An Analog Neuromorphic On-Chip Training System With IGZO TFT-Based 6T1C

Abstract

This article proposes an analog synapse-based neu- romorphic on-chip training system that uses emerging indium gallium zinc oxide (IGZO) thin film transistor (TFT) synapse cells to store multi-bit states for deep neural networks (DNNs). IGZO TFT demonstrates extremely low leakage currents, preserving the charge stored in capacitors during prolonged training periods. The 6 transistor 1 capacitor (6T1C) structure, characterized by its symmetrical design and current sources configuration, achieves