Low-Power, Linear, and Uniform Bimodal Resistive Switching in Proton Conducting/Insulating Bilayer-Based Memristor

The emergence of artificial intelligence (AI) has recently necessitated the processing of big data. However, a separation between the memory and processing unit leads to significant time and power waste in conventional computing architecture. Therefore, memristors have been spotlighted due to their ability to store and process information at once with simple structures. However, conductive filament formation due to random ion movement induces stochastic resistive switching and nonlinear conductance modulation. In this study, we demonstrate a proton-electron coupled memristor controlled either by humidity or voltage using a tyrosine-rich peptide/Al2O3 bilayer to mitigate those bottlenecks. Introducing the proton insulating layer into a peptide memristor device significantly enhanced electrical performance in terms of linear weight update, uniform resistive switching, and low power consumption. The interlayered memristor device exhibited computing voltage reduction of 56%, two orders of magnitude increased switching window, and linearity and uniformity improvement by 36% and 85%, respectively. Consequently, the image recognition simulation showed a 20% accuracy improvement. These improvements are elucidated by filament confinement effect from the switching medium shift due to input mode. Therefore, these results not only provide a material-level strategy for high-performance memristors but also demonstrate bimodally controllable memristors for biomimetic electronics.