Energy-efficient, Reconfigurable Optoelectronic Artificial Synapses Based on MoWS$_2$ Alloy for Pattern Recognition and Color Image Filtering Applications

Abstract

Two-dimensional transition-metal dichalcogenide alloys are potential candidates for advanced optoelectronic and neuromorphic applications due to their strong light-matter interactions and controllable defect properties. However, large-area growth of such alloys remains challenging, while the correlation between their physical and neuromorphic properties remains largely unclear. In this work, we present an innovative microcavity chemical vapor deposition (CVD) reactor pathway to grow uniform, and large-area MoWS$_2$ mono- and few-layer alloy films for demonstrating optoelectronic synaptic functionalities. Driven by growth-induced intrinsic sulfur vacancies, as confirmed by XPS, KPFM, and STEM measurements, our optoelectronic synaptic device (OSD) successfully emulates essential biological synaptic features, such as excitatory postsynaptic currents (EPSC), paired-pulse facilitation (PPF~170%), and stimulus-dependent short- and long-term plasticities (STP & LTP). With picojoule-order energy consumption per synaptic event and nanoampere-order dark current, the device enables low-power neuromorphic learning, including emulation of Pavlovian associative learning. Furthermore, the experimentally measured conductance weight-update characteristics enabled an artificial neural network (ANN) simulation to achieve 92.43% recognition accuracy on the MNIST handwritten digit dataset. Finally, we demonstrate advanced neuromorphic visual processing by executing color image filtering based on the device's wavelength-selective photoresponse characteristics. This simple, yet multifunctional device architecture provides a promising path toward energy-efficient, spectral-selective neuromorphic vision applications.