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Impact of Parasitic Capacitance on Neuromorphic Neuron Circuits

  • Bo Sun
  • , Ziquan Lei
  • , Jiayi Li
  • , Chunbing Guo
  • Guangdong University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, the impact of the parasitic parameters of wires in a package on performance of neuron circuits is studied with consideration of neural information encoding characteristics. The H-H neuron and the Izhikevich neuron are used for their simple structure and good biophysics realistic. One of neural information codes, which is called S-Space Coding, is selected as the major indicator of the neuron's performance. A simplified T-Type transmission line is applied to describe the parasitic parameters of wires in package. A set of pulse-width modulation signal is used as stimulations in numerical studies. According to input and output, S-Space symbol sequences of selected neurons can be calculated. By analyzing the symbol sequence, this work finds that the wire resistance may block input signal to neurons. An input port circuit is needed for, instead of connecting input end and neurons with wires directly. The parasitic capacitance introduces a significant delay on output spikes. With the help of S-Space coding, parasitic capacitance has little effect on neural information.

Original languageEnglish
Title of host publication2020 21st International Conference on Electronic Packaging Technology, ICEPT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728168265
DOIs
StatePublished - Aug 2020
Externally publishedYes
Event21st International Conference on Electronic Packaging Technology, ICEPT 2020 - Guangzhou, China
Duration: 12 Aug 202015 Aug 2020

Publication series

Name2020 21st International Conference on Electronic Packaging Technology, ICEPT 2020

Conference

Conference21st International Conference on Electronic Packaging Technology, ICEPT 2020
Country/TerritoryChina
CityGuangzhou
Period12/08/2015/08/20

Keywords

  • Neuromorphic Circuit
  • Neuron Modeling
  • Parasitic Capacitance
  • S-Space Coding

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