HfO2-Based Memristor as an Artificial Synapse for Neuromorphic Computing with Tri-Layer HfO2/BiFeO3/HfO2 Design

Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal-oxide semiconductor devices in recent times. In this work, tri-layer HfO2/BiFeO3(BFO)/HfO2 memristors are designed by inserting traditional ferroelectric BFO layers measuring approximate to 4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10(4) and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.

Automated summary

Tri-layer HfO2/BiFeO3/BFO/HfO2 memristors, designed with traditional ferroelectric BFO layers and thickness optimization, exhibit excellent resistive switching performance, multi-level storage ability, and successful realization of essential synaptic functions. Conductive filaments composed of Hafnium single crystal are observed to enhance RS behavior, showing promising potential for neuromorphic computing.