Steep‐Slope CuInP ₂ S ₆ Ferroionic Threshold Switching Field‐Effect Transistor for Implementation of Artificial Spiking Neuron

Abstract

Abstract Spiking neural networks (SNNs) have garnered considerable attention as energy‐efficient and biologically inspired computing paradigms. However, despite the growing interest, the development of hardware‐based SNNs has remained limited, primarily because of insufficient research on hardware‐based spiking neuron devices. In this study, a CuInP 2 S 6 (CIPS)‐based threshold switching field‐effect transistor (TS‐FET) is presented, featuring steep switching characteristics, and demonstrate its potential as an energy‐efficient spiking neuron device. The proposed device exhibits outstanding characteristics: ultra‐steep subthreshold swing (SS ≈7.5 mV dec −1 ), high on/off current ratio (>10 7 ), and ultra‐low off current (≈0.3 pA) due to the ferroionic properties of CIPS. The tunable dynamics for Cu + ion migration induce a phase transition, leading to sharp resistance switching and efficient spiking. This device successfully mimics key neuronal dynamics, including leaky integrate‐and‐fire, threshold tuning, and spatiotemporal dynamics, without requiring auxiliary reset circuits. Furthermore, SNN is constructed by integrating CIPS‐based synaptic and neuron devices and evaluate face classification performance using an unsupervised learning approach, achieving a recognition accuracy of 95.83% via the lateral inhibition function of the neuron device. The findings highlight the potential of CIPS TS‐FET as energy‐efficient spiking neuron device applications for next‐generation SNN‐based neuromorphic computing systems.