Ion-Gated MoS₂ Unipolar Synaptic Transistors for Neuromorphic Computing and Physics-Inspired Image Processing

Unipolar synaptic plasticity constitutes a critical requirement for neuromorphic systems interfacing with inherently unipolar sensory data streams, yet conventional synaptic devices remain constrained by bipolar operational modes that limit compatibility and energy efficiency. In this work, we design a few-layer MoS2-based ion-gated synaptic transistor that achieves unipolar synaptic enhancement and suppression under both positive and negative gate biases by dynamically regulating gate voltage pulses and drain-source voltage (VDS). Device physics analysis reveals synaptic plasticity governed by cation migration kinetics and relaxation dynamics in the ionic liquid dielectric, with threshold-dependent behavior emerging from gate and VDS terminal responses. Utilizing this dual-gating mechanism, we implement an optimized reservoir computing framework achieving 98.3% accuracy in digit classification, enhancing baseline performance by 3% through unipolar voltage coding validated through numerical simulations. Moreover, we develop a physics-integrated image processing architecture that directly incorporates device characteristics to execute local contrast enhancement and texture refinement, operating at about 2 orders of magnitude lower power consumption than conventional von Neumann systems. This work establishes 2D van der Waals materials as a versatile platform for codesigning neuromorphic hardware and adaptive algorithms, which advance energy-efficient, bioinspired computing systems compatible with real-world sensory inputs.