{"title":"通过多种机器学习策略将测试反应转化为图像,以进行测试结束预测","authors":"Hongfei Wang, Jingyao Li, Jiayi Wang, Zijun Ping, Hongcan Xiong, Wei Liu, Dongmian Zou","doi":"10.1145/3661310","DOIUrl":null,"url":null,"abstract":"Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.","PeriodicalId":50944,"journal":{"name":"ACM Transactions on Design Automation of Electronic Systems","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies\",\"authors\":\"Hongfei Wang, Jingyao Li, Jiayi Wang, Zijun Ping, Hongcan Xiong, Wei Liu, Dongmian Zou\",\"doi\":\"10.1145/3661310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.\",\"PeriodicalId\":50944,\"journal\":{\"name\":\"ACM Transactions on Design Automation of Electronic Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Design Automation of Electronic Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3661310\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Design Automation of Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3661310","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies
Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.