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.

准确、及时和选择性地检测移动障碍物对于自主机器人可靠地避免碰撞至关重要。用于障碍物检测的基于 CMOS 的尖峰神经元的面积效率和能量效率低下问题可以通过新兴的二维 (2D) 材料设备的可重构、可调和低功耗操作能力来解决。我们提出了一种超低功耗尖峰神经元，它使用具有超薄通用 2D 材料通道的静电调谐双栅极晶体管构建。2D 亚阈值晶体管 (2D-ST) 经过精心设计，可在低电流亚阈值状态下运行。载流子传输通过跨势垒热离子和福勒-诺德海姆接触势垒隧道电流在各种栅极和漏极偏置下进行建模。使用采用 45 nm CMOS 技术组件的 2D-ST 设计的神经元电路的仿真显示出每尖峰约 3.5 pJ 的高能效以及仿生 I 类以及振荡尖峰。它还展示了复杂的神经元行为，例如对于动态视觉系统至关重要的尖峰频率适应和抑制后反弹。小叶巨型运动探测器（LGMD）是在蝗虫中发现的一种碰撞检测生物神经元。我们的神经元电路可以产生类似 LGMD 的尖峰行为，并以 <100 pJ 的能量成本检测障碍物。此外，它可以重新配置以高选择性地区分隐现和后退的物体。