KFU. Card publication. A technique of analog circuits testing and diagnosis based on neuromorphic classifier

KAZAN
FEDERAL UNIVERSITY

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- 中文
- ES

A TECHNIQUE OF ANALOG CIRCUITS TESTING AND DIAGNOSIS BASED ON NEUROMORPHIC CLASSIFIER

Form of presentation

Articles in international journals and collections

Year of publication

2016

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Authors, employees of KFU

Mosin Sergey Gennadevich, author

Bibliographic description in the original language

Mosin, S. "A technique of analog circuits testing and diagnosis based on neuromorphic classifier," in Advances in Intelligent Systems and Computing Vol. 425, 2016, pp. 381-393.

Annotation

The technique of functional testing the analog integrated circuits based on neuromorphic classifier (NC) has been proposed. The structure of NC providing detection both catastrophic and parametric faults taking into account the tolerance on parameters of internal components has been described. The NC ensures the associative fault detection reducing a time on diagnosis in comparison with parametric tables. The approach to selection of essential characteristics used for the NC training has been represented. The wavelet transform of transient responses, Monte Carlo method and statistical processing are used for the essential characteristics selection with maximum distance between faulty and fault-free conditions. The experimental results for the active filter demonstrating high fault coverage and low likelihood of alpha and beta errors at diagnosis have been shown.

Keywords

Analog integrated circuits, Monte Carlo methods, Reconfigurable hardware, Signal processing, Wavelet transforms, Circuits testing, Fault coverages, Functional testing, Neuromorphic classifier, Parametric fault, Statistical processing

The name of the journal

Advances in Intelligent Systems and Computing

URL

http://link.springer.com/chapter/10.1007%2F978-3-319-28658-7_33

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

https://repository.kpfu.ru/eng/?p_id=127241&p_lang=2

Resource files

File name	Size (MB)	Format
P381.pdf	0,18	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Mosin Sergey Gennadevich	ru_RU
dc.date.accessioned	2016-01-01T00:00:00Z	ru_RU
dc.date.available	2016-01-01T00:00:00Z	ru_RU
dc.date.issued	2016	ru_RU
dc.identifier.citation	Mosin, S. "A technique of analog circuits testing and diagnosis based on neuromorphic classifier," in Advances in Intelligent Systems and Computing Vol. 425, 2016, pp. 381-393.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=127241&p_lang=2	ru_RU
dc.description.abstract	Advances in Intelligent Systems and Computing	ru_RU
dc.description.abstract	The technique of functional testing the analog integrated circuits based on neuromorphic classifier (NC) has been proposed. The structure of NC providing detection both catastrophic and parametric faults taking into account the tolerance on parameters of internal components has been described. The NC ensures the associative fault detection reducing a time on diagnosis in comparison with parametric tables. The approach to selection of essential characteristics used for the NC training has been represented. The wavelet transform of transient responses, Monte Carlo method and statistical processing are used for the essential characteristics selection with maximum distance between faulty and fault-free conditions. The experimental results for the active filter demonstrating high fault coverage and low likelihood of alpha and beta errors at diagnosis have been shown.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Analog integrated circuits	ru_RU
dc.subject	Monte Carlo methods	ru_RU
dc.subject	Reconfigurable hardware	ru_RU
dc.subject	Signal processing	ru_RU
dc.subject	Wavelet transforms	ru_RU
dc.subject	Circuits testing	ru_RU
dc.subject	Fault coverages	ru_RU
dc.subject	Functional testing	ru_RU
dc.subject	Neuromorphic classifier	ru_RU
dc.subject	Parametric fault	ru_RU
dc.subject	Statistical processing	ru_RU
dc.title	A technique of analog circuits testing and diagnosis based on neuromorphic classifier	ru_RU
dc.type	Articles in international journals and collections	ru_RU