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

William Taube, Fengyuan Liu, Anastasios Vilouras, Dhayalan Shakthivel, Carlos García Núñez, Hadi Heidari, Fabrice Labeau, Duncan Gregory, Ravinder Dahiya

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

2 Citations (Scopus)


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 publication2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Number of pages4
ISBN (Electronic)978-1-5090-2959-4
ISBN (Print)978-1-5090-2960-0
Publication statusPublished - 26 Jan 2017
Externally publishedYes


  • silicon neurons
  • silicon nanowires (Si-NWs)
  • artificial neural network (ANN)
  • physical neural networks (PNN)
  • pattern recognition


Dive into the research topics of 'Modelling of nanowire FETs based neural network for tactile pattern recognition in E-skin'. Together they form a unique fingerprint.

Cite this