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ON Semi publishes a promotional video on its EMCCD sensors:

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Teledyne Dalsa shows how different the world is in LWIR band:

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AutosSens 2017 publishes Sony Director of Product Marketing for Automotive Sensing, Abhay Rai, presentation on requirements to image sensors for autonomous driving. The presentation also shows Sony image sensor division acquisitions and achievements:




Update: Sony publishes a web page dedicated to its automotive image sensor features.

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ON Semi publishes Giri Venkat talk at AutoSense 2017 on Cybersecurity Considerations for Autonomous Vehicles Sensors:

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MDPI Special Issue Special Issue on the 2017 International Image Sensor Workshop publishes University of Edinburgh and ST paper "Single-Photon Tracking for High-Speed Vision" Istvan Gyongy, Neale A.W. Dutton, and Robert K. Henderson.

"Quanta Imager Sensors provide photon detections at high frame rates, with negligible read-out noise, making them ideal for high-speed optical tracking. At the basic level of bit-planes or binary maps of photon detections, objects may present limited detail. However, through motion estimation and spatial reassignment of photon detections, the objects can be reconstructed with minimal motion artefacts. We here present the first demonstration of high-speed two-dimensional (2D) tracking and reconstruction of rigid, planar objects with a Quanta Image Sensor, including a demonstration of depth-resolved tracking."

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MDPI Special Issue on the 2017 International Image Sensor Workshop publishes Panasonic paper "A Real-Time Ultraviolet Radiation Imaging System Using an Organic Photoconductive Image Sensor" by Toru Okino, Seiji Yamahira, Shota Yamada, Yutaka Hirose, Akihiro Odagawa, Yoshihisa Kato, and Tsuyoshi Tanaka.

"We have developed a real time ultraviolet (UV) imaging system that can visualize both invisible UV light and a visible (VIS) background scene in an outdoor environment. As a UV/VIS image sensor, an organic photoconductive film (OPF) imager is employed. The OPF has an intrinsically higher sensitivity in the UV wavelength region than those of conventional consumer Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS) or Charge Coupled Devices (CCD). As particular examples, imaging of hydrogen flame and of corona discharge is demonstrated. UV images overlapped on background scenes are simply made by on-board background subtraction. The system is capable of imaging weaker UV signals by four orders of magnitude than that of VIS background. It is applicable not only to future hydrogen supply stations but also to other UV/VIS monitor systems requiring UV sensitivity under strong visible radiation environment such as power supply substations."

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ST publishes a couple of videos devoted to 3D image creation: Structure-from-Motion



Fusing data from camera, depth sensor and inertial sensors



FlightSense ToF Technology

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AutoSens publishes ARM Senior Manager of Image Quality, Alexis Lluis Gomez, presentation on automotive ISP:

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Embedded Vision Alliance publishes Infineon and PMD demo of the world’s smallest 3D ToF module from the CES:

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Samsung mobile image sensor page unveils its 3-layer stacked image sensor capturing 1080p video at 480fps:


Thanks to DJ for the link!

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Samsung patent application US20180012069 "Fingerprint sensor, fingerprint sensor package, and fingerprint sensing system using light sources of display panel" by Dae-young Chung, Hee-chang Hwang, Kun-yong Yoon, Woon-bae Kim, Bum-suk Kim, Min Jang, Min-chul Lee, and Jung-woo Kim proposes an optical fingerprint image sensor under an OLED display panel:

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MDPI Special Issue on IISW 2017 publishes ST paper "A 750 K Photocharge Linear Full Well in a 3.2 μm HDR Pixel with Complementary Carrier Collection" by Frédéric Lalanne, Pierre Malinge, Didier Hérault, Clémence Jamin-Mornet, and Nicolas Virollet.

"The native HDR pixel concept based on a parallel electron and hole collection for, respectively, a low signal level and a high signal level is particularly well-suited for this performance challenge. The theoretical performance of this pixel is modeled and compared to alternative HDR pixel architectures. This concept is proven with the fabrication of a 3.2 μm pixel in a back-side illuminated (BSI) process including capacitive deep trench isolation (CDTI). The electron-based image uses a standard 4T architecture with a pinned diode and provides state-of-the-art low-light performance, which is not altered by the pixel modifications introduced for the hole collection. The hole-based image reaches 750 kh+ linear storage capability thanks to a 73 fF CDTI capacitor. Both images are taken from the same integration window, so the HDR reconstruction is not only immune to the flicker issue but also to motion artifacts."

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AutoSens publishes a video of Espros CEO Beat De Coi presentation of a pulsed ToF sensor in October 2017:

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Intel begins shipping two RealSense D400 Depth Cameras from its next-generation D400 product family: the D415 and D435, based on previously announced D400 3D modules.

RealSense D415

Intel is also offering its D4 and D4M (mobile version) depth processor chips for stereo cameras:

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SeekingAlpha publishes an analysis of the recent ams business moves:

"ams has assembled strong capabilities in 3D sensing - one of the strongest emerging new opportunities in semiconductors. 3D sensing can detect image patterns, distance, and shape, allowing for a wide range of uses, including facial recognition, augmented reality, machine vision, robotics, and LIDAR.

Although ams is not currently present in the software side, the company has recently begun investing in software development as a way to spur future adoption. Ams has also recently begun a collaboration with Sunny Optical, a leading Asian sensor manufacturer, to take advantage of Sunny's capabilities in module manufacturing.

At this point it remains to be seen how widely adopted 3D sensing will be; 3D sensing could become commonplace on all non-entry level iPhones in a short time and likewise could gain broader adoption in Android devices. What's more, there is the possibility of adding 3D sensing to other consumer devices like tablets, not to mention adding 3D sensing to the back of phones in future models."

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Ben Gurion University, Israel, researches implement a physically impossible thing - converting regular RGB consumer camera images into hyperspectral ones, purely by software. Their paper "Sparse Recovery of Hyperspectral Signal from Natural RGB Images" by Boaz Arad and Ohad Ben-Shahar presented at European Conference on Computer Vision (ECCV) in Amsterdam, The Netherlands, in October 2016, says:

"We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input."


"The goal of our research is the reconstruction of the hyperspectral data from natural images from their (single) RGB image. Prima facie, this appears a futile task. Spectral signatures, even in compact subsets of the spectrum, are very high (and in the theoretical continuum, infinite) dimensional objects while RGB signals are three dimensional. The back-projection from RGB to hyperspectral is thus severely underconstrained and reversal of the many-to-one mapping performed by the eye or the RGB camera is rather unlikely. This problem is perhaps expressed best by what is known as metamerism – the phenomenon of lights that elicit the same response from the sensory system but having different power distributions over the sensed spectral segment.

Given this, can one hope to obtain good approximations of hyperspectral signals from RGB data only? We argue that under certain conditions this otherwise ill-posed transformation is indeed possible; First, it is needed that the set of hyperspectral signals that the sensory system can ever encounter is confined to a relatively low dimensional manifold within the high or even infinite-dimensional space of all hyperspectral signals. Second, it is required that the frequency of metamers within this low dimensional manifold is relatively low. If both conditions hold, the response of the RGB sensor may in fact reveal much more on the spectral signature than first appears and the mapping from the latter to the former may be achievable.

Interestingly enough, the relative frequency of metameric pairs in natural scenes has been found to be as low as 10^−6 to 10^−4. This very low rate suggests that at least in this domain spectra that are different enough produce distinct sensor responses with high probability.

The eventual goal of our research is the ability to turn consumer grade RGB cameras into a hyperspectral acquisition devices, thus permitting truly low cost and fast HISs."

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Teledyne Dalsa publishes a nice demo of its 1MP 30fps X-Ray sensor:

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SD Optics publishes two videos of depth sensing by means of fast focus variations of its MEMS lens:

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Imec article on 3D bonding technology by Eric Beyne, imec fellow & program director 3D system integration presents solutions that are supposed to reach 100nm contact pitch:

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Sensors and Materials publishes a paper "Complementary Metal Oxide Semiconductor Image Sensor Using Gate/Body-tied P-channel Metal Oxide Semiconductor Field Effect Transistor-type Photodetector for High-speed Binary Operation" by Byoung-Soo Choi, Sang-Hwan Kim, Jimin Lee, Chang-Woo Oh, Sang-Ho Seo, and Jang-Kyoo Shin from Kyungpook National University, Korea.

"In this paper, we propose a CMOS image sensor that uses a gate/body-tied p-chnnel metal oxide semiconductor field effect transistor (PMOSFET)-type photodetector for highspeed binary operation. The sensitivity of the gate/body-tied PMOSFET-type photodetector is approximately six times that of the p–n junction photodetector for the same area. Thus, an active pixel sensor with a highly sensitive gate/body-tied PMOSFET-type photodetector is more appropriate for high-speed binary operation."

The 3T-style pixel uses pmos instead of PD and has a non-linear response. Probably, its inherent non-linearity has been the main reason that the binary operation mode is proposed:

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MDPI publishes a paper "Smartphone Spectrometers" by Andrew J.S. McGonigle, Thomas C. Wilkes, Tom D. Pering, Jon R. Willmott, Joseph M. Cook, Forrest M. Mims, and Alfio V. Parisi from University of Sheffield, UK and University of Sydney and University of Southern Queensland, Australia.

"Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a ‘lab in a phone’ capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades."

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GM autonomous car safety report details the sensors on board of Cruise self-driving vehicle: "To perform Perception functions, the vehicle has five LiDARs, 16 cameras and 21 radars. Their combined data provides sensor diversity allowing Perception to see complex environments."

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MDPI Sensors Special Issue on the 2017 International Image Sensor Workshop publishes Brillnics paper "An Over 90 dB Intra-Scene Single-Exposure Dynamic Range CMOS Image Sensor Using a 3.0 μm Triple-Gain Pixel Fabricated in a Standard BSI Process" by Isao Takayanagi, Norio Yoshimura, Kazuya Mori, Shinichiro Matsuo, Shunsuke Tanaka, Hirofumi Abe, Naoto Yasuda, Kenichiro Ishikawa, Shunsuke Okura, Shinji Ohsawa, and Toshinori Otaka.

"To respond to the high demand for high dynamic range imaging suitable for moving objects with few artifacts, we have developed a single-exposure dynamic range image sensor by introducing a triple-gain pixel and a low noise dual-gain readout circuit. The developed 3 μm pixel is capable of having three conversion gains. Introducing a new split-pinned photodiode structure, linear full well reaches 40 ke−. Readout noise under the highest pixel gain condition is 1 e− with a low noise readout circuit. Merging two signals, one with high pixel gain and high analog gain, and the other with low pixel gain and low analog gain, a single exposure dynamic rage (SEHDR) signal is obtained. Using this technology, a 1/2.7”, 2M-pixel CMOS image sensor has been developed and characterized. The image sensor also employs an on-chip linearization function, yielding a 16-bit linear signal at 60 fps, and an intra-scene dynamic range of higher than 90 dB was successfully demonstrated. This SEHDR approach inherently mitigates the artifacts from moving objects or time-varying light sources that can appear in the multiple exposure high dynamic range (MEHDR) approach."

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PRNewswire: Innoviz presents its prototype LiDAR (model Pro) at CES, with quite a complete performance spec, a rarity among LiDAR startups (sans Velodyne):


Looking forward, the company intends to bring the automotive-grade InnovizOne model sometime in 2019. It requires quite a leap in technology to reach the targets in resolution, FOV, range and size set at last year's CES:


Update: As of January 16, 2018, the following design targets are presented for InnovizOne automotive-qualified product on the company page:

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Kazuo Hirai, Sony CEO and President, spent few minutes talking about automotive imaging in his CES keynote:



Samsung announces an open DRVLINE platform that includes "a brand-new ADAS forward-facing camera system, created by Samsung and HARMAN, which is engineered to meet upcoming New Car Assessment Program (NCAP) standards. These include lane departure warning, forward collision warning, pedestrian detection, and automatic emergency braking."


On the sensors side, Samsung DRVLINE partners include 3 LiDAR startups:

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TI publishes Youtube demos on use cases for its ToF image sensors (based on Softkinetic pixels):





PRNewswire: OmniVision and Spreadtrum announced a turnkey active stereo 3D camera reference design for smartphones.

"We anticipate face login to be one of the key features for upcoming mobile devices. Achieving this requires complete camera hardware and software system development, which can be an extremely complicated, resource-intensive and expensive process. The aim of this active stereo 3D camera reference design is to reduce time to market and other key hurdles for our customers," said Sylvia Zhang, head of marketing for OmniVision's mobile segment. "This offering reflects our strong partnership with Spreadtrum and our continued determination to deliver innovative solutions that ease our customers' pain points."

Pairing OmniVision's OV9282 and OV7251 global shutter sensors with the Spreadtrum SC9853 AP has resulted in a compelling active stereo camera solution. SC9853 is based on 14nm octa-core 64-bit Intel Airmont processor architecture.

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Yole Developpement presentation "Machine Vision sensors & cameras competitive landscape" is available for free but requires registration. The June 23, 2017 33-page presentation has many interesting slides on image sensor market:

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Yole Developpement article on CNN and ISP forecasts dramatic growth on vision processor market:

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GlobeNewswire: NXP, LG, and HELLA Aglaia announce a strategic collaboration for automotive vision applications. The new vision platform will be made available to all automakers to promote best-in-class detection and classification of vulnerable road users to save lives.

Many of today’s automotive vision platforms are proprietary, inflexible and provide little room for automakers to differentiate in the global marketplace. They also inhibit further software integration and innovation and generally lock out the ability to combine the best available sensing technology and software sources in the market. The collaborators’ joint development work, led by LG, is based on the conviction that vision platforms must be open and safe to meet NCAP guidelines and to pave the way for level 3 to 5 automated driving.

The camera-based vision system, developed by LG together with NXP and HELLA Aglaia, is designed to detect and classify vulnerable road users such as pedestrians and cyclists and activate auto emergency braking (AEB). The cameras can be attached to the windshield behind a car’s rear-view mirror.

Additionally, the system detects traffic signs, notify the driver of speed limits, monitor lane keeping and facilitate steering correction in case of unintentional drift. NXP’s accelerated vision processing IP, together with algorithm expertise from HELLA Aglaia and LG allow such applications to run at very low latency and within a market leading power envelope.

“Openness and collaboration empower innovation. Now is the time for partners such as LG Electronics, NXP and HELLA Aglaia to combine resources in an open vision system for the safety of this generation and generations to come,” said Lee Woo-jong, president of LG’s Vehicle Components Company. “We look forward to working with NXP and HELLA Aglaia on our camera-based vision system, one that carmakers around the world will use to help bring about the autonomous revolution.”

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