Modelling of nanowire FETs based neural network for tactile pattern recognition in E-skin

William Taube, Fengyuan Liu, Anastasios Vilouras, Dhayalan Shaktivel, Carlos Garcia Nunez, Hadi Heidari, Fabrice Labeau, Duncan Gregory, Ravinder Dahiya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents device, circuit and system modelling to validate the use of neural nanowire FETs (u-NWFETs) towards a hardware-realizable Neural Network. Hardware neural networks are promising for neuromorphic computing and have many prospective applications for bi-directional interface in prosthetics, and electroceuticals etc. Device simulation of a u-NWFET has been carried out followed by circuit implementation to validate the use of silicon nanowires (Si-NWs) as neuronal elements. A system level simulation of 258 neurons (225 sensor neurons, 50 hidden layer neurons and 3 output layer neurons) has been performed to demonstrate tactile pattern recognition. Training has been carried out and validation of the trained network gives an accurate classification of a database of 50 tactile images into 3 classifiers.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages572-575
Number of pages4
ISBN (Electronic)9781509029594
DOIs
Publication statusPublished - Jan 1 2016
Externally publishedYes
Event12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 - Shanghai, China
Duration: Oct 17 2016Oct 19 2016

Publication series

NameProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016

Other

Other12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
CountryChina
CityShanghai
Period10/17/1610/19/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Instrumentation
  • Biomedical Engineering

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