scholarly journals In-sensor optoelectronic computing using electrostatically doped silicon

2022 ◽  
Author(s):  
Houk Jang ◽  
Henry Hinton ◽  
Woo-Bin Jung ◽  
Min-Hyun Lee ◽  
Changhyun Kim ◽  
...  
Keyword(s):  
Data Transfer ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Oxide Semiconductor ◽  
Scale Production ◽  
Silicon Photodiode ◽  
Seamless Integration ◽  
Cmos Image Sensors ◽  
Doped Silicon

Abstract Complementary metal-oxide-semiconductor (CMOS) image sensors are a visual outpost of many machines that interact with the world. While they presently separate image capture in front-end silicon photodiode arrays from image processing in digital back-ends, efforts to process images within the photodiode array itself are rapidly emerging, in hopes of minimizing the data transfer between sensing and computing, and the associated overhead in energy and bandwidth. Electrical modulation, or programming, of photocurrents is requisite for such in-sensor computing, which was indeed demonstrated with electrostatically doped, but non-silicon, photodiodes. CMOS image sensors are currently incapable of in-sensor computing, as their chemically doped photodiodes cannot produce electrically tunable photocurrents. Here we report in-sensor computing with an array of electrostatically doped silicon p-i-n photodiodes, which is amenable to seamless integration with the rest of the CMOS image sensor electronics. This silicon-based approach could more rapidly bring in-sensor computing to the real world due to its compatibility with the mainstream CMOS electronics industry. Our wafer-scale production of thousands of silicon photodiodes using standard fabrication emphasizes this compatibility. We then demonstrate in-sensor processing of optical images using a variety of convolutional filters electrically programmed into a 3 × 3 network of these photodiodes.

scholarly journals Super Field-of-View Lensless Camera by Coded Image Sensors

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1329 ◽  
Author(s):  
Tomoya Nakamura ◽  
Keiichiro Kagawa ◽  
Shiho Torashima ◽  
Masahiro Yamaguchi
Keyword(s):  
Imaging System ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Computational Imaging ◽  
Field Of View ◽  
Oxide Semiconductor ◽  
Back Side ◽  
Cmos Image Sensors ◽  
Rich Information

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor (CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realized super-field-of-view imaging without lenses or coding masks and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

scholarly journals Numerical Model of SPAD-Based Direct Time-of-Flight Flash LIDAR CMOS Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5203
Author(s):  
Alessandro Tontini ◽  
Leonardo Gasparini ◽  
Matteo Perenzoni
Keyword(s):  
Single Photon ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Oxide Semiconductor ◽  
Simulation Environment ◽  
Lidar System ◽  
Cmos Image Sensors ◽  
Flash Lidar ◽  
Illumination Source

We present a Montecarlo simulator developed in Matlab® for the analysis of a Single Photon Avalanche Diode (SPAD)-based Complementary Metal-Oxide Semiconductor (CMOS) flash Light Detection and Ranging (LIDAR) system. The simulation environment has been developed to accurately model the components of a flash LIDAR system, such as illumination source, optics, and the architecture of the designated SPAD-based CMOS image sensor. Together with the modeling of the background noise and target topology, all of the fundamental factors that are involved in a typical LIDAR acquisition system have been included in order to predict the achievable system performance and verified with an existing sensor.

scholarly journals Super Field-of-View Lensless Camera by Coded Image Sensors

2019 ◽  
Author(s):  
Tomoya Nakamura ◽  
Keiichiro Kagawa ◽  
Shiho Torashima ◽  
Masahiro Yamaguchi
Keyword(s):  
Imaging System ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Computational Imaging ◽  
Field Of View ◽  
Oxide Semiconductor ◽  
Back Side ◽  
Cmos Image Sensors ◽  
Rich Information

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

scholarly journals Proximity Gettering Design of Hydrocarbon–Molecular–Ion–Implanted Silicon Wafers Using Dark Current Spectroscopy for CMOS Image Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2073 ◽  
Author(s):  
Kazunari Kurita ◽  
Takeshi Kadono ◽  
Satoshi Shigematsu ◽  
Ryo Hirose ◽  
Ryosuke Okuyama ◽  
...  
Keyword(s):  
Dark Current ◽  
Image Sensor ◽  
Image Sensors ◽  
Silicon Wafers ◽  
Device Fabrication ◽  
Oxide Semiconductor ◽  
Device Performance ◽  
Molecular Ion ◽  
Cmos Image Sensors ◽  
Ion Implanted

We developed silicon epitaxial wafers with high gettering capability by using hydrocarbon–molecular–ion implantation. These wafers also have the effect of hydrogen passivation on process-induced defects and a barrier to out-diffusion of oxygen of the Czochralski silicon (CZ) substrate bulk during Complementary metal-oxide-semiconductor (CMOS) device fabrication processes. We evaluated the electrical device performance of CMOS image sensor fabricated on this type of wafer by using dark current spectroscopy. We found fewer white spot defects compared with those of intrinsic gettering (IG) silicon wafers. We believe that these hydrocarbon–molecular–ion–implanted silicon epitaxial wafers will improve the device performance of CMOS image sensors.

Research on Noise Sources in CMOS Image Sensors

2010 ◽  
Vol 159 ◽  
pp. 527-531
Author(s):  
Bao Yuan Han ◽  
Yuan Yuan Shang ◽  
Xiao Xu Zhao ◽  
Hui Liu
Keyword(s):  
Solid State ◽  
Metal Oxide ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensors ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Noise Sources ◽  
Cmos Image Sensors

CMOS (Complementary Metal Oxide Semiconductor) imagers as a solid state array develop rapidly. This article introduces various types of CMOS image sensors’ noises in detail, the cause of each noise, as well as a brief overview of current methods for reducing different noise.

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