Steep-Slope CuInP₂S₆ Ferroionic Threshold Switching Field-Effect Transistor for Implementation of Artificial Spiking...

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 CuInP₂S₆ (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 (>10⁷), 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.