SG-FET Based Spiking Neuron With Ultra-Low Energy Consumption for ECG Signal Classification

Abstract

This paper presents an energy-efficient single-transistor leaky integrate-and-fire neuron, based on Suspended Gate-FET (SG-FET), for signal classification and neuromorphic computing applications. By leveraging the SG-FET model, extensive simulations were conducted to demonstrate the device's remarkable neuronal ability. The device faithfully emulated the intricate behaviour of biological neurons, without the need for external circuitry. One of the standout achievements lies in the device's astonishingly low energy consumption of 94.5 aJ per spike. Therefore, it outperforms the previously proposed one-transistor (1-T) neurons, which makes it a potential candidate for energy-efficient neuromorphic computing. To verify the practical viability of the device, an emulation was seamlessly integrated into a spiking neural network framework, allowing for real-time signal classification. In this specific case, the device excelled in the classification of electrocardiogram (ECG) signals, achieving an impressive accuracy rate of 85.6%. This outcome highlights the device's efficacy in handling real-world signal processing tasks with remarkable precision and efficiency.