A CMOS Compatible Bulk FinFET-Based Ultra Low Energy Leaky Integrate and Fire Neuron for Spiking Neural Networks

Abstract

The fundamental building block of an artificial spiking neural network (SNN) is an element which can effectively mimic a biological neuron. There are several electronic and spintronic devices which have been demonstrated as a neuron. But the main concern here is the energy consumption and large area of those artificial neurons. In this letter, we propose and demonstrate a highly scalable and CMOS compatible bulk FinFET with an ${n}^{+}$ buried layer for ultra low energy artificial neuron using well calibrated TCAD simulations. The proposed device shows the signature spiking frequency versus input voltage curve of a biological neuron. The energy per spike of the integrate block of the proposed leaky integrate and fire (LIF) neuron is ${6.3}~\textit {fJ}$ /spike which is the minimum reported till date.