Delang LinDongxiang LuoFion Sze Yan YeungHoi Sing KwokRongsheng Chen
Neuromorphic systems that enable parallel processing and self-learning are promising solutions to overcome the limitations of Von Neumann architectures. Synaptic devices are fundamental in neuromorphic computing and are being developed for optical stimulation to improve performance. In this letter, highperformance deep-ultraviolet (DUV) synaptic transistors based on the ITMO-ITZO-ITMO channel are proposed. The response speed and current of the fabricated devices were greatly improved by the stacked active layer, achieving post-synaptic currents exceeding 40 nA under weak DUV pulse stimulation (20 ms pulse width and 20 mu W/cm2 2 light intensity). Moreover, these devices showed an ultrahigh paired-pulse facilitation index of 410% with a 20 ms interval. Additionally, the DUV synaptic transistors exhibited various synaptic plasticity, successfully simulated the learning behavior of the human brain, and enabled 4-bit time-series signal recognition. This marks a significant advancement toward efficient neuromorphic systems with enhanced decision-making capabilities.
Seung‐Eon AhnSanghun JeonYoug Woo JeonChangjung KimMyoung‐Jae LeeChang‐Won LeeJongbong ParkIhun SongArokia NathanSungsik LeeU‐In Chung
Qian YangShi-yuan DURong-si LUO
Xinkai SunJae‐Hoon HanZhenyuan XiaoSimin ChenTaewon JinTaehyeon NohSeoungmin ParkJaekyun KimJidong JinYounghyun Kim
Taebin LimJinbaek BaeJiseob LeeJin Jang
Takanori TakahashiYukiharu Uraoka