Flexible Active Crossbar Arrays Using Amorphous Oxide Semiconductor Technology toward Artificial Neural Networks Hardware

Abstract

Memristor crossbar arrays can compose the efficient hardware for artificial intelligent applications. However, the requirements for a linear and symmetric synaptic weight update and low cycle-to-cycle (C2C) and device-to-device variability as well as the sneak-path current issue have been delaying its further development. This study reports on a thin-film amorphous oxide-based 4×4 1-transistor 1-memristor (1T1M) crossbar. The a-IGZO crossbar is built on a flexible polyimide substrate, enabling IoT and wearable applications. In the novel framework, the thin-film transistor and memristor are fabricated at the same level, with the same processing steps and sharing the same materials for all layers. The 1T1M cells show linear and symmetrical plasticity characteristic with low C2C variability. The memristor performs like an analog dot product engine and vector–matrix multiplications in the 4×4 crossbars is demonstrated experimentally, in which the sneak-path current issue is successfully suppressed, resulting in a proof-of-concept for a cost-effective, flexible artificial neural networks hardware.