{"title":"快速掩模近场计算使用全卷积网络","authors":"Jiaxin Lin, Lisong Dong, Taian Fan, Xu Ma, Rui Chen, Yayi Wei","doi":"10.1109/IWAPS51164.2020.9286805","DOIUrl":null,"url":null,"abstract":"Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.","PeriodicalId":165983,"journal":{"name":"2020 International Workshop on Advanced Patterning Solutions (IWAPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Fast mask near-field calculation using fully convolution network\",\"authors\":\"Jiaxin Lin, Lisong Dong, Taian Fan, Xu Ma, Rui Chen, Yayi Wei\",\"doi\":\"10.1109/IWAPS51164.2020.9286805\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.\",\"PeriodicalId\":165983,\"journal\":{\"name\":\"2020 International Workshop on Advanced Patterning Solutions (IWAPS)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Workshop on Advanced Patterning Solutions (IWAPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWAPS51164.2020.9286805\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Workshop on Advanced Patterning Solutions (IWAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWAPS51164.2020.9286805","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast mask near-field calculation using fully convolution network
Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.