Ultra-low power neuromorphic obstacle detection using a two-dimensional materials-based subthreshold transistor

Kartikey Thakar, Bipin Rajendran, Saurabh Lodha

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 modeled via over-the-barrier thermionic and Fowler–Nordheim contact barrier tunneling 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 per 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. We also show that the spiking neuron circuit can function reliably with ±40% variation in the 2D-ST current as well as up to 3 dB signal-to-noise ratio with additive white Gaussian noise in the input synaptic current.

Original languageEnglish (US)
Article number68
Journalnpj 2D Materials and Applications
Volume7
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Materials Science
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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