Spiking Neural Network Integrated with Impact Ionization Field-Effect Transistor Neuron and a Ferroelectric Field-Effect Transistor Synapse

초록

The integration of artificial spiking neurons based on steep-switching logic devices and artificial synapses with neuromorphic functions enables an energy-efficient computer architecture that mimics the human brain well, known as a spiking neural network (SNN). 2D materials with impact ionization or ferroelectric characteristics have the potential for use in such devices. However, research on 2D spiking neurons remains limited and investigations of 2D artificial synapses far more common. An innovative 2D spiking neuron is implemented using a WSe2 impact ionization transistor (I2FET), while a spiking neural network is formed by combining it with a 2D ferroelectric synaptic device (FeFET). The suggested 2D spiking neuron demonstrates precise spiking behavior that closely resembles that of actual neurons. In addition, it achieves a low energy consumption of 2 pJ/spike. The better impact ionization properties of WSe2 are responsible for this efficiency. Furthermore, an all-2D SNN consisting of 2D I2FET neurons and 2D FeFET synapses is constructed, which achieves high accuracy of 87.5% in a face classification task by unsupervised learning. The integration of a 2D SNN with 2D steep-switching spiking neuronal devices and 2D synaptic devices shows great potential for the development of neuromorphic systems with improved energy efficiency and computational capabilities. 2D spiking neural network consisting of 2D ferroelectric FET synapses and 2D impact ionization FET spiking neurons are demonstrated. This study marks a major leap in developing fast-switching, energy-efficient ultra-low-power devices vital for future artificial neural networks, addressing key challenges in energy management for spiking neuron devices. image

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