How to Explain Hyperspectral Imaging to Your Family and Relatives

Finnish company Specim publishes a nice 2-part video explanation of hyperpectral imaging principles. Like in many popular videos, there are some minor mistakes, but the overall work is still quite good:

Image Sensing Content at ISSCC 2019

ISSCC 2019 to be held on February 17-21 in San Francisco publishes its program with a number of image sensor papers. The image Sensor session starts with Smartsens presentation, probably the first image sensor company from China presenting its work at ISSCC:

A Stacked Global-Shutter CMOS Imager with SC-Type Hybrid-GS Pixel and Self-Knee Point Calibration Single-Frame HDR and On-Chip Binarization Algorithm for Smart Vision Applications
C. Xu, Y. Mo, G. Ren, W. Ma, X. Wang, W. Shi, J. Hou, K. Shao, H. Wang, P. Xiao, Z. Shao, X. Xie, X. Wang, C. Yiu
SmartSens Technology

Energy-Efficient Low-Noise CMOS Image Sensor with Capacitor Array-Assisted Charge-Injection SAR ADC for Motion-Triggered Low-Power IoT Applications
K. D. Choo, L. Xu, Y. Kim, J-H. Seol, X. Wu, D. Sylvester, D. Blaauw
University of Michigan, Ann Arbor, MI

A Data-Compressive 1.5b/2.75b Log-Gradient QVGA Image Sensor with Multi-Scale Readout for Always-On Object Detection
C. Young, A. Omid-Zohoor, P. Lajevardi, B. Murmann
Stanford University, Stanford, CA; Robert Bosch, Sunnyvale, CA

A 76mW 500fps VGA CMOS Image Sensor with Time-Stretched Single-Slope ADCs Achieving 1.95e- Random Noise
I. Park, C. Park, J. Cheon, Y. Chae,
Yonsei University, Seoul, Korea
Kumoh National Institute of Technology, Gyeongbuk, Korea

Dual-Tap Pipelined-Code-Memory Coded-Exposure-Pixel CMOS Image Sensor for Multi-Exposure Single-Frame Computational Imaging
N. Sarhangnejad, N. Katic, Z. Xia, M. Wei, N. Gusev, G. Dutta, R. Gulve, H. Haim, M. Moreno Garcia, D. Stoppa, K. N. Kutulakos, R. Genov
University of Toronto, Toronto, Canada; Synopsys, Toronto, Canada; Fondazione Bruno Kessler, Trento, Italy; ams AG, Ruschlikon, Switzerland

A 400×400-Pixel 6μm-Pitch Vertical Avalanche Photodiodes CMOS Image Sensor Based on 150ps-Fast Capacitive Relaxation Quenching in Geiger Mode for Synthesis of Arbitrary Gain Images
Y. Hirose, S. Koyama, T. Okino, A. Inoue, S. Saito, Y. Nose, M. Ishii, S. Yamahira, S. Kasuga, M. Mori, T. Kabe, K. Nakanishi, M. Usuda, A. Odagawa, T. Tanaka
Panasonic, Nagaokakyo, Japan

A 256×256 40nm/90nm CMOS 3D-Stacked 120dB-Dynamic-Range Reconfigurable Time-Resolved SPAD Imager
R. K. Henderson, N. Johnston, S. W. Hutchings, I. Gyongy, T. Al Abbas, N. Dutton, M. Tyler, S. Chan, J. Leach
University of Edinburgh, Edinburgh, United Kingdom; STMicroelectronics, Edinburgh, United Kingdom; Heriot-Watt University, Edinburgh, United Kingdom

A 32×32-Pixel 0.9THz Imager with Pixel-Parallel 12b VCO-Based ADC in 0.18μm CMOS
S. Yokoyama, M. Ikebe, Y. Kanazawa, T. Ikegami, P. Ambalathankandy, S. Hiramatsu, E. Sano, Y. Takida, H. Minamide
Hokkaido University, Sapporo, Japan; RIKEN, Sendai, Japan

A 512-Pixel 3kHz-Frame-Rate Dual-Shank Lensless Filterless Single- Photon-Avalanche-Diode CMOS Neural Imaging Probe
C. Lee, A. J. Taal, J. Choi, K. Kim, K. Tien, L. Moreaux, M. L. Roukes, K. L. Shepard
Columbia University, New York, NY; KIST, Seoul, Korea; California Institute of Technology, Pasadena

The Industry Showcase event includes:

• ams AG, Premstätten, Austria, Direct Time-of-Flight Module in CMOS 55nm HV for Mobile Applications
• Ouster, San Francisco, CA, Native camera imaging on LiDAR and deep learning enablement
• Samsung Electronics, Hwaseong, Korea, Motion Artifact Free Dynamic Vision Sensor for Machine Vision

Automotive Gesture Recognition Market

GlobeNewswire: Global Market Insights forecasts that automotive gesture recognition market will grow at about 44% CAGR from 2018 to 2024 led by rising trend towards customer comfort and advanced driving experience. The market is expected to reach $13.6bn by 2024 from $1bn in 2017:

Event-Based Sensor Use Case

Neuro Vision spin-off from Zurich University Institut de la Vision, Paris, shows a use case for an event-based camera:

Aeye LiDAR Shows 1000m Track Detection, Raises $40m

Techchrunch, Optics.org, VentureBeat: AEye raises a $40m in Series B. round led by Taiwania Capital, the investment firm created and backed by Taiwan’s National Development Council, and includes returning investors Kleiner Perkins, Intel Capital, Airbus Ventures and Tychee Partners.

This brings the LiDAR startup’s total funding to about $61m. In the announcement, founder and CEO Luis Dussan said Taiwania’s investment is a strategic one and will give AEye more access to manufacturing, logistics and tech resources in Asia. AEye also plans to launch a new product at CES in January.

In tests monitored and validated by VSI Labs, a research company that focuses on autonomous-vehicle technology, AEye said that its iDAR sensor, which combines a solid-state lidar and high-resolution camera in one device, was able to detect and track a white color moving truck from one kilometer away. AEye claims that this is four to five times the distance other current lidar systems can detect.

In a press statement, AEye chief of staff Blair LaCorte said the company believes iDAR can potentially track moving objects, including trucks and drones, from 5km to 10km away.

Sony Adds Some Data on its DSLR/ILC Sensors

Sony publishes flyers for 8 new products for DSLR/ILC cameras spanning from 150MP medium format IMX411 to 20MP 60fps MFT IMX272 sensors, including full-frame and APS-C sensors:

IWISS2018 Posters List

4th International Workshop on Image Sensors and Imaging Systems (IWISS2018) to be held on Nov. 28-29 in Tokyo, publishes the list of posters:

(1)	[Poster Presentation] Joint optimization for compressive video sensing and reconstruction under hardware constraints	Michitaka Yoshida (Kyushu Univ.), Akihiko Torii, Masatoshi Okutomi (Tokyo Tech), Kenta Endo, Yukinobu Sugiyama (Hamamatsu Photonics K. K.), RIn-ichiro Taniguchi (Kyushu Univ.), Hajime Nagahara (Osaka Univ.)
(2)	[Poster Presentation] Multi-band plasmonic color filtering through nanostructured metal thin film for RGB-NIR image sensor	Atsutaka Miyamichi, Atsushi Ono, Keiichiro Kagawa, Keita Yasutomi, Shoji Kawahito(Shizuoka Univ.)
(3)	[Poster Presentation] CMOS Image Sensor with Pseudorandom Pixel Placement For Jaggy Elmination	Junichi Akia (Kanazawa Univ.)
(4)	[Poster Presentation] [Poster Presentaion] Tissue measurement based on a multi-tap CMOS image sensor with binary structured light	Yuki Nishioka, Keiichiro Kagawa, Keita Yasutomi, Shoji Kawahito (Shizuoka Univ.)
(5)	[Poster Presentation] [Poster Presentation] On a BSI Image Signal Accumulation Sensor of 100 Mfps	Nguyen Ngo, Kazuhiro Morimoto, Kazuhiro Shimonomura, Takeharu Goji Etoh(Ritsumeikan Univ.)
(6)	[Poster Presentation] Measuring the Shape and Reflectance of Real Objects for Reproducing the Material Appearance	Zar Zar Tun, Seiji Tsunezaki, Takashi Komuro(Saitama Univ.), Shoji Yamamoto (TMCIT), Norimichi Tsumura (Chiba Univ.)
(7)	[Poster Presentation] [Poster Presentation] Evolution of ultra-high-speed image sensors- Light-in-Flight imaging to Ultra-fast X-ray imaging	Kazuhiro Morimoto, Nguyen Ngo, Kazuhiro Shimonomura, Takeharu Etoh (Ritsumeikan Univ.)
(8)	[Poster Presentation] Visualization of time-of-flight signals with normalized and calibrated phasor plot	Ryota Miyagi, Keiichiro Kagawa, Yuta Murakami (Shizuoka Univ.), Hajime Nagahara(Osaka Univ.), Keita Yasutomi, Shoji Kawahito(Shizuoka Univ.)
(9)	[Poster Presentation] A real-time stress monitoring system using a high sensitivity camera	Leyi Tan, Masashi Hakamata, Chen Cao, Keiichiro Kagawa (Shizuoka univ), Norimichi Tsumura (Chiba univ), Shoji Kawahito(Shizuoka univ)
(10)	[Poster Presentation] A low noise global shutter CMOS image sensor with multiple sampling	Hiroyuki Yamaguchi, Toshinori Otaka, Takayuki Hamamoto (TUS)
(11)	[Poster Presentation] Measuring the advertisement effect based on emotional responses analysis with crowdsourcing	Kenta Masui, Genki Okada, Norimichi Tsumura(Chiba Univ.)
(12)	[Poster Presentation] Implantable CMOS Image Sensor Using Multilayer Filter Emission and Fiber Coupled Laser Excitation	Erus Rustami, Yasumi Ohta, Kiyotaka Sasagawa, Makito Haruta (NAIST), Toshihiko Noda (TUT), Takashi Tokuda, Jun Ohta(NAIST)
(13)	[Poster Presentation] Multi-storied photodiode CMOS image sensor with wire grid polarizer to detect polarization information	Yoshiaki Takemoto, Yusuke Yamamoto, Yoshiharu Ajiki, Yusaku Koyama, Hideki Kato, Toru Kondo, Haruhisa Saito, Yuichi Gomi, Seisuke Matsuda, Yoshitaka Tadaki (Olympus)
(14)	[Poster Presentation] [Poster Presentation] 3D-Sensing Technology based on Lensless Light-Field Imaging with Fresnel Zone Aperture	Keita Yamaguchi, Yusuke Nakamura, Kazuyuki Tajima, Toshiki Ishii, Koji Yamasaki, Takeshi Shimano (Hitachi, Ltd.)
(15)	[Poster Presentation] A high-sensitivity CMOS image sensor with front-side inner-lens	Godeun Seok, Yunkyung Kim (Dong-A Univ.)
(16)	[Poster Presentation] [Poster Presentation] Lensless imaging by coded image sensors	Tomoya Nakamura (Tokyo Tech.), Keiichiro Kagawa (Shizuoka Univ.), Shiho Torashima, Masahiro Yamaguchi (Tokyo Tech.)
(17)	Improvement of Imaging system for hand-waving finger vein authentication	Hiroyuki Suzuki, Jumpei Nagata, Kazuki Hirai(Tokyo Tech.), Keiichiro Kagawa (Shizuoka Univ.), Takashi Obi, Nagaaki Ohyama (Tokyo Tech.), Takashi Komuro (Saitama Univ.)
(18)	[Poster Presentation] A 4-tap Lock-in Pixels with a high NIR Sensitivity for Long Range Time-of-Flight Range Sensor	Sanggwon Lee, Keita Yasutomi, Masato Morita, Shoji Kawahito (Shizuoka Univ.)
(19)	Development of a mobile multi-spectral compound-eye camera TOMBO	Tetsuya Nakanishi (Osaka Univ.), Keiichiro Kagawa (Shizuoka Univ.), Yasuo Masaki(ASAHI E.L.), Jun Tanida (Osaka Univ.)
(20)	[Poster Presentation] Averaging Pixel Current Calibration Algorithm for Reducing Fixed Pattern Noise in the Bolometer-Type Uncooled Infrared Image Sensor	Sang-Hwan Kim, Byoung-Soo Choi, Jimin Lee, Junwoo Lee, Jewon Lee (Kyungpook National Univ.), Jae-Hyoun Park, Kyoung-Il Lee (KETI), Jang-Kyoo Shin (Kyungpook National Univ.)
(21)	[Poster Presentation] Effects of Aperture Width on the Performance of Monochrome CMOS Image Sensor Using Offset Pixel Aperture Technique for Depth Extraction	Jimin Lee, Byoung-Soo Choi, Sang-Hwan Kim, Jewon Lee, Junwoo Lee (Kyungpook National Univ.), Seunghyuk Chang, JongHo Park, Sang-Jin Lee (CISS), Jang-Kyoo Shin (Kyungpook National Univ.)
(22)	[Poster Presentation] [Poster Presentation] A Current-Mode Differential Sensing CMOS Imager for Optical Linear Encoder	You-Shin Chen, Tzu-Hsiang Hsu (NTHU), Chien-Wen Chen (ITRI), Chih-Cheng Hsieh(NTHU)

Dual-Gate Organic Phototransistor for Image Sensing

Nature publishes a paper "Dual-gate organic phototransistor with high-gain and linear photoresponse" Philip C. Y. Chow, Naoji Matsuhisa, Peter Zalar, Mari Koizumi, Tomoyuki Yokota, and Takao Someya from Hong Kong University of Science and Technology, Holst Centre (The Netherlands), and University of Tokyo.

"The conversion of light into electrical signal in a photodetector is a crucial process for a wide range of technological applications. Here we report a new device concept of dual-gate phototransistor that combines the operation of photodiodes and phototransistors to simultaneously enable high-gain and linear photoresponse without requiring external circuitry. In an oppositely biased, dual-gate transistor based on a solution-processed organic heterojunction layer, we find that the presence of both n- and p-type channels enables both photogenerated electrons and holes to efficiently separate and transport in the same semiconducting layer. This operation enables effective control of trap carrier density that leads to linear photoresponse with high photoconductive gain and a significant reduction of electrical noise. As we demonstrate using a large-area, 8 × 8 imaging array of dual-gate phototransistors, this device concept is promising for high-performance and scalable photodetectors with tunable dynamic range."

Human Eye Resolution in Megapixels

Quora publishes an answer on human eye resolution question written by Michael Bross, former Pychology Professor at Concordia University, Montreal, among 93 other answers. Few interesting quotes:

"...if you look of what is going on in the eye it looks messy, the ‘seeing’ is done by the visual cortex.

Note that the light has to pass trough several structures before it gets to the retina, cornea, aqueous humor, lens, vitreous humor (humors are a translucent gel/watery like medium), blood vessels, and then it has to traverse 4 layers of nerve cells before it gets to the light receptors (rods and cones) at the back of the retina.

So plenty of photons get absorbed before reaching the receptors, add to this that quite a few of them will be bouncing around in the eye ball, and it has been estimated that only around 20–25% of light entering the eye reaches the receptors.

So to put that into pixel estimates (I’m relying here on data from Hendrik Lensch at the Max Plank Institute Informatik), given a 19″ LED viewed at 60 cm. without hyperacuity the visual cortex would process Pixel 3,000x3,000 pixels, with hyperacuity 18,000x18,000."

High Photon Throughput SPAD Imager

MDPI Special Issue The International SPAD Sensor Workshop publishes a paper "A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for Single-Photon Counting and 3D Time-of-Flight Imaging" by Chao Zhang, Scott Lindner, Ivan Michel Antolovic, Martin Wolf, and Edoardo Charbon from Delft University of Technology, University of Zurich, EPFL, and Kavli Institute of Nanoscience.

"Per-pixel time-to-digital converter (TDC) architectures have been exploited by single-photon avalanche diode (SPAD) sensors to achieve high photon throughput, but at the expense of fill factor, pixel pitch and readout efficiency. In contrast, TDC sharing architecture usually features high fill factor at small pixel pitch and energy efficient event-driven readout. While the photon throughput is not necessarily lower than that of per-pixel TDC architectures, since the throughput is not only decided by the TDC number but also the readout bandwidth. In this paper, a SPAD sensor with 32 × 32 pixels fabricated with a 180 nm CMOS image sensor technology is presented, where dynamically reallocating TDCs were implemented to achieve the same photon throughput as that of per-pixel TDCs. Each 4 TDCs are shared by 32 pixels via a collision detection bus, which enables a fill factor of 28% with a pixel pitch of 28.5 μm. The TDCs were characterized, obtaining the peak-to-peak differential and integral non-linearity of −0.07/+0.08 LSB and −0.38/+0.75 LSB, respectively. The sensor was demonstrated in a scanning light-detection-and-ranging (LiDAR) system equipped with an ultra-low power laser, achieving depth imaging up to 10 m at 6 frames/s with a resolution of 64 × 64 with 50 lux background light."