Electro-optically co-modulated ZnS synaptic memristor for neuromorphic recognition systems

Memristor-based neuromorphic computing offers a revolutionary strategy to address the limitations of traditional computing architectures. Developing synaptic memristors co-modulated by electrical and optical signals is crucial for realizing neural networks with high-efficiency parallel processing and in-memory computing, yet it remains a significant challenge. Herein, wide-bandgap zinc sulfide (ZnS) is introduced to design Ag/ZnS/FTO optoelectronic synaptic memristors. The devices verify reliable resistive switching (RS) behavior, primarily attributed to being dominated by sulfur vacancies (VS), with a narrow Set/Reset distribution (variation < 0.04/0.03 V), an On/Off ratio of ∼26, and a retention time exceeding 104 s. Under electrical, especially near-infrared light (808 and 980 nm) stimulation, these memristors accurately mimic diverse synaptic plasticity functions, including excitatory post-synaptic current, short-term/long-term memory, long-term potentiation/depression, paired-pulse facilitation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, spike-dependent dynamic plasticity, spike-rate-dependent plasticity, and Ebbinghaus learning–forgetting behaviors. Notably, applying to handwritten digit recognition on the MNIST dataset, the system achieves an 88.25% classification accuracy, demonstrating its potential for practical neuromorphic applications. These findings open an avenue for the development of sulfide-based optoelectronic synaptic devices and advanced neuromorphic computing systems.