{"title":"Multiplane light conversion design with physical neural network","authors":"Zheyuan Zhu, Joe H. Doerr, Guifang Li, S. Pang","doi":"10.1364/dh.2022.m6a.2","DOIUrl":null,"url":null,"abstract":"We present a physical neural network (PNN) approach towards multiplane light conversion (MPLC) design. PNN performs a full parameter search with flexible optimization pathways and can tune various design attributes as hyperparameters.","PeriodicalId":227456,"journal":{"name":"Digital Holography and 3-D Imaging 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Holography and 3-D Imaging 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/dh.2022.m6a.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We present a physical neural network (PNN) approach towards multiplane light conversion (MPLC) design. PNN performs a full parameter search with flexible optimization pathways and can tune various design attributes as hyperparameters.