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Heavy Aluminum Wire Wedge Bonding Strength Prediction Using a Transducer Driven Current Signal and an Artificial Neural Network

contributor authorFuliang Wang
contributor authorJunhui Li
contributor authorShaohua Liu
contributor authorLei Han
date accessioned2020-03-12T23:51:25Z
date available2020-03-12T23:51:25Z
date issued2014
identifier issn0894-6507
identifier other6762998.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1125806?locale-attribute=fa&show=full
formatgeneral
languageEnglish
publisherIEEE
titleHeavy Aluminum Wire Wedge Bonding Strength Prediction Using a Transducer Driven Current Signal and an Artificial Neural Network
typeJournal Paper
contenttypeMetadata Only
identifier padid8305514
subject keywordsaluminium alloys
subject keywordsbackpropagation
subject keywordselectronic engineering computing
subject keywordselectronics packaging
subject keywordslead bonding
subject keywordsmechanical strength
subject keywordsneural nets
subject keywordstransducers
subject keywordswavelet transforms
subject keywordsAl
subject keywordsartificial neural network training
subject keywordsbackpropagation
subject keywordsbonding test samples
subject keywordsdata acquisition system
subject keywordsfundamental frequency component
subject keywordsheavy aluminum wire wedge bonding strength prediction system
subject keywordspower electronic devices
subject keywordsshear strength
subject keywordstransducer driven current signal
subject keywordswavelet model
subject keywordsAcoustics
subject keywordsAluminum
subject keywordsArtificial neura
identifier doi10.1109/TSM.2014.2310223
journal titleSemiconductor Manufacturing, IEEE Transactions on
journal volume27
journal issue2
filesize11797031
citations0


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