An Artificial Neuron with a Leaky Fin-Shaped Field-Effect Transistor for a Highly Scalable Capacitive Neural Network

Abstract

A capacitive neural network with a capacitive crossbar array that can replace a traditional resistive crossbar array can drastically lower static power consumption during reading operations because a capacitor consumes only dynamic power. Herein, a leaky fin-shaped field-effect transistor (L-FinFET) neuron is fabricated and then applied for use in a highly scalable capacitive neural network with leaky integrate-and-fire (LIF) operations that are attributed to a leaky charge trap layer in a gate stack. An additional circuit such as a voltage-to-current converter (V-I converter) is no longer required when the L-FinFET is applied to the capacitive neural network, as the L-FinFET can directly accept a voltage signal from capacitive synapses. Furthermore, a reset circuit is not necessary given the ability to spontaneously restore to the initial state owing to the leaky charge trap layer. A highly scalable capacitive neural network is realizable due to the size-reduction ability of the L-FinFET and the simplified circuit. Finally, an entirely hardware-based capacitive neural network with the L-FinFET is demonstrated for the recognition of a simple pattern.