Hafnia-based Double Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing

Abstract

Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as low-power memories and neuromorphic computing. The tunneling electroresistance (TER) is tunable through the polarization state of the HZO film. To circumvent the challenge of fabricating thin ferroelectric HZO layers in the tunneling range of 1-3 nm range, ferroelectric/dielectric double layer sandwiched between two symmetric metal electrodes are used. Due to the decoupling of the ferroelectric polarization storage layer and a dielectric tunneling layer with a higher bandgap, a significant TER ratio between the two polarization states is obtained. By exploiting previously reported switching behaviour and the gradual tunability of the resistance, FTJs can be used as potential candidates for the emulation of synapses for neuromorphic computing in spiking neural networks. The implementation of two major components of a synapse are shown: long term depression/potentiation by varying the amplitude/width/number of voltage pulses applied to the artificial FTJ synapse, and spike-timing-dependent-plasticity curves by applying time-delayed voltages at each electrode. These experimental findings show the potential of spiking neural networks and neuromorphic computing that can be implemented with hafnia-based FTJs.