A Novel Structured Single Device Neuron for Low Standby Power and Compact System Application

We propose a single device neuron that utilizes a ferroelectric layer, a split gate, and a truncated floating gate structure. The proposed neuron device, named Single-Device Leaky-FeFET (SD L-FeFET), successfully emulates the neuronal dynamics for both excitatory and inhibitory connections while reducing the standby power by eliminating tail current. A spiking neural network (SNN) using the newly developed SD L-FeFET neuron shows MNIST handwriting digit pattern recognition of 92.5% and face recognition of 91.7%, which is comparable performance to the state-of-art SNN simulation results using conventional complex cell designs.

Automated summary

We propose a single device neuron called SD L-FeFET that simulates neuronal dynamics for both excitatory and inhibitory connections while reducing standby power. The spiking neural network (SNN) using SD L-FeFET achieves a pattern recognition accuracy of 92.5% for MNIST handwriting digits and 91.7% for face recognition, comparable to state-of-the-art SNN simulations with conventional complex cell designs.