  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.
