Highly Linear and Symmetric Analog Neuromorphic Synapse Based on Metal Oxide Semiconductor Transistors with Self‐Assembled Monolayer for High‐Precision Neural Network Computation

Abstract

Abstract This work presents an analog neuromorphic synapse device consisting of two oxide semiconductor transistors for high‐precision neural networks. One of the two transistors controls the synaptic weight by charging or discharging the storage node, which leads to a conductance change in the other transistor. The programmed weight maintains for more than 300 s as electrons in the storage node are well preserved due to the extremely low off current of the oxide transistor. Ideal synaptic behaviors are achieved by utilizing superior properties of oxide transistors such as a high on/off ratio, low off current, and large‐area uniformity. To further improve the synaptic performance, self‐assembled monolayer treatment is applied for reducing the transistor conductance. The reduction of on current reduces the power consumption, and the reduced off current improves the retention characteristics. There is no noticeable decrease in simulated neural network accuracy even when the measured device‐to‐device variation is intentionally increased by 200%, indicating the possibility of large‐array operation with the synapse device.

Keywords

• artificial intelligence
• computing‐in‐memory
• indium gallium zinc oxide transistor
• neuromorphic device
• self‐assembled monolayer