Reconfigurable Neuron and Synapse Operations in a Steep-Switching Nonvolatile Transistor.

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 CuInPS/h-BN/WSe 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.