Department of Electrical Engineering, IIT Bombay, Mumbai, 400076, India.
Department of Electrical Engineering, IIT Gandhinagar, Gandhinagar, 382355, India.
Sci Rep. 2017 Aug 15;7(1):8257. doi: 10.1038/s41598-017-07418-y.
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10 neuron based) large neural networks.
受神经生物学启发的尖峰神经网络 (SNN) 能够实现高效的学习和识别任务。为了实现类似于生物学的大规模网络，需要一种功率和面积效率高的电子神经元。早期，我们通过一种新颖的基于 4 端碰撞电离的 n+/p/n+ 带扩展栅极（门控-INPN）器件，通过物理模拟展示了一种 LIF 神经元。与传统的模拟电路实现相比，观察到面积和功率有了显著的改善。在本文中，我们提出并实验证明了一种紧凑的传统 3 端部分耗尽（PD）SOI-MOSFET（100nm 栅长）来替代 4 端门控-INPN 器件。SOI-MOSFET 中的碰撞电离（II）诱导浮栅效应用于捕获 LIF 神经元行为，以展示输入对尖峰频率的依赖性。与生物学相比，MHz 操作实现了吸引人的硬件加速。总的来说，传统的 PD-SOI-CMOS 技术实现了非常大规模集成（VLSI），这对于生物学规模（基于~10 个神经元）的大型神经网络至关重要。
