Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

Abstract

In memristors, the implementation of the Bienenstock–Cooper–Munro (BCM) learning rule plays a significant role in the modulation balance of artificial synapses and the reduction of energy consumption owing to their sliding frequency threshold. At present, the BCM learning rule is mostly achieved by adjusting gating voltage or channel current in field effect transistors. However, owing to the lack of the tunable degrees of freedom, the progress of two-terminal memristors is limited to simulating the BCM learning rule. In this study, by adjusting the series resistance, three types of BCM-like learning rules are found in a two-terminal BaTiO₃ memristor. Specifically, the abnormal BCM learning rule with high-frequency depression and low-frequency potentiation is obtained for a small series resistance, the monotonous BCM learning rule with high-frequency potentiation and low-frequency depression is achieved for a large series resistance, and the type of BCM learning rule with the enhanced depression effect is obtained for a moderate series resistance. These three BCM learning rules are related to the non-monotonous conductance modulation caused by the migration of ionized oxygen vacancies and are proved by X-ray photoelectron spectroscopy. Moreover, spike rate-dependent plasticity (SRDP) and history-dependent plasticity are achieved. This study offers promising prospects for neuromorphic computing.