Ultra-low power neuromorphic obstacle detection using a two-dimensional materials-based subthreshold transistor
Ultra-low power neuromorphic obstacle detection using a two-dimensional materials-based subthreshold transistor
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DOI：
10.1038/s41699-023-00422-z
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发表时间：
2023-09-18
影响因子：
9.7
通讯作者：
Lodha，Saurabh
中科院分区：
文献类型：
--
作者：
Thakar，Kartikey;Rajendran，Bipin;Lodha，Saurabh
准确、及时、有选择性地检测移动障碍物是自主机器人可靠避碰的关键。基于cmos的脉冲神经元用于障碍物检测的面积和能量效率低下，可以通过新兴的二维（2D）材料器件的可重构、可调和低功耗操作能力来解决。我们提出了一个超低功耗的尖峰神经元，使用静电调谐双栅极晶体管与超薄和通用的二维材料通道。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工作。载流子输运已经通过过势垒热离子和Fowler-Nordheim接触势垒隧道电流在大范围的栅极和漏极偏置上进行了建模。利用2D-ST和45 nm CMOS技术元件设计的神经元电路仿真显示，每个尖峰的能量效率高达3.5 pJ，具有仿生i类和振荡尖峰。它还展示了复杂的神经元行为，如峰频适应和抑制后反弹，这对动态视觉系统至关重要。巨叶运动检测器（LGMD）是蝗虫体内发现的一种碰撞检测生物神经元。我们的神经元回路可以产生类似lgmd的尖峰行为，并在能量消耗<100 pJ的情况下检测障碍物。此外，它可以重新配置，以高选择性区分隐现和后退的物体。我们还发现，当2D-ST电流变化为±40%时，尖峰神经元电路可以可靠地工作，并且在输入突触电流中存在加性高斯白噪声时，信噪比高达3db。
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.