Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems

Abstract

A positive-feedback (PF) neuron device capable of threshold tuning and simultaneously processing excitatory ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{+}$ </tex-math></inline-formula> ) and inhibitory ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{-}$ </tex-math></inline-formula> ) signals is experimentally demonstrated to replace conventional neuron circuits, for the first time. Thanks to the PF operation, the PF neuron device with steep switching characteristics can implement integrate-and-fire (IF) function of neurons with low-energy consumption. The structure of the PF neuron device efficiently merges a gated PNPN diode and a single MOSFET. Integrate-and-fire (IF) operation with steep subthreshold swing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</i> < 1 mV/dec) is experimentally implemented by carriers accumulated in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> floating body of the PF neuron device. The carriers accumulated in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> floating body are discharged by an inhibitory signal applied to the merged FET. Moreover, the threshold voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {th}}$ </tex-math></inline-formula> ) of the proposed PF neuron is controlled by using a charge storage layer. The low-energy consuming PF neuron circuit (~0.62 pJ/spike) consists of one PF device and only five MOSFETs for the IF and reset operation. In a high-level system simulation, a deep-spiking neural network (D-SNN) based on PF neurons with four hidden layers (1024 neurons in each layer) shows high-accuracy (98.55%) during a MNIST classification task. The PF neuron device provides a viable solution for high-density and low-energy neuromorphic systems.