神经形态工程学
晶体管
二极管
人工神经网络
电气工程
计算机科学
算法
拓扑(电路)
光电子学
材料科学
物理
电压
工程类
人工智能
作者
Taejin Jang,Bosung Jeon,S. J. Song,Woo Young Choi
标识
DOI:10.1109/led.2023.3252579
摘要
A poly-Si overpass channel synaptic (OCS) transistor is proposed for the extremely-low-power operation and low RC delay of neuromorphic systems. The OCS transistor has two major structural advantages. First, the on- current ( ${I}_{\text {on}}{)}$ can be reduced to sub 100 nA with high on/off ratio ( $10^{{5}}{)}$ because the channel wraps around the fin-shaped bottom gate. Second, the weights of the OCS transistors are finely divided by increasing the volume of the charge storage layer. The inference and weight update operations of a NOR-type OCS array were experimentally demonstrated. It was confirmed that the fabricated diode-connected (D-C) OCS array is suitable for vector-matrix multiplication (VMM) operation with its weighted-sum error smaller than 0.79% in inference. Each synaptic weight in the D-C OCS array was adjusted into sub-nA resolution by Fowler-Nordheim (FN) tunneling with asymmetric gates. Finally, the classification accuracy of the fashion MNIST dataset is 91.29% even after one year with four-bit quantization of spiking neural network (SNN).
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