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SeekingAlpha: ST reports its 2017 earnings, including the imaging business:

"In Imaging, 2017 was a year of continued success as revenue grew triple digit year-over-year. Our proprietary time-of-flight technology gained traction and we released our third generation of laser ranging sensors. Also our specialized 3D sensing technology ramped in volume for a major customer and we also won designs for depth sensing time-of-flight solution to support assisted driving with a tier-1 automotive supplier, certainly a priority for us.

...We also successfully ramped up an important time-of-flight product and specialized imaging sensor, addressing a 3D sensing application for a major customer. For 2018, we are focused on the further development of our next generation of imaging technologies. We will launch a new Single Photon Avalanche Diode, SPAD, in 40-nanometer technology, enabling a step change in performance of time-of-flight application. We will also secure the following generation with a 3D SPAD, further boosting the performance, scalability and enabling higher resolution time-of-flight sensors.

This is key for multiple markets from 3D sensing to LIDAR. We are also developing our next generation of global shutter technologies with significant performance improvements in near infrared light detection, as well as our Q2 [ph] image sensor with backlight and 3D integration for visible and non-visible light application."

The outgoung ST CEO Carlo Bozotti says "I believe for the next 10 years, we see imaging becoming more and more important in the automotive and we want to be there and I think we can be there with our technologies... I believe that with our imaging technology, we can certainly contribute to the new ways of autonomous driving, the LIDARs, the three sensors."

The company's 2017 earnings presentation also sets targets for 2018:

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Nikkei overviews Sony IEDM 2017 presentation on 3-layer stacked process flow for the fast image sensor presented at 2017 ISSCC:


The processing flow is:

• The pixel, DRAM and logic wafers are manufactured using 90nm, 30nm and 40nm processes, respectively.
• The DRAM wafer and the logic wafer are joined together, and the thickness of the DRAM wafer is reduced to 3μm
• The DRAM and logic wafers are electrically connected with TSVs
• The stacked wafers of the DRAM and logic are joined to the pixel wafer
• The 3-layer wafer stack is thinned down to 130um and connected with TSVs

The numbers of TSVs connecting the pixel layer with DRAM is ~15,000 and DRAM with logic layer is ~20,000. Both of the TSVs have a diameter of 2.5μm and a pitch of 6.3μm. The 1/2.3-inch sensor has a resolution of 21.3MP and pixel size of 1.22um.

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Imec announces its very first SWIR hyperspectral camera. The SWIR camera integrates CMOS-based spectral filters together with InGaAs-based imagers, thus combining the compact and low-cost capabilities of CMOS technology with the spectral range of InGaAs.

Semiconductor CMOS-based hyperspectral imaging filters, as designed and manufactured by imec for the past five years, have been utilized in a manner where they are integrated monolithically onto silicon-based CMOS image sensors, which has a sensitivity range from 400 – 1000 nm visible and near-IR (VNIR) range. However, it is expected that more than half of commercial multi and hyperspectral imaging applications need discriminative spectral data in the 1000 – 1700 nm SWIR range.

“SWIR range is key for hyperspectral imaging as it provides extremely valuable quantitative information about water, fatness, lipid and protein content of organic and inorganic matters like food, plants, human tissues, pharmaceutical powders, as well as key discriminatory characteristics about plastics, paper, wood and many other material properties,” commented Andy Lambrechts, program manager for integrated imaging activities at imec. “It was a natural evolution for imec to extend its offering into the SWIR range while leveraging its core capabilities in optical filter design and manufacturing, as well as its growing expertise in designing compact, low-cost and robust hyperspectral imaging system solutions to ensure this complex technology delivers on its promises.”

Imec’s initial SWIR range hyperspectral imaging cameras feature both linescan ‘stepped filter’ designs with 32 to 100 or more spectral bands, as well as snapshot mosaic solutions enabling the capture of 4 to 16 bands in real-time at video-rate speeds.

“The InGaAs imager industry is at a turning point,” explained Jerome Baron, business development manager of integrated imaging and vision systems at imec. “As the market recognizes the numerous applications of SWIR range hyperspectral imaging cameras beyond its traditional military, remote sensing and scientific niche fields, the time is right for organizations such as imec to enable compact, robust and low-cost hyperspectral imaging cameras in the SWIR range too. Imec’s objectives will be to advance this offering among the most price sensitive volume markets for this technology which include food sorting, waste management and recycling, industrial machine vision, precision agriculture and medical diagnostics.”

Imec’s hyperspectral imaging SWIR range camera preliminary results: a) SWIR camera, b) 100+ spectral bands measurement in 1.1 – 1.7um range after calibration c) live RAW image of one human hand with 640 x 480 pixels spatial resolution and 100+bands stepped filters deposited and patterned at pixel level d) Spectral plot showing skin reflectance values after scanning.

Hyperspectral imaging in SWIR range with imec’s LS 100+ bands in 1.1 – 1.7µm range enables classification of nuts versus their nut’s shells.

Hyperspectral imaging in SWIR range with imec LS 100+ bands in 1.1 – 1.7um range enables classification of various different textiles.

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PRNewswire: While facial recognition is widely seen as a major step forward for the technology, some VCs invest into counter-face recognition development. Tel Aviv, Israel-based D-ID startup developing deep learning solution to protect identities from face recognition technologies announces the completion of a $4m seed round.

D-ID, which stands for de-identification, claims to have developed an solution that produces images, which are unrecognizable to facial recognition algorithms while keeping them indistinguishable to the human eye and is designed to be difficult for AI to overcome.

"Our biometric data is being collected and used irresponsibly by governments and organizations. D-ID is here to change that," says Gil Perry, Co-Founder and CEO of D-ID.

"The growing sophistication of facial recognition technologies are turning our faces into passwords. But these passwords aren’t protected and cannot be changed once compromised. D-ID offers a system that protects images from unauthorized, automated face recognition.

Images are processed in a ground-breaking way that causes face recognition algorithms to fail to identify the subject in the image, while maintaining enough similarity to the original image for humans not to notice the difference."

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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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