Electrically controlled tuning the nonlinearity and asymmetry of synaptic behaviors in a magnetoelectrically coupled memristor for high-performance neuromorphic computing

High-performance artificial synaptic devices capable of emulating biological synaptic functions are crucial for developing energy-efficient neuromorphic computing systems. Memristors, whose conductance can be modulated to mimic synaptic plasticity, offer a promising platform for such applications. In this work, we present an electric field-controlled memristor based on an FeGaB/PMN-PT multiferroic heterostructure, in which synaptic weights are continuously tuned by applied voltage pulses. The device was fabricated via magnetron sputtering and exhibits nonvolatile, multi-level resistance switching under pulsed electric field modulation, achieving 83 distinct and continuously adjustable resistance states. Key synaptic functionalities, including long-term potentiation/depression, paired-pulse facilitation, and spike-timing-dependent plasticity, are successfully demonstrated. When integrated into a recurrent neural network, the system achieves a recognition accuracy of 92.4% in a benchmark task. By quantifying the nonlinearity (NL) and asymmetry of weight updates and directly linking them to accuracy performance, we further reveal that device-level NL critically governs system-level computational accuracy. This insight provides a clear optimization pathway for future artificial spintronic synapses, underscoring their potential for high-speed, nonvolatile, and adaptive neuromorphic hardware.