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UArizona Imaging Technology Laboratory's sensor processing capabilities

 

KAIST  Design parameters of freeform color splitters for image sensors

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In a paper titled "Robust Pixel Design Methodologies for a Vertical Avalanche Photodiode (VAPD)-Based CMOS Image Sensor" Inoue et al. from Panasonic Japan write:

We present robust pixel design methodologies for a vertical avalanche photodiode-based CMOS image sensor, taking account of three critical practical factors: (i) “guard-ring-free” pixel isolation layout, (ii) device characteristics “insensitive” to applied voltage and temperature, and (iii) stable operation subject to intense light exposure. The “guard-ring-free” pixel design is established by resolving the tradeoff relationship between electric field concentration and pixel isolation. The effectiveness of the optimization strategy is validated both by simulation and experiment. To realize insensitivity to voltage and temperature variations, a global feedback resistor is shown to effectively suppress variations in device characteristics such as photon detection efficiency and dark count rate. An in-pixel overflow transistor is also introduced to enhance the resistance to strong illumination. The robustness of the fabricated VAPD-CIS is verified by characterization of 122 different chips and through a high-temperature and intense-light-illumination operation test with 5 chips, conducted at 125 °C for 1000 h subject to 940 nm light exposure equivalent to 10 kLux. 

 

Open access link to full paper:  https://www.mdpi.com/1424-8220/24/16/5414

Cross-sectional views of a pixel: (a) a conventional SPAD and (b) a VAPD-CIS. N-type and P-type regions are drawn by blue and red, respectively.
 

(a) A chip photograph of VAPD-CIS overlaid with circuit block diagrams. (b) A circuit diagram of the VAPD pixel array. (c) A schematic timing diagram of the pixel circuit illustrated in (b).
 (a) An illustrative time-lapsed image of the sun. (b) Actual images of the sun taken at each time after starting the experiment. The test lasted for three hours, and as time passed, the sun, initially visible on the left edge of the screen, moved to the right.

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Apple Japan

Image Sensor Technical Program Manager - Minato, Tokyo-to, Japan - Link

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DPReview: https://www.dpreview.com/news/2948041351/ccd-image-sensor-pioneer-george-e-smith-passes-away-at-95 

NYTimes:  https://www.nytimes.com/2025/05/30/science/george-e-smith-dead.html

George E. Smith died at the age of 95. Working with Willard S. Boyle at Bell Labs, he invented the CCD image sensor technology.

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Eyeo raises €15 million seed round to give cameras perfect eyesight

• Eyeo replaces traditional filters with advanced color-splitting technology originating from imec, world-leading research and innovation hub in nanoelectronics and digital technologies. For the first time, photons are not filtered but guided to single pixels, delivering maximum light sensitivity and unprecedented native color fidelity, even in challenging lighting conditions.
• Compatible with any sensor, eyeo’s single photon guiding technology breaks resolution limits - enabling truly effective sub-0.5-micron pixels for ultra-compact, high-resolution imaging in XR, industrial, security, and mobile applications - where image quality is the top purchasing driver.

Eindhoven (Netherlands), May 7, 2025 – eyeo today announced it has raised €15 million in seed funding, co-led by imec.xpand, Invest-NL, joined by QBIC fund, High-Tech Gründerfonds (HTGF) and Brabant Development Agency (BOM). Eyeo revolutionizes the imaging market for consumer, industrial, XR and security applications by drastically increasing the light sensitivity of image sensors. This breakthrough unlocks picture quality, color accuracy, resolution, and cost efficiency, which was never before possible in smartphones and beyond.

The €15 million raised will drive evaluation kit development, prepare for scale manufacturing of a first sensor product, and expand commercial partnerships to bring this breakthrough imaging technology to market.

The Problem: Decades-old color filter technology throws away 70% of light, crippling sensor performance
For decades, image sensors have relied on the application of red, green, and blue color filters on pixels to make your everyday color picture or video. Color filters, however, block a large portion of the incoming light, and thereby limit the sensitivity of the camera. Furthermore, they limit the scaling of the pixel size below ~0.5 micron. These longstanding issues have stalled advancements in camera technology, constraining both image quality and sensor efficiency. In smartphone cameras, manufacturers have compensated for this limitation by increasing the sensor -and thus camera- size, to capture more light. While this improves low-light performance, it also leads to larger, bulkier cameras. Compact, high-sensitivity image sensors are essential for slimmer smartphones and emerging applications such as robotics and AR/VR devices, where size, power efficiency, and image quality are crucial.

The Breakthrough: Color-splitting via vertical waveguides
Eyeo introduces a novel image sensor architecture that eliminates the need for traditional color filters, making it possible to maximize sensitivity without increasing sensor size. Leveraging breakthrough vertical waveguide-based technology that splits light into colors, eyeo develops sensors that efficiently capture and utilize all incoming light, tripling sensitivity compared to existing technologies. This is particularly valuable in low-light environments, where current sensors struggle to gather enough light for clear, reliable imaging. Additionally, unlike traditional filters that block certain colors (information that is then interpolated through software processing), eyeo’s waveguide technology allows pixels to receive complete color data. This approach instantly doubles resolution, delivering sharper, more detailed images for applications that demand precision, such as computational photography, machine vision, and spatial computing. 

Jeroen Hoet, CEO of eyeo: “Eyeo is fundamentally redefining image sensing by eliminating decades-old limitations. Capturing all incoming light and drastically improving resolution is just the start—this technology paves the way for entirely new applications in imaging, from ultra-compact sensors to enhanced low-light performance, ultra-high resolution, and maximum image quality. We’re not just improving existing systems; we’re creating a new standard for the future of imaging.”

Market Readiness and Roadmap
Eyeo has already established partnerships with leading image sensor manufacturers and foundries to ensure the successful commercialization of its technology. The €15M seed funding will be used to improve its current camera sensor designs further, optimizing the waveguide technology for production scalability and accelerating the development of prototypes for evaluation. By working closely with industry leaders, eyeo aims to bring its advanced camera sensors to a wide range of applications, from smartphones and VR glasses to any compact device that uses color cameras. The first evaluation kits are expected to be available for selected customers within the next two years. 

Eyeo is headquartered in Eindhoven (NL), with an R&D office in Leuven (BE).

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PR Newswire: https://www.prnewswire.com/news-releases/glass-imaging-raises-20-million-funding-round-to-expand-ai-imaging-technologies-302451849.html

Glass Imaging Raises $20 Million Funding Round To Expand AI Imaging Technologies

LOS ALTOS, Calif., May 12, 2025 /PRNewswire/ -- Glass Imaging, a company harnessing the power of artificial intelligence to revolutionize digital image quality, today unveiled a Series A funding round led by global software investor Insight Partners. The $20 million round will allow Glass Imaging to continue to refine and implement their proprietary GlassAI technologies across a wide range of camera platforms - from smartphones to drones to wearables and more. The Series A round was joined by previous Glass Imaging investors GV (Google Ventures), Future Ventures and Abstract Ventures.

Glass Imaging uses artificial intelligence to extract the full image quality potential on current and future cameras by reversing lens aberrations and sensor imperfections. Glass works with manufacturers to integrate GlassAI software to boost camera performance 10x resulting in sharper, more detailed images under various conditions that remain true to life with no hallucinations or optical distortions.

"At Glass Imaging we are building the future of imaging technology," said Ziv Attar, Founder and CEO, Glass Imaging. "GlassAI can unlock the full potential of all cameras to deliver stunning ultra-detailed results and razor sharp imagery. The range of use cases and opportunities across industry verticals are huge."

"GlassAI leverages edge AI to transform Raw burst image data from any camera into stunning, high-fidelity visuals," said Tom Bishop, Ph.D., Founder and CTO, Glass Imaging. "Our advanced image restoration networks go beyond what is possible on other solutions: swiftly correcting optical aberrations and sensor imperfections while efficiently reducing noise, delivering fine texture and real image content recovery that outperforms traditional ISP pipelines."

"We're incredibly proud to lead Glass Imaging's Series A round and look forward to what the team will build next as they seek to redefine just how great digital image quality can be," said Praveen Akkiraju, Managing Director, Insight Partners. "The ceiling for GlassAI integration across any number of platforms and use cases is massive. We're excited to see this technology expand what we thought cameras and imaging devices were capable of." Akkiraju will join Glass Imaging's board and Insight's Jonah Waldman will join Glass Imaging as a board observer.

Glass Imaging previously announced a $9.3M extended Seed funding round in 2024 led by GV and joined by Future Ventures, Abstract and LDV Capital. That funding round followed an initial Seed investment in 2021 led by LDV Capital along with GroundUP Ventures.

For more information on Glass Imaging and GlassAI visit https://www.glass-imaging.com/

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From PR Newswire: https://www.prnewswire.com/news-releases/leopard-imaging-and-sony-semiconductor-solutions-collaborate-to-showcase-li-imx454-multispectral-cameras-at-automate-and-embedded-vision-summit-302452836.html

Leopard Imaging and Sony Semiconductor Solutions Collaborate to Showcase LI-IMX454 Multispectral Cameras at Automate and Embedded Vision Summit

FREMONT, Calif., May 12, 2025 /PRNewswire/ -- Leopard Imaging Inc., a global innovator in intelligent vision solutions, is collaborating with Sony Semiconductor Solutions Corporation (Sony) to present the cutting-edge LI-IMX454 Multispectral Camera at both Automate and Embedded Vision Summit.

Leopard Imaging launched LI-USB30-IMX454-MIPI-092H camera with high-resolution imaging across diverse lighting spectrums, powered by Sony's advanced IMX454 multispectral image sensor. Unlike conventional RGB sensors, Sony's IMX454 image sensor integrates eight distinct spectral filters directly onto each photodiode, allowing the camera to capture light across 41 wavelengths from 450 nm to 850 nm in a single shot utilizing Sony's dedicated signal processing—without the need for mechanical scanning or bulky spectral elements.

Multispectral imaging has historically been underutilized due to cost and complexity. With the LI-IMX454, Leopard Imaging and Sony aim to democratize access to this powerful technology by offering a compact, ready-to-integrate solution for a wide range of industries: from industrial inspection to medical diagnostics, precision agriculture, and many more.

"We're excited to collaborate with Sony to bring this next-generation imaging solution to market," said Bill Pu, President and Co-Founder of Leopard Imaging. "The LI-IMX454 cameras not only deliver high-resolution multispectral data but also integrate seamlessly with AI and machine vision systems for intelligent decision-making."

The collaboration also incorporates Sony's proprietary signal processing software, optimized to support key functions essential to multispectral imaging: defect correction, noise reduction, auto exposure control, robust non-RGB based classification, and color image generation.

Leopard Imaging and Sony will showcase live demos of LI-IMX454 cameras at both Automate and Embedded Vision Summit. To visit Automate: Huntington Place, Booth #8000 on May 12-13. To visit Embedded Vision Summit: Santa Clara Convention Center, Booth #700 on May 21 - 22. To arrange a meeting at the event, please contact marketing@leopardimaging.com.

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IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room-Temperature Semiconductor Detectors Symposium - 1-8 November 2025 - Yokohama, Japan - Website

SPIE Future Sensing Technologies 2025 - 11-13 November 2025 - Yokohama, Japan - Website

14th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detectors (HSTD 14) - 16-21 November 2025 - Taipei, Taiwan - Website

Compamed - 17-20 November 2025 - Dusseldorf, Germany - Website

SEMI MEMS & Imaging Sensors Summit 2025 - 19-20 November 2025 - Munich, Germany - Website

17th Symposium Sensor Data Fusion: Trends, Solutions and Applications - Bonn, Germany - 24-26 November 2025 - Website

RSNA 2025 - 30 November-4 December 2025 - Chicago, Illinois, USA - Website

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

Return to Conference List index

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Global Smartphone CIS Shipments Climb 2% YoY in 2024

Samsung is no longer in the top-3 smartphone CIS suppliers.

•  Global smartphone image sensor shipments rose 2% YoY to 4.4 billion units in 2024.
• Meanwhile, the average number of cameras per smartphone declined further to 3.7 units in 2024 from 3.8 units in 2023.
• Sony maintained its leading position, followed by GalaxyCore in second place and OmniVision in third.
• Global smartphone image sensor shipments are expected to fall slightly YoY in 2025.

 

https://www.counterpointresearch.com/insight/post-insight-research-notes-blogs-global-smartphone-cis-shipments-climbs-2-yoy-in-2024/

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This plenary presentation was delivered at the Electronic Imaging Symposium held in Burlingame, CA over 2-6 February 2025. For more information see: http://www.electronicimaging.org

Title: Imaging in the Age of Artificial Intelligence

Abstract: AI is revolutionizing imaging, transforming how we capture, enhance, and experience visual content. Advancements in machine learning are enabling mobile phones to have far better cameras, enabling capabilities like enhanced zoom, state-of-the-art noise reduction, blur mitigation, and post-capture capabilities such as intelligent curation and editing of your photo collections, directly on device.
This talk will delve into some of these breakthroughs, and describe a few of the latest research directions that are pushing the boundaries of image restoration and generation, pointing to a future where AI empowers us to better capture, create, and interact with visual content in unprecedented ways.

Speaker: Peyman Milanfar, Distinguished Scientist, Google (United States)

Biography: Peyman Milanfar is a Distinguished Scientist at Google, where he leads the Computational Imaging team. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz for 15 years, two of those as Associate Dean for Research. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass. Over the last decade, Peyman's team at Google has developed several core imaging technologies that are used in many products. Among these are the zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution ("Super Res Zoom") pipeline, and several generations of state of the art digital upscaling algorithms. Most recently, his team led the development of the "Photo Unblur" feature launched in Google Photos for Pixel devices.
Peyman received his undergraduate education in electrical engineering and mathematics from the UC Berkeley and his MS and PhD in electrical engineering from MIT. He holds more than two dozen patents and founded MotionDSP, which was acquired by Cubic Inc. Along with his students and colleagues, he has won multiple best paper awards for introducing kernel regression in imaging, the RAISR upscaling algorithm, NIMA: neural image quality assessment, and Regularization by Denoising (RED). He's been a Distinguished Lecturer of the IEEE Signal Processing Society and is a Fellow of IEEE "for contributions to inverse problems and super-resolution in imaging".

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Abstract: This article presents a 3.96- μ m, 640×640 pixel stacked digital pixel sensor capable of capturing co-located monochrome (MONO) and near-infrared (NIR) frames simultaneously in a dual-channel global shutter (GS) operation. A super-pixel structure is proposed with diagonally arranged 2×2 MONO and NIR sub-pixels. To enhance visible light sensitivity, large and small non-uniform micro-lenses are formed on the MONO and NIR sub-pixels, respectively. Each floating diffusion (FD) shared super-pixel is connected to an in-pixel analog-to-digital converter and two banks of 10-bit static random access memories (SRAMs) to enable the dual-channel GS operation. To achieve high dynamic range (DR) in the MONO channel, a triple-quantization (3Q) operation is performed. Furthermore, a single-channel digital-correlated double sampling (D-CDS) 3Q operation is implemented. The fabricated sensor achieved 6.2-mW low power consumption at 30 frames/s with dual-channel capture. The MONO channel achieved 124-dB DR in the 3Q operation and 60 dB for the NIR channel. The sensor fits the stringent form-factor requirement of an augmented reality headset by consolidating MONO and NIR imaging capabilities.

Open access link: https://ieeexplore.ieee.org/document/10706075 

Pixel level co-located MONO and NIR sub-pixels.

 

Sub-pixel and SRAM-bank usage. (a) Dual-channel operation. (b) Single-channel digital-CDS operation.

Fabricated chip. (a) Chip micrograph. (b) Chip top-level block diagram.

 

Photo-response and SNR curves of digital-CDS operation (after linearization).

 

Sample images captured by dual-channel operation. (a) MONO frame (HDR image). (b) NIR frame ( 2× gain for visual).

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Several news sources are repeating a Bloomberg report that Sony is considering partially spinning off its semiconductor business.

https://finance.yahoo.com/news/sony-reportedly-mulling-semiconductor-unit-155046940.html 

Sony Group is contemplating a spinoff of its semiconductor unit, a move that could see Sony Semiconductor Solutions become an independent entity as early as this year, reports Bloomberg. The move, which is still under discussion, is part of the group’s strategy to streamline business operations and concentrate on core entertainment sector. The potential spinoff would involve distributing most of Sony's holding in the chip business to its shareholders while retaining a minority stake.

https://www.trendforce.com/news/2025/04/29/news-sony-reportedly-mulls-chip-division-spinoff-and-listing-to-strengthen-entertainment-focus/ 

According to Bloomberg, sources indicate that Sony Group is weighing the spin-off of its semiconductor subsidiary, Sony Semiconductor Solutions, with an IPO potentially taking place as early as this year. Another report from Bloomberg adds that the move would mark the PlayStation maker’s latest step in streamlining its operations and strengthening its focus on entertainment. As noted by the report, sources indicate that Sony is exploring a “partial spin-off” structure, under which the parent company would retain a stake in the subsidiary.

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In an ArXiV preprint titled "Multi-Length-Scale Dopants Analysis of an Image Sensor via Focused Ion Beam-Secondary Ion Mass Spectrometry and Atom Probe Tomography", Guerguis et al write:

The following article presents a multi-length-scale characterization approach for investigating doping chemistry and spatial distributions within semiconductors, as demonstrated using a state-of-the-art CMOS image sensor. With an intricate structural layout and varying doping types/concentration levels, this device is representative of the current challenges faced in measuring dopants within confined volumes using conventional techniques. Focused ion beam-secondary ion mass spectrometry is applied to produce large-
area compositional maps with a sub-20 nm resolution, while atom probe tomography is used to extract atomic-scale quantitative dopant profiles. Leveraging the complementary capabilities of the two methods, this workflow is shown to be an effective approach for resolving nano- and micro- scale dopant information, crucial for optimizing the performance and reliability of advanced semiconductor devices.

Preprint: https://arxiv.org/pdf/2501.08980 

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In an ArXiV preprint titled "Multi-Length-Scale Dopants Analysis of an Image Sensor via Focused Ion Beam-Secondary Ion Mass Spectrometry and Atom Probe Tomography", Guerguis et al write:

The following article presents a multi-length-scale characterization approach for investigating doping chemistry and spatial distributions within semiconductors, as demonstrated using a state-of-the-art CMOS image sensor. With an intricate structural layout and varying doping types/concentration levels, this device is representative of the current challenges faced in measuring dopants within confined volumes using conventional techniques. Focused ion beam-secondary ion mass spectrometry is applied to produce large-
area compositional maps with a sub-20 nm resolution, while atom probe tomography is used to extract atomic-scale quantitative dopant profiles. Leveraging the complementary capabilities of the two methods, this workflow is shown to be an effective approach for resolving nano- and micro- scale dopant information, crucial for optimizing the performance and reliability of advanced semiconductor devices.

Preprint: https://arxiv.org/pdf/2501.08980 

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 The Fundamentals of CMOS Image Sensors with Richard Crisp 

This video provides a sneak peek of "CMOS Image Sensors: Technology, Applications, and Camera Design Methodology," an SPIE course taught by imaging systems expert Richard Crisp. The course covers everything from the basics of photon capture to sensor architecture and real-world system implementation.
The preview highlights key differences between CCD and CMOS image sensors, delves into common sensor architectures such as rolling shutter and global shutter, and explains the distinction between frontside and backside illumination.
It also introduces the primary noise sources in image sensors and how they can be managed through design and optimization techniques such as photon transfer analysis and MTF assessment.
You'll also see how the course approaches imaging system design using a top-down methodology. This includes considerations regarding pixel architecture, optics, frame rate, and data bandwidth, all demonstrated through practical examples, such as a networked video camera design.
Whether you're an engineer, scientist, or technical manager working with imaging systems, this course is designed to help you better understand the technology behind modern CMOS image sensors and how to make informed design choices. Enjoy!

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Guo et al. from Changchun Institute of Optics, University of Chinese Academy of Sciences, and Gpixel Inc. published a paper titled "Study on 3D Effects on Small Time Delay Integration Image Sensor Pixels" in Sensors.

Abstract: This paper demonstrates the impact of 3D effects on performance parameters in small-sized Time Delay Integration (TDI) image sensor pixels. In this paper, 2D and 3D simulation models of 3.5 μm × 3.5 μm small-sized TDI pixels were constructed, utilizing a three-phase pixel structure integrated with a lateral anti-blooming structure. The simulation experiments reveal the limitations of traditional 2D pixel simulation models by comparing the 2D and 3D structure simulation results. This research validates the influence of the 3D effects on the barrier height of the anti-blooming structure and the full well potential and proposes methods to optimize the full well potential and the operating voltage of the anti-blooming structure. To verify the simulation results, test chips with pixel sizes of 3.5 μm × 3.5 μm and 7.0 μm × 7.0 μm were designed and manufactured based on a 90 nm CCD-in-CMOS process. The measurement results of the test chips matched the simulation data closely and demonstrated excellent performance: the 3.5 μm × 3.5 μm pixel achieved a full well capacity of 9 ke- while maintaining a charge transfer efficiency of over 0.99998.

Paper link [open access]: https://www.mdpi.com/1424-8220/25/7/1953

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 SPAD and SPAD Arrays: Theory, Practice, and Applications

 

The video is a comprehensive webinar on Single Photon Avalanche Diodes (SPADs) and SPAD arrays, addressing their theory, applications, and recent advancements. It is led by experts from the New Jersey Institute of Technology and Hamamatsu, discussing technical fundamentals, challenges, and innovative solutions to improve the performance of SPAD devices. Key applications highlighted include fluorescence lifetime imaging, remote gas sensing, quantum key distribution, and 3D radiation detection, showcasing SPAD's unique ability to timestamp events and enhance photon detection efficiency.

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Recent speculative news article suggest that Samsung is weighing exiting CIS business after recent exit by SK Hynix.

News source: https://www.digitimes.com/news/a20250312PD213/cis-samsung-sk-hynix-business-lsi.html

SK Hynix is shutting down its CMOS image sensor (CIS) business, fueling industry speculation over whether Samsung Electronics will follow suit. Samsung's system LSI division, which oversees its CIS operations, is undergoing an operational diagnosis...

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In this keynote held at the 2024 International Conference on Computational Photography, Prof. Davide Scaramuzza from the University of Zurich presents a visionary keynote on event cameras, which are bio-inspired vision sensors that outperform conventional cameras with ultra-low latency, high dynamic range, and minimal power consumption. He dives into the motivation behind event-based cameras, explains how these sensors work, and explores their mathematical modeling and processing frameworks. He highlights cutting-edge applications across computer vision, robotics, autonomous vehicles, virtual reality, and mobile devices while also addressing the open challenges and future directions shaping this exciting field.
00:00 - Why event cameras matter to robotics and computer vision

07:24 - Bandwidth-latency tradeoff
08:24 - Working principle of the event camera
10:50 - Who sells event cameras
12:27 - Relation between event cameras and the biological eye
13:19 - Mathematical model of the event camera
15:35 - Image reconstruction from events
18:32 - A simple optical-flow algorithm
20:20 - How to process events in general
21:28 - 1st order approximation of the event generation model
23:56 - Application 1: Event-based feature tracking
25:03 - Application 2: Ultimate SLAM
26:30 - Application 3: Autonomous navigation in low light
27:38 - Application 4: Keeping drones fly when a rotor fails
31:06 - Contrast maximization for event cameras
34:14 - Application 1: Video stabilization
35:16 - Application 2: Motion segmentation
36:32 - Application 3: Dodging dynamic objects
38:57 - Application 4: Catching dynamic objects
39:41 - Application 5: High-speed inspection at Boeing and Strata
41:33 - Combining events and RGB cameras and how to apply deep learning
45:18 - Application 1: Slow-motion video
48:34 - Application 2: Video deblurring
49:45 - Application 3: Advanced Driving Assistant Systems
56:34 - History and future of event cameras
58:42 - Reading material and Q&A

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From PetaPixel: https://petapixel.com/2025/04/15/sony-unveils-the-worlds-smallest-and-lightest-lidar-depth-sensor/

Sony announced the AS-DT1, the world’s smallest and lightest miniature precision LiDAR depth sensor.

Measuring a mere 29 by 29 by 31 millimeters (1.14 by 1.14 by 1.22 inches) excluding protrusions, the Sony AS-DT1 LiDAR Depth Sensor relies upon sophisticated miniaturization and optical lens technologies from Sony’s machine vision industrial cameras to accurately measure distance and range. The device utilizes “Direct Time of Flight” (dToF) LiDAR technology and features a Sony Single Photon Avalanche Diode (SPAD) image sensor. 

From the official Sony webpage: https://pro.sony/ue_US/products/lidar/as-dt1

• 1.14 (W) x 1.14 (H) x 1.22 in (D)
• 50 g (1.1 oz)
• Utilizes dToF LiDAR technology
• Single Photon Avalanche Diode (SPAD) sensor
• Range distance of 40 m (131 ft) indoor, 20 m (65.6 ft) outdoor
• Lightweight aluminum alloy housing structure
• 2 USB-C ports
• Connector for external power, UART interface and trigger

• HFoV 30° or more
• Maximum measurement range at 15 fps, 50 percent reflectivity, center: Indoor: 131.23 ft and Outdoor: 65.62 ft
• Measurement accuracy at 10 m: Indoor/Outdoor: ±0.2 in
• Distance resolution: 0.98 in
• Frame rate: 30 fps
• 15 fps @ Maximum ranging distance mode
• Number of ranging points 576（24 x 24）
• Laser wavelength 940 nm
• Dimensions 1.14 (W) x 1.14 (H) x 1.22 in (D) (excluding protrusions)
• Weight 1.1 oz or less

 

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ASNT Annual Conference - 6-9 October 2025 - Orlando, Florida, USA - Website

Scientific Detector Workshop 6-10 October 2025 - Canberra, Australia - Website

AutoSens Europe - 7-9 October 2025 - Barcelona, Spain - Website

SPIE/COS Photonics Asia - 12-14 October 2025 - Beijing, China - Website

BioPhotonics Conference - 14-16 October 2025 - Online - Website 

IEEE Sensors Conference - 19-22 October 2025 - Vancouver, British Columbia, Canada - Website 

Optica Laser Congress and Exhibition - 19-23 October 2025 - Prague, Czech Republic - Website

OPTO Taiwan - 22-24 October 2025 - Taipei, Taiwan - Website

Image Sensors Asia - 30-31 October 2025 - Seoul, South Korea - Website

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