A Capacitor-Based Synaptic Device with IGZO Access Transistors for Neuromorphic Computing

Abstract

Analog in-memory computing synaptic devices have been widely studied for efficient implementation of deep learning. As a candidate for a synaptic device, Si-CMOS and capacitor-based synaptic devices have been proposed. However, due to Si-CMOS leakage currents, it is difficult to achieve sufficient retention time. In our research, we verified IGZO TFT with low leakage current and capacitor-based synapses can show linear and symmetric weight update characteristics as well as excellent device variation characteristics. We also verified that IGZO TFT has a leakage current per channel width of 1μm of ~10-17A, which is much lower than the Si-CMOS, resulting in higher accuracy in deep neural network training.