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MEMS As a Continuous Time Recurrent Neuron (CTRN) Computing Unit

[+] Author Affiliations
Fadi Alsaleem, Mehari Tesfay, Mostafa Rafaie

University of Nebraska at Lincoln, Omaha, NE

Mohammad H. Hasan

University of Nebraska at Lincoln, Lincoln, NE

Paper No. DETC2018-85075, pp. V004T08A023; 8 pages
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 23rd Design for Manufacturing and the Life Cycle Conference; 12th International Conference on Micro- and Nanosystems
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5179-1
  • Copyright © 2018 by ASME


Smart analysis of sensory data in real time is growing more important by the advent of the internet of things (IoT). Currently, this demand is being met by microprocessors utilizing classical computing algorithms or using simple machine learning schemes; which, aside from their high computing power requirements, are projected to reach an imminent plateau as transistor shrinking is reaching a physical limit. In this paper, we show the potential of transforming the dynamics of micro-electro-mechanical system MEMS by activating electrical resonance to perform simple neurological processes, such as detection and memory, similar to those existing in recurrent neurons.

Copyright © 2018 by ASME



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