Attojoule Hexagonal Boron Nitride‐Based Memristor for High‐Performance Neuromorphic Computing

Abstract

AbstractIn next‐generation neuromorphic computing applications, the primary challenge lies in achieving energy‐efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h‐BN)‐based metal‐insulator‐semiconductor (MIS) memristors operating is presented at the attojoule‐level tailored for high‐performance artificial neural networks. The memristors benefit from a wafer‐scale uniform h‐BN resistive switching medium grown directly on a highly doped Si wafer using metal–organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h‐BN‐based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano‐structural analysis, confirming that the h‐BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule‐level energy consumption. Furthermore, the formation of atomic‐scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi‐resistance states, making these memristors well‐suited for next‐generation neuromorphic applications. The h‐BN‐based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.