A Lithium-Ion-Driven Electrolyte-Gated 2D Synaptic Transistor Based on Se_0.3Te_0.7 Nanosheet for Reservoir Computing

The solid-state electrolyte has been explored as a gate dielectric for neuromorphic computing, offering enhanced gate controllability and enabling synaptic behaviors. However, the integration of solid-state electrolytes with 2D materials to develop high-performance reservoir computing (RC) systems remains rarely explored. In this study, an electrolyte-gated synaptic transistor (EGST) integrated with 2D Se_0.3Te_0.7 nanosheet and lithium phosphorus oxynitride (LiPON) solid-state electrolyte is proposed. This device leverages ion-carrier coupling to effectively modulate channel conductance (achieving an on/off current ratio of ≈7 × 10³) and exhibits a range of synaptic plasticity, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC) and paired-pulse facilitation (PPF), which are driven by the intrinsic nonlinearity of ionic dynamics. Building on these capabilities, a 2D-EGST-based RC system is simulated and demonstrate its computational capability through a handwritten digit classification task using the Modified National Institute of Standards and Technology (MNIST) dataset. The system achieves an identification accuracy exceeding 90%, outperforming the previously reported performance of other electrolyte-gated transistor (EGT)-based RC systems. It is believed that the 2D-EGST offers new insights into the interplay between electronics and ion dynamics in 2D materials combined with solid-state electrolytes, thereby paving the way for future applications of 2D-EGST in neuromorphic computing.
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