Yield Estimation of a Memristive Sensor Array

Vishal Gupta, Saurabh Khandelwal, Giulio Panunzi, Eugenio Martinelli, Said Hamdioui, Abusaleh Jabir, Marco Ottavi

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

Abstract

This paper proposes a method to calculate the yield of a memristor based sensor array considered as the probability that the chip provides acceptable sensing results when the array is affected by manufacturing defects. The modeling is based on a Markov Chain approach, in which each state represents an operating chip configuration and the state transitions take into account manufacturing defects. The proposed method is applicable to evaluate the yield with different fault models to achieve the comparative yield obtained by several redundancy allocations.

Original languageEnglish
Title of host publicationProceedings - 2020 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
PublisherIEEE
ISBN (Electronic)9781728181875
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes
Event26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 - Virtual, Online, Italy
Duration: 13 Jul 202016 Jul 2020
Conference number: 26
https://www.iolts2020virtual.cloud/welcome/

Conference

Conference26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
Abbreviated titleIOLTS 2020
Country/TerritoryItaly
CityVirtual, Online
Period13/07/2016/07/20
Internet address

Keywords

  • Gas Sensor
  • Markov Modeling
  • Memristor
  • Redundancy
  • Yield

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