Ultra-Low Power Neuromorphic Obstacle Detection Using a Two-Dimensional Materials-Based Subthreshold Transistor

TL;DR

This paper introduces an ultra-low power neuromorphic obstacle detection neuron using 2D material-based transistors, enabling efficient, reconfigurable collision detection with complex neuronal behaviors.

Contribution

It presents a novel 2D subthreshold transistor-based neuron circuit capable of biomimetic spiking and obstacle detection at unprecedented low energy levels.

Findings

Energy efficiency of ~3.5 pJ/spike

Demonstrates complex neuronal behaviors like spike-frequency adaptation

Detects obstacles with energy cost <100 pJ

Abstract

Accurate, timely and selective detection of moving obstacles is crucial for reliable collision avoidance in autonomous robots. The area- and energy-inefficiency of CMOS-based spiking neurons for obstacle detection can be addressed through the reconfigurable, tunable and low-power operation capabilities of emerging two-dimensional (2D) materials-based devices. We present an ultra-low power spiking neuron built using an electrostatically tuned dual-gate transistor with an ultra-thin and generic 2D material channel. The 2D subthreshold transistor (2D-ST) is carefully designed to operate under low-current subthreshold regime. Carrier transport has been modelled via over-the-barrier thermionic and Fowler-Nordheim contact barrier tunnelling currents over a wide range of gate and drain biases. Simulation of a neuron circuit designed using the 2D-ST with 45 nm CMOS technology components shows high energy efficiency of ~3.5 pJ/spike and biomimetic class-I as well as oscillatory spiking. It also demonstrates complex neuronal behaviors such as spike-frequency adaptation and post-inhibitory rebound that are crucial for dynamic visual systems. Lobula giant movement detector (LGMD) is a collision-detecting biological neuron found in locusts. Our neuron circuit can generate LGMD-like spiking behavior and detect obstacles at an energy cost of <100 pJ. Further, it can be reconfigured to distinguish between looming and receding objects with high selectivity.