Van der Waals Antiferroelectric CuCrP₂S₆-Based Artificial Synapse for High-Precision Neuromorphic Computation

2D van der Waals heterostructure-based artificial synapses have emerged as a compelling platform for next-generation neuromorphic systems, owing to their tunable electrical conductivity and layer-engineered functionality through controlled stacking of 2D materials. In this work, an engineered SnS₂/h-BN/CuCrP₂S₆ van der Waals antiferroelectric field-effect transistor (AFe-FET) is presented that implements synaptic weight modulation through the synergistic interplay of charge trapping dynamics and electric-field-controlled ferroelectric polarization switching. The AFe-FET architecture successfully emulates essential neuroplasticity features, including paired-pulse facilitation, short-term plasticity, and long-term plasticity. The device exhibits exceptional long-term potentiation (LTP) and long-term depression (LTD), with an ultralow nonlinearity coefficient of 1.1 for both LTP and LTD operations, high symmetricity (30), and broad dynamic range (G_max/G_min = 10). The AFe-FET-based neuromorphic system demonstrates an outstanding computational efficacy, i.e. a classification accuracy of 97.7% on the MNIST benchmark. Furthermore, implementing reservoir computing architectures enables cognitive process emulation, attaining 94.7% task recognition accuracy in brain-inspired decision-making simulations. This investigation establishes new design paradigms for high-fidelity synaptic devices, providing a strategy for energy-efficient neuromorphic computing systems with biological plausibility.