{"title":"优化输入数据向量，改进opc神经网络训练","authors":"G. Teplov, Almira Galeeva, A. Kuzovkov","doi":"10.29003/m2493.mmmsec-2021/137-140","DOIUrl":null,"url":null,"abstract":"This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.","PeriodicalId":151453,"journal":{"name":"Mathematical modeling in materials science of electronic component","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC\",\"authors\":\"G. Teplov, Almira Galeeva, A. Kuzovkov\",\"doi\":\"10.29003/m2493.mmmsec-2021/137-140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.\",\"PeriodicalId\":151453,\"journal\":{\"name\":\"Mathematical modeling in materials science of electronic component\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical modeling in materials science of electronic component\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29003/m2493.mmmsec-2021/137-140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical modeling in materials science of electronic component","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29003/m2493.mmmsec-2021/137-140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC
This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.
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