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Neural network based prediction of stable equivalent series resistance in voltage regulator characterization

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dc.citedby10
dc.contributor.authorZaman M.H.M.en_US
dc.contributor.authorMustafa M.M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorid42262357500en_US
dc.contributor.authorid7102076189en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.date.accessioned2023-05-29T06:52:44Z
dc.date.available2023-05-29T06:52:44Z
dc.date.issued2018
dc.description.abstractHigh demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR. � 2018 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/eei.v7i1.857
dc.identifier.epage142
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85045645286
dc.identifier.spage134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85045645286&doi=10.11591%2feei.v7i1.857&partnerID=40&md5=6d89acb76c7ebf48ff9b4eae771a8f38
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23881
dc.identifier.volume7
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleBulletin of Electrical Engineering and Informatics
dc.titleNeural network based prediction of stable equivalent series resistance in voltage regulator characterizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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