{"title":"2D Spectral Transposed Convolutional Neural Network for S-Parameter Predictions","authors":"Yiliang Guo, Xingchen Li, Madhavan Swaminathan","doi":"10.1109/EPEPS53828.2022.9947109","DOIUrl":null,"url":null,"abstract":"In packaging problems, S-parameter predictions are necessary. Machine learning methods lead to dimensionality related challenges which we address here through spectral trans-posed convolutional neural network using 2D kernels. Results show that Normalized Mean-squared Error (NMSE) dropped 0.002 by using 53.7% of the parameters.","PeriodicalId":284818,"journal":{"name":"2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEPS53828.2022.9947109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In packaging problems, S-parameter predictions are necessary. Machine learning methods lead to dimensionality related challenges which we address here through spectral trans-posed convolutional neural network using 2D kernels. Results show that Normalized Mean-squared Error (NMSE) dropped 0.002 by using 53.7% of the parameters.