Optoelectronic Neural Networks: An Atomically Thin Optoelectronic Machine Vision Processor (Adv. Mater. 36/2020)

In article number 2002431, Seongjun Park, Donhee Ham, and co‐workers describe pioneering work on an optoelectronic neural network built out of 2D MoS2. They highlight its use in a machine vision processor, which mimics two core functions of human vision to recognize optical images of hand‐written nu...

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Published in:Advanced materials (Weinheim) 2020-09, Vol.32 (36), p.n/a
Main Authors: Jang, Houk, Liu, Chengye, Hinton, Henry, Lee, Min‐Hyun, Kim, Haeryong, Seol, Minsu, Shin, Hyeon‐Jin, Park, Seongjun, Ham, Donhee
Format: Article
Language:English
Subjects:
2D materials
crossbar arrays
integrated circuits
neural networks
transition metal dichalcogenides
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container_issue 36
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container_title Advanced materials (Weinheim)
container_volume 32
creator Jang, Houk
Liu, Chengye
Hinton, Henry
Lee, Min‐Hyun
Kim, Haeryong
Seol, Minsu
Shin, Hyeon‐Jin
Park, Seongjun
Ham, Donhee
description In article number 2002431, Seongjun Park, Donhee Ham, and co‐workers describe pioneering work on an optoelectronic neural network built out of 2D MoS2. They highlight its use in a machine vision processor, which mimics two core functions of human vision to recognize optical images of hand‐written numbers.
doi_str_mv 10.1002/adma.202070275
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subjects 2D materials
crossbar arrays
integrated circuits
neural networks
transition metal dichalcogenides
title Optoelectronic Neural Networks: An Atomically Thin Optoelectronic Machine Vision Processor (Adv. Mater. 36/2020)
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