| Literature DB >> 33741964 |
Qian-Bing Zhu1,2, Bo Li1,2, Dan-Dan Yang3, Chi Liu1, Shun Feng1,4, Mao-Lin Chen1, Yun Sun1, Ya-Nan Tian5, Xin Su6, Xiao-Mu Wang6, Song Qiu7, Qing-Wen Li8, Xiao-Ming Li9, Hai-Bo Zeng3, Hui-Ming Cheng10,11,12, Dong-Ming Sun13,14.
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.Entities:
Year: 2021 PMID: 33741964 PMCID: PMC7979753 DOI: 10.1038/s41467-021-22047-w
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 14.919