Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Abstract The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instabili...

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Autores principales:	Udaya Shankar Santhana Krishnan, Kalpana Palanisamy
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/b68c0ec3f79f4426b0c6acd73020ef89
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spelling	oai:doaj.org-article:b68c0ec3f79f4426b0c6acd73020ef892021-11-17T13:28:44ZRecycled integrated circuit detection using reliability analysis and machine learning algorithms1751-861X1751-860110.1049/cdt2.12005https://doaj.org/article/b68c0ec3f79f4426b0c6acd73020ef892021-01-01T00:00:00Zhttps://doi.org/10.1049/cdt2.12005https://doaj.org/toc/1751-8601https://doaj.org/toc/1751-861XAbstract The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely K‐means clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded‐cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K‐means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day.Udaya Shankar Santhana KrishnanKalpana PalanisamyWileyarticleComputer engineering. Computer hardwareTK7885-7895Electronic computers. Computer scienceQA75.5-76.95ENIET Computers & Digital Techniques, Vol 15, Iss 1, Pp 20-35 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95 Udaya Shankar Santhana Krishnan Kalpana Palanisamy Recycled integrated circuit detection using reliability analysis and machine learning algorithms
description	Abstract The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely K‐means clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded‐cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K‐means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day.
format	article
author	Udaya Shankar Santhana Krishnan Kalpana Palanisamy
author_facet	Udaya Shankar Santhana Krishnan Kalpana Palanisamy
author_sort	Udaya Shankar Santhana Krishnan
title	Recycled integrated circuit detection using reliability analysis and machine learning algorithms
title_short	Recycled integrated circuit detection using reliability analysis and machine learning algorithms
title_full	Recycled integrated circuit detection using reliability analysis and machine learning algorithms
title_fullStr	Recycled integrated circuit detection using reliability analysis and machine learning algorithms
title_full_unstemmed	Recycled integrated circuit detection using reliability analysis and machine learning algorithms
title_sort	recycled integrated circuit detection using reliability analysis and machine learning algorithms
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b68c0ec3f79f4426b0c6acd73020ef89
work_keys_str_mv	AT udayashankarsanthanakrishnan recycledintegratedcircuitdetectionusingreliabilityanalysisandmachinelearningalgorithms AT kalpanapalanisamy recycledintegratedcircuitdetectionusingreliabilityanalysisandmachinelearningalgorithms
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Recycled integrated circuit detection using reliability analysis and machine learning algorithms

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