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

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MACHINE LEARNING AND DATA MINING METHODS IN TESTING AND DIAGNOSTICS OF ANALOG AND MIXED-SIGNAL INTEGRATED CIRCUITS: CASE STUDY

Form of presentation

Articles in international journals and collections

Year of publication

2019

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Authors, employees of KFU

Mosin Sergey Gennadevich, author

Osin Yuriy Nikolaevich, author

Bibliographic description in the original language

Mosin S., Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study//Communications in Computer and Information Science. - 2019. - Vol.968, Is.. - P.240-255.

Annotation

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 %.

Keywords

Machine Learning, Data Mining, Testing, Diagnostics, Analog and Mixed-Signal IC, Entropy, Principal Component Analysis, Fault Coverage, Neuromorphic Fault Dictionary

The name of the journal

Communications in Computer and Information Science

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059941032&doi=10.1007%2f978-981-13-5758-9_21&partnerID=40&md5=901ae3f2d300ad037b88584345d2fcec

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=195184&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Mosin Sergey Gennadevich	ru_RU
dc.contributor.author	Osin Yuriy Nikolaevich	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Mosin S., Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study//Communications in Computer and Information Science. - 2019. - Vol.968, Is.. - P.240-255.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=195184&p_lang=2	ru_RU
dc.description.abstract	Communications in Computer and Information Science	ru_RU
dc.description.abstract	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 %.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Machine Learning	ru_RU
dc.subject	Data Mining	ru_RU
dc.subject	Testing	ru_RU
dc.subject	Diagnostics	ru_RU
dc.subject	Analog and Mixed-Signal IC	ru_RU
dc.subject	Entropy	ru_RU
dc.subject	Principal Component Analysis	ru_RU
dc.subject	Fault Coverage	ru_RU
dc.subject	Neuromorphic Fault Dictionary	ru_RU
dc.title	Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study	ru_RU
dc.type	Articles in international journals and collections	ru_RU