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Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study

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dc.contributor.author Mosin S.
dc.date.accessioned 2020-01-15T21:56:23Z
dc.date.available 2020-01-15T21:56:23Z
dc.date.issued 2019
dc.identifier.issn 1865-0929
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/156425
dc.description.abstract © 2019, Springer Nature Singapore Pte Ltd. Artificial intelligence methods are widely used in different interdisciplinary areas. The paper is devoted to application the method of machine learning and data mining to construction a neuromorphic fault dictionary (NFD) for testing and fault diagnostics in analog/mixed-signal integrated circuits. The main issues of constructing a NFD from the big data point of view are considered. The method of reducing a set of essential characteristics based on the principal component analysis and approach to a cut down the training set using entropy estimation are proposed. The metrics used for estimating the classification quality are specified based on the confusion matrix. The case study results for analog filters are demonstrated and discussed. Experimental results for both cases demonstrate the essential reduction of initial training set and saving of time on the NFD training with high fault coverage up to 100%. The proposed method and approach can be used according to the design-for-testability flow for analog/mixed-signal integrated circuits.
dc.relation.ispartofseries Communications in Computer and Information Science
dc.subject Analog and mixed-signal IC
dc.subject Data mining
dc.subject Diagnostics
dc.subject Entropy
dc.subject Fault coverage
dc.subject Machine learning
dc.subject Neuromorphic fault dictionary
dc.subject Principal component analysis
dc.subject Testing
dc.title Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study
dc.type Conference Paper
dc.relation.ispartofseries-volume 968
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 240
dc.source.id SCOPUS18650929-2019-968-SID85059941032


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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