TY - JOUR
T1 - Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors
AU - Nie, Yiling
AU - Xie, Pengshan
AU - Chen, Xu
AU - Jin, Chenxing
AU - Liu, Wanrong
AU - Shi, Xiaofang
AU - Xu, Yunchao
AU - Peng, Yongyi
AU - Ho, Johnny C.
AU - Sun, Jia
AU - Yang, Junliang
N1 - Publisher Copyright:
© 2022 Chinese Institute of Electronics.
PY - 2022/11
Y1 - 2022/11
N2 - The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing. Herein, a multifunctional synaptic transistor is constructed by using 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) and indium gallium arsenide (InGaAs) nanowires (NWs) hybrid heterojunction thin film as the active layer. Under illumination, the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine. The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors, including excitatory postsynaptic current, long/short-term memory and Pavlovian learning. Furthermore, a high classification accuracy of 89.72% can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors. Thus, this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.
AB - The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing. Herein, a multifunctional synaptic transistor is constructed by using 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) and indium gallium arsenide (InGaAs) nanowires (NWs) hybrid heterojunction thin film as the active layer. Under illumination, the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine. The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors, including excitatory postsynaptic current, long/short-term memory and Pavlovian learning. Furthermore, a high classification accuracy of 89.72% can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors. Thus, this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.
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U2 - 10.1088/1674-4926/43/11/112201
DO - 10.1088/1674-4926/43/11/112201
M3 - Article
AN - SCOPUS:85143526185
SN - 1674-4926
VL - 43
JO - Journal of Semiconductors
JF - Journal of Semiconductors
IS - 11
M1 - 112201
ER -