Yield Estimation of a Memristive Sensor Array

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Yield Estimation of a Memristive Sensor Array

Conference Paper (2020)

Author(s)

Vishal Gupta (University of Rome Tor Vergata)

Saurabh Khandelwal (Oxford Brookes University)

Giulio Panunzi (University of Rome Tor Vergata)

Eugenio Martinelli (University of Rome Tor Vergata)

Said Hamdioui (TU Delft - Quantum & Computer Engineering)

Abusaleh Jabir (Oxford Brookes University)

Marco Ottavi (University of Rome Tor Vergata)

Department

Quantum & Computer Engineering

Redundancy Memristor Yield Markov Modeling Gas Sensor

DOI related publication

https://doi.org/10.1109/IOLTS50870.2020.9159727 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:af183d72-6a98-4189-a8d5-772236fecca0

More Info

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Publication Year

2020

Language

English

Department

Quantum & Computer Engineering

Article number

9159727

Pages (from-to)

1-2

ISBN (print)

978-1-7281-8188-2

ISBN (electronic)

978-1-7281-8187-5

Event

26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 (2020-07-13 - 2020-07-16), Virtual, Online, Italy

Downloads counter

213

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.