Reconfigurable Neuron and Synapse Operations in a Steep‐Switching Nonvolatile Transistor

Abstract Neuromorphic systems that emulate the information transmission of biological neural networks face challenges in their integration owing to the disparate features of neuron‐ and synapse‐mimicking devices, leading to complex and inefficient system architectures. Herein, the study proposes a steep‐switching nonvolatile field‐effect transistor leveraging a CuInP ₂ S ₆ /h‐BN/WSe ₂ heterostructure to enable reconfigurable neuron‐ and synapse‐modes by electrostatically modulating the carrier density of the channel to control its Fermi level, thereby facilitating leaky‐integrate‐and‐fire (LiF) neuron operation. In addition, an additional ferroelectric‐gating effect enhances the chemical potential of the channel through interactions between ferroelectric dipoles and channel carriers, allowing LiF operation at a reduced operating bias condition. The synaptic mode is activated by shifting the Fermi level of the channel toward the valence band, where the increased carrier density induces a screening effect that suppresses impact ionization and causes the device to operate predominantly through ferroelectric effects, enabling weight‐modulated synaptic functionality. A device‐to‐system level simulation of the spiking neural network is performed based on a single device neuron‐synapse integrated system, achieving an accuracy of 95.83% for human face recognition via lateral inhibition function of the neuron device. This study presents a promising approach for the development of a cointegrated and highly scalable neuromorphic computing technology.