Please use this identifier to cite or link to this item: https://doi.org/10.1007/s005420100099
DC FieldValue
dc.titleBackpropagation approximation approach based generation of macromodels for static and dynamic simulations
dc.contributor.authorTay, F.E.H.
dc.contributor.authorOngkodjojo, A.
dc.contributor.authorLiang, Y.C.
dc.date.accessioned2014-06-17T02:40:08Z
dc.date.available2014-06-17T02:40:08Z
dc.date.issued2001-10
dc.identifier.citationTay, F.E.H., Ongkodjojo, A., Liang, Y.C. (2001-10). Backpropagation approximation approach based generation of macromodels for static and dynamic simulations. Microsystem Technologies 7 (3) : 120-136. ScholarBank@NUS Repository. https://doi.org/10.1007/s005420100099
dc.identifier.issn09467076
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55181
dc.description.abstractIn this paper, a novel backpropagation approximation approach based macromodeling technique for a lateral folded-beam comb-drive micro-resonator is proposed. For static simulations, the results show that the macromodel speeds up simulations by a maximum factor of 291 over the finite element approach with less than 1.4% error for small displacements of up to 2 μm. The simulation results also show good agreement with the experimental results. For dynamic simulations, it speeds up simulations by a factor of 456 over the finite element approach with less than 2% error for an applied Vac of up to 125 V. The findings also show that the dynamic transient analysis of our device is valid for an applied Vdc of up to 125 V. Similarly, the dynamic simulation results also show quality agreement with the experimental results.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s005420100099
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1007/s005420100099
dc.description.sourcetitleMicrosystem Technologies
dc.description.volume7
dc.description.issue3
dc.description.page120-136
dc.identifier.isiut000172501200007
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