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  • 标题:Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET
  • 本地全文:下载
  • 作者:Sangya Dutta ; Vinay Kumar ; Aditya Shukla
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2017
  • 卷号:7
  • 期号:1
  • DOI:10.1038/s41598-017-07418-y
  • 语种:English
  • 出版社:Springer Nature
  • 摘要: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(11) neuron based) large neural networks.
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