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An Atomically Thin Optoelectronic Machine Vision Processor
Author(s) -
Jang Houk,
Liu Chengye,
Hinton Henry,
Lee MinHyun,
Kim Haeryong,
Seol Minsu,
Shin HyeonJin,
Park Seongjun,
Ham Donhee
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.202002431
Subject(s) - photodetector , materials science , optoelectronics , electronic circuit , transistor , crossbar switch , neuromorphic engineering , electronics , image sensor , computer science , electrical engineering , artificial intelligence , artificial neural network , telecommunications , voltage , engineering
2D semiconductors, especially transition metal dichalcogenide (TMD) monolayers, are extensively studied for electronic and optoelectronic applications. Beyond intensive studies on single transistors and photodetectors, the recent advent of large‐area synthesis of these atomically thin layers has paved the way for 2D integrated circuits, such as digital logic circuits and image sensors, achieving an integration level of ≈100 devices thus far. Here, a decisive advance in 2D integrated circuits is reported, where the device integration scale is increased by tenfold and the functional complexity of 2D electronics is propelled to an unprecedented level. Concretely, an analog optoelectronic processor inspired by biological vision is developed, where 32 × 32 = 1024 MoS 2 photosensitive field‐effect transistors manifesting persistent photoconductivity (PPC) effects are arranged in a crossbar array. This optoelectronic processor with PPC memory mimics two core functions of human vision: it captures and stores an optical image into electrical data, like the eye and optic nerve chain, and then recognizes this electrical form of the captured image, like the brain, by executing analog in‐memory neural net computing. In the highlight demonstration, the MoS 2 FET crossbar array optically images 1000 handwritten digits and electrically recognizes these imaged data with 94% accuracy.