{"title":"基于学习的表征模型用于新兴存储技术的质量保证","authors":"Xhesila Xhafa, P. Girard, A. Virazel","doi":"10.1109/ETS56758.2023.10174202","DOIUrl":null,"url":null,"abstract":"The shrinking of technology nodes has led to high-density memories containing large amounts of transistors which are prone to defects and reliability issues. Their test is generally based on the use of well-known March algorithms targeting Functional Fault Models (FFMs). This Ph.D. thesis aims to introduce a novel approach for advanced and emerging memory testing that relies on the Cell-Aware (CA) methodology to further improve the yield of System on Chips (SoCs).","PeriodicalId":211522,"journal":{"name":"2023 IEEE European Test Symposium (ETS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies\",\"authors\":\"Xhesila Xhafa, P. Girard, A. Virazel\",\"doi\":\"10.1109/ETS56758.2023.10174202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The shrinking of technology nodes has led to high-density memories containing large amounts of transistors which are prone to defects and reliability issues. Their test is generally based on the use of well-known March algorithms targeting Functional Fault Models (FFMs). This Ph.D. thesis aims to introduce a novel approach for advanced and emerging memory testing that relies on the Cell-Aware (CA) methodology to further improve the yield of System on Chips (SoCs).\",\"PeriodicalId\":211522,\"journal\":{\"name\":\"2023 IEEE European Test Symposium (ETS)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE European Test Symposium (ETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETS56758.2023.10174202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE European Test Symposium (ETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETS56758.2023.10174202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies
The shrinking of technology nodes has led to high-density memories containing large amounts of transistors which are prone to defects and reliability issues. Their test is generally based on the use of well-known March algorithms targeting Functional Fault Models (FFMs). This Ph.D. thesis aims to introduce a novel approach for advanced and emerging memory testing that relies on the Cell-Aware (CA) methodology to further improve the yield of System on Chips (SoCs).
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