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A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems

Qian-Bing Zhu, Bo Li, Dan-Dan Yang, Chi Liu, Shun Feng, Mao-Lin Chen, Yun Sun, Ya-Nan Tian, Xin Su, Xiao-Mu Wang, Song Qiu (), Qing-Wen Li, Xiao-Ming Li (), Hai-Bo Zeng, Hui-Ming Cheng () and Dong-Ming Sun ()
Additional contact information
Qian-Bing Zhu: Chinese Academy of Sciences
Bo Li: Chinese Academy of Sciences
Dan-Dan Yang: Nanjing University of Science and Technology
Chi Liu: Chinese Academy of Sciences
Shun Feng: Chinese Academy of Sciences
Mao-Lin Chen: Chinese Academy of Sciences
Yun Sun: Chinese Academy of Sciences
Ya-Nan Tian: Northeastern University
Xin Su: Nanjing University
Xiao-Mu Wang: Nanjing University
Song Qiu: Chinese Academy of Sciences
Qing-Wen Li: Chinese Academy of Sciences
Xiao-Ming Li: Nanjing University of Science and Technology
Hai-Bo Zeng: Nanjing University of Science and Technology
Hui-Ming Cheng: Chinese Academy of Sciences
Dong-Ming Sun: Chinese Academy of Sciences

Nature Communications, 2021, vol. 12, issue 1, 1-7

Abstract: Abstract The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2.

Date: 2021
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DOI: 10.1038/s41467-021-22047-w

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