Photoelectric Synapse Based on InGaZnO Nanofibers for High Precision Neuromorphic Computing
Yixin Zhu, Huiwu Mao, Ying Zhu, Li Zhu, Chunsheng Chen, Xiangjing Wang, Shuo Ke, Chuanyu Fu, Changjin Wan, Qing Wan
Abstract
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 (~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.