Photoelectric Synapse Based on InGaZnO Nanofibers for High Precision Neuromorphic Computing

被引：24
作者：
Zhu, Yixin [1 ,2 ]
Mao, Huiwu [1 ,2 ]
Zhu, Ying [1 ,2 ]
Zhu, Li [1 ,2 ]
Chen, Chunsheng [1 ,2 ]
Wang, Xiangjing [1 ,2 ]
Ke, Shuo [1 ,2 ]
Fu, Chuanyu [1 ,2 ]
Wan, Changjin [1 ,2 ]
Wan, Qing [1 ,2 ]
机构：
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210093, Peoples R China
[2] Nanjing Univ, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China
来源：
IEEE ELECTRON DEVICE LETTERS | 2022年 / 43卷 / 04期
基金：
中国国家自然科学基金;
关键词：
IGZO nanofiber; LTP; multilevel characteristics; neural network;
D O I：
10.1109/LED.2022.3149900
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We propose an indium gallium zinc oxide (IGZO) nanofiber based photoelectric synapse. Long-term potentiation and depression emulations are realized by exploiting optical and electrical stimulus as the excitatory and inhibitory inputs, respectively. Significantly, IGZO nanofiber-based photoelectric synapse exhibit multilevel characteristics (up to 10 bits) with low updating energy (similar to 1.0 fJ). Furthermore, an artificial neural network (ANN) based on IGZO nanofiber photoelectric synapse is built and evaluated through simulations. The performance indicates more than 93% accuracy in recognizing the standard MNIST handwritten digits, showing the great potential for high-precision neuromorphic computing by the IGZO nanofiber photoelectric synapse.
引用
收藏
页码：651 / 654
页数：4
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