Implementation of edge computing using HfAlO_x-based memristor

In this era of extensive big-data, efficient processing of this data has become a critical aspect of computing. Resistive random access memory devices have exemplary non-volatile memory properties and have received significant research attention as next-generation devices for emulating neuromorphic computing. In this study, we present a synaptic memristor incorporating a thin Al-doped hafnium oxide (HfAlO) film layer sandwiched between two electrodes, which is designed to emulate various functions of the biological brain. Investigations into endurance and retention revealed the non-volatile nature of the memristor, with consistent resistive switching observed during continuous operation and a low operating voltage requirement of less than 1.7 V. Additionally, by applying different conditional stimuli, we successfully implemented Pavlovian associative learning. Furthermore, using sequential pulses with varying sequences resulted in the creation of 4-bit edge computing, demonstrating capabilities for energy- and time-efficient data processing. The synaptic and computing properties exhibited by the Mo/HfAlO/TiN device highlighted its valuable features, positioning the device as a promising candidate for energy-efficient neuromorphic computing hardware in the field of artificial intelligence.