visits Cupertino fourteen times a year, and his close work with Apple’s camera team is proof of that. 

TDK has leveraged ninety years of expertise in magnetic materials to establish this process technology. The TMR sensors manufactured at the Asama Techno Plant in Japan also have a sustainable manufacturing system using 100% renewable energy.

The background of each photo that iPhone users casually take is the result of such a long-term accumulation of Japanese precision technology and technology. TMR sensors are by no means a prominent component, but they will continue to evolve as an important technology that supports the modern smartphone experience. 

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