A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector

Abstract

Accurately detecting a potential collision and triggering a timely escape response is critical in the field of robotics and autonomous vehicle safety. The lobula giant movement detector (LGMD) neuron in locusts can detect an approaching object and prevent collisions within a swarm of millions of locusts. This single neuronal cell performs nonlinear mathematical operations on visual stimuli to elicit an escape response with minimal energy expenditure. Collision avoidance models based on image processing algorithms have been implemented using analogue very-large-scale-integration designs, but none is as efficient as this neuron in terms of energy consumption or size. Here we report a nanoscale collision detector that mimics the escape response of the LGMD neuron. The detector comprises a monolayer molybdenum disulfide photodetector stacked on top of a non-volatile and programmable floating-gate memory architecture. It consumes a small amount of energy (in the range of nanojoules) and has a small device footprint (~1 µm × 5 µm). By integrating a MoS2 photodetector with a floating-gate memory device, a nanoscale collision detector can be created that mimics the escape response of the lobula giant movement detector neuron.