Self‐Powered, Flexible, and Multi‐Sensory Artificial Synaptic Devices for Intelligent Object Recognition

Abstract The burgeoning fields of artificial intelligence and bio‐inspired robotics necessitate the development of advanced artificial synapses that transcend the limitations of conventional unitary and rigid systems. This study presents a self‐powered, flexible, multi‐sensory artificial synaptic device based on a heterojunction structure of reduced graphene oxide (rGO) and sulfur vacancy‐rich ZnIn 2 S 4 (Sv‐ZIS), which ingeniously integrates optical perception and tactile sensing capabilities within a single platform. By engineering interfacial voids and defect states, the dynamic photo‐carrier trapping and releasing enable the emulation of fundamental synaptic behaviors under optical stimulation, with relaxation time reaching 200 s. Concurrently, the device exhibits a quantifiable mechanical response to strain and external force, originating from the contact potential difference between the Sv‐ZIS and rGO layers. Remarkably, all functionalities are achieved at zero bias, ensuring minimal energy consumption. Leveraging the multi‐sensory ability, an artificial neural network is constructed for intelligent object recognition based on their optical transmittance and weight characteristics, achieving a high accuracy of 97.67%. This work establishes a novel paradigm for the development of energy‐efficient, flexible, and multi‐modal neuromorphic devices, paving the way for advanced applications in soft robotics, wearable electronics, and human‐machine interfaces.