Artificial synapses based on 2D-layered palladium diselenide heterostructure dynamic memristor for neuromorphic applications

Abstract

The transformation of partially amorphous two-dimensional (2D) material layers represents a promising avenue for enhancing the reliability of heterostructure memristor devices and achieving high-density memory with synaptic characteristics. In this study, we investigate the advantages of aluminum oxide-based redox reactive layers integrated with 2D pentagonal palladium diselenide (PdSe2) layers, resulting in the realization of a reliable multilevel conductance memory controlled by DC and pulse voltages. Through careful analysis, we observed that the formation and rupture of conductive filaments related to oxygen vacancies were significantly controlled at the Al2O3/PdSe2 interface, where a mixed oxide of Al-PdSeOx was formed. This exciting phenomenon was further corroborated by comprehensive chemical analysis of the synapse structure as well as various synaptic behaviors, predominantly modulated by the facile movement of oxygen ions under the influence of external electric fields. Remarkably, our experimental results demonstrated improved endurance with multiple memory states achieved by judiciously altering the RESET voltage and SET current compliance, yielding an impressive memory window exceeding 10. Moreover, we successfully achieve a controlled transition from short-term to long-term memory through the application of identical and nonidentical consecutive pulse voltages. Inspired by biological synapses, our PdSe2-based artificial synapse exhibits biocompatible short-term plasticity, such as spike rate-dependent plasticity, paired-pulse facilitation, and experience-dependent plasticity, effectively mimicking the functionality of its biological counterparts. Notably, this biocompatible synapse also demonstrates great potential for visual memory processing, and multilevel 4-bit reservoir computing by demonstrating 5 × 4 digit image recognition thereby offering a distinct advantage for artificial neuromorphic applications. © 2023 Elsevier Ltd