{"title":"神经网络在延迟故障测试中的应用:测试点插入和随机电路训练","authors":"S. Millican, Yang Sun, Soham Roy, V. Agrawal","doi":"10.1109/ATS47505.2019.000-7","DOIUrl":null,"url":null,"abstract":"This article presents methods of increasing logic built-in self-test (LBIST) delay fault coverage using artificial neural networks (ANNs) to selecting test point (TP) locations a method to train ANNs using randomly generated circuits. This method increases delay test quality both during and after manufacturing. This article also trains ANNs without relying on valuable third-party intellectual property (IP) circuits. Results show higher-quality TPs are selected in significantly reduced CPU time and third-party IP is not be required for ANN training.","PeriodicalId":258824,"journal":{"name":"2019 IEEE 28th Asian Test Symposium (ATS)","volume":"602 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Applying Neural Networks to Delay Fault Testing: Test Point Insertion and Random Circuit Training\",\"authors\":\"S. Millican, Yang Sun, Soham Roy, V. Agrawal\",\"doi\":\"10.1109/ATS47505.2019.000-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents methods of increasing logic built-in self-test (LBIST) delay fault coverage using artificial neural networks (ANNs) to selecting test point (TP) locations a method to train ANNs using randomly generated circuits. This method increases delay test quality both during and after manufacturing. This article also trains ANNs without relying on valuable third-party intellectual property (IP) circuits. Results show higher-quality TPs are selected in significantly reduced CPU time and third-party IP is not be required for ANN training.\",\"PeriodicalId\":258824,\"journal\":{\"name\":\"2019 IEEE 28th Asian Test Symposium (ATS)\",\"volume\":\"602 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 28th Asian Test Symposium (ATS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATS47505.2019.000-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 28th Asian Test Symposium (ATS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATS47505.2019.000-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying Neural Networks to Delay Fault Testing: Test Point Insertion and Random Circuit Training
This article presents methods of increasing logic built-in self-test (LBIST) delay fault coverage using artificial neural networks (ANNs) to selecting test point (TP) locations a method to train ANNs using randomly generated circuits. This method increases delay test quality both during and after manufacturing. This article also trains ANNs without relying on valuable third-party intellectual property (IP) circuits. Results show higher-quality TPs are selected in significantly reduced CPU time and third-party IP is not be required for ANN training.