Superior Performance of Printed Optoelectronic Synapses Based on Defect-Controlled Monolayer MoS2 with Ultralow Power Consumption for Neuromorphic Computing

Abstract

Optoelectronic synapses (OES), which integrate photodetection and synaptic functions in a single platform, offer a promising approach to mimic the visual processing capabilities of the human brain. Two-dimensional (2D) materials are attractive for OES devices due to their excellent energy efficiency and high photoelectric conversion capability. However, most 2D material-based OES devices rely on conventional lithography and post-growth defect engineering, both of which require expensive cleanroom facilities and complex processing steps. Here, we present a hybrid fabrication strategy that integrates cost-effective printed technology, as an alternative to conventional lithography, with in-situ defect engineering during CVD growth of monolayer MoS2. The latter intrinsically tunes the defect density and eliminates the need for any post-growth treatments. This approach enables precise control over defect density while ensuring large-area uniformity and fabrication scalability. The resulting OES devices exhibit excellent photoresponsivity (10.3 A/W) and stable synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, short-term memory, long-term memory, and spike-timing-dependent plasticity. Remarkably, the device mimics human learning and forgetting with ultralow energy consumption of 1.2 fJ per synaptic event, outperforming the energy efficiency of biological synapses (~10 fJ). Furthermore, an artificial neural network trained using device-derived parameters achieves a recognition accuracy of 87.1% on the MNIST handwritten digit dataset. Density functional theory calculations elucidate the crucial role of in-situ engineered sulfur vacancies in modulating carrier dynamics and defect-assisted charge trapping, providing a fundamental understanding of the light-induced synaptic behavior in the device.