Sony SWIR Presentation

Lucid Vision presents its SWIR camera based on Sony SenSWIR InGaAs stacked sensor:

Q3 Smartphone Sensor Shipments Drop by 24% YoY

Aiji Micro App: According to Qunzhi Consulting’s (English name Sigmaintell) market report, global smartphone CIS shipments in Q3 2021 were approximately 1.21B, a YoY decline of approximately 24.0%. Qunzhi Consulting estimates that global smartphone image sensor shipments in 2021 will be approximately 5.05B, a YoY decrease of 10.3%:

Samsung, DxOMark, and Counterpoint on Mobile Camera Innovations and Trends

Tecno Mobile publishes a webinar "Mobile Camera Trends 2022: Innovation Talk:"

Sony SenSWIR White Paper

Sony publishes a white paper promoting its InGaAs SWIR sensors:

MIPI CSE Adds Functional Safety to Automotive Image Sensor Interface

MIPI Alliance has recently released the Camera Service Extensions (MIPI CSESM) v1.0 specification, which enhances the MIPI Camera Serial Interface 2 (MIPI CSI-2) image sensor interface with end-to-end functional safety and other features for automotive applications:

• A cyclic redundancy check (CRC-32) to detect data transmission errors, ensuring the image data captured by the sensor is accurate when received at the ECU
• A frame counter to detect frame loss or duplication, with its accuracy verified by the CRC, ensuring the continuity of video streams from cameras to ECU
• A message counter that can be used as a timeout checker to detect any loss of data caused by a stopped or stuck transmission between an image sensor and ECU
• Service Extension Packet (SEP) functionality is implemented over the entire PHY link, which provides packetization and uniform delivery of image data.

While the initial release of CSE provides the functional safety enabling features described above, the next version is already under development. CSE v2.0 will add security features to CSE_i and CSE_c, and will work with the upcoming MIPI Security specification to bring end-to-end security to the MASS framework. Version 2.0 is expected to be completed in mid-2022.

Pixart Unveils Distance Sensors

EETimes: Pixart launches Single-Axis Distance Sensor (SAS) product line based on the optical-geometric technology.  There are two sensors in the new product line: PAC7088J1 is a middle-range version and PAC7088J2 is s short range one.

Photon Counting Camera Use Cases

Hamamatsu publishes a new flyer for its Orca-Quest qCMOS camera with example applications for its photon resolving capability:

Image Sensors and AI Processors

PRNewswire: Xailient announces face recognition AI for Sony's intelligent vision sensor IMX500 with 97.8% accuracy up to 3 meters distance. On the IMX500, Xailient claims to provide the world's most power-efficient Face Recognition AI.

Shivy Yohanandan, Chief Scientist and co-founder of Xailient, says, "The IMX500 is a game-changer for the smart camera market. Delivering the image sensor and AI processing on a single chip has dramatic power efficiency benefits, which is important to extending battery life."

Ray Edwards, Xailient's VP of Market Development, says, "This should sell like hotcakes! Anyone building a smart camera system would be nuts not to consider the IMX500. This is the new standard."

SynSense and Prophesee announced a partnership. The two companies leverage their respective expertise in sensing and processing to develop ultra-low-power solutions for implementing intelligence on the edge for event-based vision applications.

The partnership combines in one single chip SynSense’s low-power vision SNN processor DYNAP-CNN with Prophesee’s event-based Metavision sensors, and is focused on developing a line of cost-efficient modules that can be manufactured at high volume.

“As IoT applications in smart homes, offices, industries and cities proliferate, there is an increasing need for more intelligence on the edge. Our experience in implementing neuromorphic enabled vision sensing with smart pixel methods complements SynSense’s expertise in neuromorphic processing to make implementing high performance vision more practical. Together we can drive the development of highly efficient, lower cost products that provide more visibility, safety and productivity in a wide range of use cases.” said Luca Verre, CEO and co-founder of Prophesee.

Image Sensors at ISSCC 2022

 ISSCC publishes its 2022 Agenda. There are 13 image sensor-related papers:

1. Charge-Domain Signal Compression in Ultra-High-Speed CMOS Image Sensors
   Keiichiro Kagawa,
   Shizuoka University, Hamamatsu, Japan
2. A 0.37W 143dB-Dynamic-Range 1Mpixel Backside-Illuminated Charge-Focusing SPAD Image Sensor with Pixel-Wise Exposure Control and Adaptive Clocked Recharging
   Y. Ota, K. Morimoto, T. Sasago, M. Shinohara, Y. Kuroda, W. Endo, Y. Maehashi, S. Maekawa, H. Tsuchiya, A. Abdelghafar, S. Hikosaka, M. Motoyama, K. Tojima, K. Uehira, J. Iwata, F. Inui, Y. Matsuno, K. Sakurai, T. Ichikawa,
   Canon, Kanagawa, Japan
3. A 64×64-Pixel Flash LiDAR SPAD Imager with Distributed Pixel-to-Pixel Correlation for Background Rejection, Tunable Automatic Pixel Sensitivity and First-Last Event Detection Strategies for Space Applications
   E. Manuzzato, A. Tontini, A. Seljak, M. Perenzoni
   Fondazione Bruno Kessler, Trento, Italy; Jozef Stefan Institute, Ljubljana, Slovenia
4. An 80×60 Flash LiDAR Sensor with In-Pixel Histogramming TDC Based on Quaternary Search and Time-Gated Δ-Intensity Phase Detection for 45m Detectable Range and Background Light Cancellation
   S. Park, B. Kim, J. Cho, J-H. Chun, J. Choi, S-J. Kim
   Ulsan National Institute of Science and Technology, Ulsan, Korea; SolidVue, Suwon, Korea, Sungkyunkwan University, Suwon, Korea
5. A 38μm Range Precision Time-of-Flight CMOS Range Line Imager with Gating Driver Jitter Reduction Using Charge-Injection Pseudo Photocurrent Reference
   K. Yasutomi, T. Furuhashi, K. Sagawa, T. Takasawa, K. Kagawa, S. Kawahito
   Shizuoka University, Hamamatsu, Japan
6. A 1/1.57-inch 50Mpixel CMOS Image Sensor with 1.0μm All-Directional Dual Pixel by 0.5μm-Pitch Full-Depth Deep-Trench Isolation Technology
   T. Jung, M. Fujita, J. Cho, K. Lee, D. Seol, S. An, C. Lee, Y. Jeong, M. Jung, S. Park, S. Baek, S. Jung, S. Lee, J. Yun, E. S. Shim, H. Han, E. Park, H. Sul, S. Kang, K. Lee, J. Ahn, D. Chang
   Samsung Electronics, Hwasung, Korea
7. A 4.9Mpixel Programmable-Resolution Multi-Purpose CMOS Image Sensor for Computer Vision
   H. Murakami, E. Bohannon, J. Childs, G. Gui, E. Moule, K. Hanzawa, T. Koda, C. Takano, T. Shimizu, Y. Takizawa, A. Basavalingappa, R. Childs, C. Cziesler, R. Jarnot, K. Nishimura, S. Rogerson, Y. Nitta,
   Sony
8. A Fully Digital Time-Mode CMOS Image Sensor with 22.9pJ/frame∙pixel and 92dB Dynamic Range
   S. Kim, T. Kim, K. Seo, G. Han,
   Yonsei University, Seoul, Korea
9. A 64Mpixel CMOS Image Sensor with 0.56μm Unit Pixels Separated by Front Deep-Trench Isolation
   S. Park, C. Lee, S. Park, H. Park, T. Lee, D. Park, M. Heo, I. Park, H. Yeo, Y. Lee, J. Lee, B. Lee, D-C. Lee, J. Kim, B. Kim, J. Pyo, S. Quan, S. You, I. Ro, S. Choi, S-I. Kim, I-S. Joe, J. Park, C-H. Koo, J-H. Kim, C. K. Chang, T. Kim, J. Kim, J. Lee, H. Kim, C-R. Moon, H-S. Kim,
   Samsung Electronics, Hwaseong, Korea
10. A 200 x 256 Image Sensor Heterogeneously Integrating a 2D Nanomaterial-Based Photo-FET Array and CMOS Time-to-Digital Converters
    H. Hinton, H. Jang, W. Wu, M-H. Lee, M. Seol, H-J. Shin, S. Park, D. Ham
    Harvard University, Cambridge, MA; Samsung Advanced Institute of Technology, Suwon, Korea
11. A 0.8V Intelligent Vision Sensor with Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode Processing-in-Sensor Technique for Image Classification
    T-H. Hsu, G-C. Chen, Y-R. Chen, C-C. Lo, R-S. Liu, M-F. Chang, K-T. Tang, C-C. Hsieh
    National Tsing Hua University, Hsinchu, Taiwan
12. Augmented Reality – The Next Frontier of Image Sensors and Compute Systems
    C. Liu, S. Chen, T-H. Tsai, B. De Salvo, J. Gomez
    Meta Reality Labs, Redmond, WA
13. Concepts, Architectures and Circuits for Sub-THz Sensing and Imaging
    A. Stelzer,
    Linz University, Linz, Austria

EDoF-ToF Paper

Rice University publishes an OSA Optica paper "EDoF-ToF: extended depth of field time-of-flight imaging" by Jasper Tan, Vivek Boominathan, Richard Baraniuk, and Ashok Veeraraghavan.

"Conventional continuous-wave amplitude-modulated time-of-flight (CWAM ToF) cameras suffer from a fundamental trade-off between light throughput and depth of field (DoF): a larger lens aperture allows more light collection but suffers from significantly lower DoF. However, both high light throughput, which increases signal-to-noise ratio, and a wide DoF, which enlarges the system’s applicable depth range, are valuable for CWAM ToF applications. In this work, we propose EDoF-ToF, an algorithmic method to extend the DoF of large-aperture CWAM ToF cameras by using a neural network to deblur objects outside of the lens’s narrow focal region and thus produce an all-in-focus measurement. A key component of our work is the proposed large-aperture ToF training data simulator, which models the depth-dependent blurs and partial occlusions caused by such apertures. Contrary to conventional image deblurring where the blur model is typically linear, ToF depth maps are nonlinear functions of scene intensities, resulting in a nonlinear blur model that we also derive for our simulator. Unlike extended DoF for conventional photography where depth information needs to be encoded (or made depth-invariant) using additional hardware (phase masks, focal sweeping, etc.), ToF sensor measurements naturally encode depth information, allowing a completely software solution to extended DoF. We experimentally demonstrate EDoF-ToF increasing the DoF of a conventional ToF system by 3.6 ×, effectively achieving the DoF of a smaller lens aperture that allows 22.1 × less light. Ultimately, EDoF-ToF enables CWAM ToF cameras to enjoy the benefits of both high light throughput and a wide DoF."

Sony - Qualcomm Joint Lab to Work on Image Sensor and Processing Optimizations

During the recent Qualcomm's mobile processor announcements, there was a part on establishing a joint lab with Sony in San Diego working on image sensor and processor co-optimization: