[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122279-en":3,"doc-seo-122279-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122279,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning Algorithms for Fault Prediction","Advances in VLSI technology increase transistor counts while shrinking feature sizes, which raises the probability and diversity of manufacturing faults. Test cost and test-data generation effort grow substantially with each technology node, making accurate fault prediction critical for efficient manufacturing testing. The work surveys and categorizes machine learning methods for predicting fault counts and fault-related test-vector sets, compares multiple ML algorithms, and discusses how ML-assisted ATPG reduces time needed to generate required test patterns for production testing.","Machine Learning Algorithms for Fault  \nPrediction  \nSima GonsaiA, Usha MehtaB, Senior Member, IEEE  \nAbstract—With the advancement in VLSI technology, the number of transistors on a device increases along with the reduction in the size of transistor. The likelihood of a manufacturing failure is rises as feature size continue to contract. The overall testing cost and testing efforts are increases exponentially with each new technology node. Therefore, it is necessary to explore the techniques which guarantee the circuit functioning with less efforts and cost. As with each new technology node, not only the possible number of faults in circuit increases but also new types of faults are being introduced. In this scenario, this paper aims to explore the various existing Machine Learning (ML) methods for the prediction of number of faults in circuit. This paper also aims to categorize the fault prediction and prediction of test vector set. The paper includes the comparison analysis of different ML algorithms in fault prediction. With the use of ML algorithm, the Automatic Test Pattern generator (ATPG) shortens the time needed to generate test set required for manufacturing testing.  \nIndex Terms—Fault model, Fault equivalence, Fault Prediction, Stuck-at-Faults, Machine Learning, Test Data, Test Pattern Generation, Automatic Test Pattern Generator, Automatic Test Equipment.  \nI. INTRODUCTION  \nWith shrinking technology, the number of transistors  \nin a given integrated circuit is increasing exponentially. The fabrication at such small  \ngeometry is becoming very much complicated. Soit is very natural that the probability of fabrication defects is increasing immensely for any fabricated IC. Such defects are generally modeled as fault. Further the advancement of transistor technology is coming up with new types of faultsand hence new fault models. Hence, despite of drastic increase in number of transistors per chip, semiconductor industry has shown the reduction in the cost of manufacturing per transistor. But over the years, the cost of testing per transistor is not much reduced comparatively [13] . Among many parameters affecting test cost, key parameter is test data generation.  \nFirst A. Author is with the Institute of Technology, Nirma University, Ahmedabad, India. (e-mail: [23ptphde213@nirmauni.ac.in](23ptphde213@nirmauni.ac.in)).  \nSecond B. Author is with the Institute of Technology, Nirma University, Ahmedabad, India. ([e-mail: usha.mehta@nirmauni.ac.in](e-mail: usha.mehta@nirmauni.ac.in)).  \nEvery fabricated IC is being tested by Automatic Test Equipment (ATE) . Speed of chip testing directly proportional to the number of test patterns generated by ATE. The test data set is being generated by Automatic Test Pattern Generator (ATPG) . When the circuit is made of billions of transistors, the ATPG requires very complex algorithms to predict the possible fault types in circuit, to predict all possible faults in the circuit and to decide the test data set for this fault list. Hence, it is necessary to adopt the new methodologies to support the automation of fault prediction and test data generation. Further, with increase in number of transistors per chip within a small area, peripheral pin accessibility is also becoming a major concern of design process. To efficiently implement the pin-allocation, for pin-access prediction and optimization, speedy and effective algorithms are required [1] .  \nWith improved technology and with development of various machine learning algorithms, Machine Learning (ML) applications are becoming more and more powerful. Different learning algorithms are used to program machine learning models. These algorithms can learn from a given set of data. This data can be in any form such as integers, strings, images, videos, audio, etc. For example, voice acknowledgment frameworks, Siri and Cortana relies on machine learning and profound neural systems like Deep Neural Network (DNN) to mimic human communicati","cbCaik4cU7cOOirA","https://ap.wps.com/l/cbCaik4cU7cOOirA","pdf",326316,1,5,"English","en",105,"# Introduction\n# Proposed Methodology\n## Problem Statement\n## Proposed Algorithms for Prediction\n# Results Analysis\n# Challenges\n# Future Scope\n# Conclusion","[{\"question\":\"Why is fault prediction important as VLSI technology scales down?\",\"answer\":\"Smaller feature sizes increase the likelihood of fabrication defects, and new technologies introduce more fault types. This drives higher testing complexity and cost, making fault prediction essential.\"},{\"question\":\"What problem does the paper focus on regarding fault prediction?\",\"answer\":\"It performs a comparative analysis of fault prediction approaches and aims to identify the best algorithm for predicting faults in combinational circuits.\"},{\"question\":\"How does machine learning relate to ATPG and test pattern generation in this work?\",\"answer\":\"The study uses ML algorithms to predict faults and employs ATPG and EDA tools (such as Synopsys and Cadence) to generate datasets for fault coverage and test set generation, helping ATPG generate test patterns more efficiently.\"}]","Machine Learning Algorithms for Fault Prediction | PDF",1785809790,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-algorithms-for-fault-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-for-fault-prediction/122279/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fault prediction important as VLSI technology scales down?","Question",{"text":75,"@type":76},"Smaller feature sizes increase the likelihood of fabrication defects, and new technologies introduce more fault types. This drives higher testing complexity and cost, making fault prediction essential.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper focus on regarding fault prediction?",{"text":80,"@type":76},"It performs a comparative analysis of fault prediction approaches and aims to identify the best algorithm for predicting faults in combinational circuits.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning relate to ATPG and test pattern generation in this work?",{"text":84,"@type":76},"The study uses ML algorithms to predict faults and employs ATPG and EDA tools (such as Synopsys and Cadence) to generate datasets for fault coverage and test set generation, helping ATPG generate test patterns more efficiently.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]