{"title":"Test Data Compression with Partial LFSR-Reseeding","authors":"Yu-Hsuan Fu, Sying-Jyan Wang","doi":"10.1109/ATS.2005.105","DOIUrl":null,"url":null,"abstract":"The large amount of test data becomes a serious problem in SOC testing. In this paper, we propose a method to improve the LFSR reseeding based compression scheme. This method rearranges a given set of test data by merging and partitioning test cubes so that they can be decompressed with a fixed-length LFSR. The compression is done by eliminating repeated patterns in consecutive seeds. A singlepolynomial LFSR is used, so that the decompression process is simple and fast. Besides, it does not need an on-chip decoder. The compression method is very efficient, as experimental results show that it reduces 23.6% of stored data and 34.8% of transferred data compared with the previous methods.","PeriodicalId":373563,"journal":{"name":"14th Asian Test Symposium (ATS'05)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"14th Asian Test Symposium (ATS'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATS.2005.105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The large amount of test data becomes a serious problem in SOC testing. In this paper, we propose a method to improve the LFSR reseeding based compression scheme. This method rearranges a given set of test data by merging and partitioning test cubes so that they can be decompressed with a fixed-length LFSR. The compression is done by eliminating repeated patterns in consecutive seeds. A singlepolynomial LFSR is used, so that the decompression process is simple and fast. Besides, it does not need an on-chip decoder. The compression method is very efficient, as experimental results show that it reduces 23.6% of stored data and 34.8% of transferred data compared with the previous methods.