[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122208-en":3,"doc-seo-122208-105":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":20,"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},122208,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-based Solutions for Comprehending and Mitigating Imperfections of Semiconductor Manufacturing and Testing - Dissertation","Semiconductor manufacturing and testing face escalating challenges that affect cost and yield as technology scales into more variation-sensitive devices. This dissertation addresses process-variation impacts and calibration-driven test overhead by proposing multiple machine learning approaches. Adaptive techniques reduce test costs by optimizing operating voltage in high-volume manufacturing, while multi-temperature-corner strategies cut time-consuming testing. RF impairment models classify and decompose transmitter issues using constellation signatures, improving performance understanding. Yield recovery is pursued via statistical correlations across key test groups, and die-damage risk is reduced by learning from acoustic emissions during wafer dicing. Each method is evaluated on industry-provided datasets, demonstrating measurable benefits in cost, yield, and defect control.","MACHINE LEARNING-BASED SOLUTIONS FOR COMPREHENDING  \nAND MITIGATING IMPERFECTIONS OF SEMICONDUCTOR  \nMANUFACTURING AND TESTING  \nby  \nDeepika Neethirajan  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| Yiorgos Makris, Chair |\n| --- |\n| Mehrdad Nourani |\n| Rashaunda Henderson |\n\nJoseph Friedman  \nCopyright © 2022 Deepika Neethirajan All Rights Reserved  \nTo my Grandparents,  \nwithout whom this wouldn’t have been possible  \nMACHINE LEARNING-BASED SOLUTIONS FOR COMPREHENDING AND MITIGATING IMPERFECTIONS OF SEMICONDUCTOR  \nMANUFACTURING AND TESTING  \nby  \nDEEPIKA NEETHIRAJAN, BE, MS  \nDISSERTATION  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY IN  \nCOMPUTER ENGINEERING  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nDecember 2022  \nACKNOWLEDGMENTS  \nFirst, I would like to thank my advisor, Dr. Yiorgos Makris for his continued guidance and support throughout my research. I would also like to thank Dr. Mehrdad Nourani, Dr. Rashaunda Henderson and Dr. Joseph Friedman, for serving as members of my dissertation committee and for providing valuable feedback.  \nI would like to acknowledge Amit Nahar, Dallas Webster and Sirish Boddikurapati from Texas Instruments; Keith Schaub and Ira Leventhal from Advantest and James Bird from NXP for providing us with the industrial dataset and their valuable inputs that led to the solutions discussed in this dissertation.  \nI thank Dr. Konstantinos Xanthopoulos for being my mentor and Dr. Mohammad Mahdi Bidmeshki for his valuable feedback during my research. I would like to express my gratitude to all my colleagues at the Trusted and Reliable Architectures lab for their continued support and friendship.  \nI would like to express my heartfelt gratitude to my mom and dad for their constant encouragement, support and love.  \nThis endeavor wouldn’t have been possible without my amazing husband Dr. Kiruba Sankaran Subramani and his unwavering love and support. Lastly, I’d like to mention our fur babies-Scooby and Bubbles-for keeping me company during all the late nights.  \nNovember 2022  \nMACHINE LEARNING-BASED SOLUTIONS FOR COMPREHENDING  \nAND MITIGATING IMPERFECTIONS OF SEMICONDUCTOR  \nMANUFACTURING AND TESTING  \nDeepika Neethirajan, PhD  \nThe University of Texas at Dallas, 2022  \nSupervising Professor: Yiorgos Makris  \nIn recent years, significant technological advancements have been made in the semiconductor industry; However, with these advancements, comes a lot of manufacturing and testing challenges that have a direct impact on the cost and yield of the overall outcome. While advanced technology nodes enable production of more powerful devices that have a smaller form factor, operation of such devices is more susceptible to process variations. To address the impacts of process variations without impacting the performance of devices, manufacturers employ post-silicon calibration techniques. One major pitfall of post-silicon calibration is the need to perform numerous test measurements and adjustments that significantly contribute towards the overall test time, thereby hindering the profit margins of new products. Along with the minimal cost expectations, there are higher quality expectations in terms of extremely low number of defects. This results in implementing exhaustive and contemporary test solutions that result in a non-negligible amount of good devices being discarded.  \nIn this work, several machine learning-based solutions are proposed to address the increasing test costs and to recover some of the yield loss. An adaptive test cost reduction technique is proposed to identify the optimal operating voltage for a High-Volume Manufacturing (HVM)  \ndevice, by taking advantage of the correlation that exists between different test measurements and operating voltages. Another test cost reduction technique was proposed to enable testing a device across multiple temperature corners, where the current testing ","cbCaiaDvuV4x77iN","https://ap.wps.com/l/cbCaiaDvuV4x77iN","pdf",10906611,1,94,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 Introduction\n## 1.1 Motivation\n## 1.2 My Contributions\n# Chapter 2 Impairments Classification and Decomposition in RF Transceivers\n## 2.1 Overview\n## 2.2 Impairments","[{\"question\":\"Why do process variations and post-silicon calibration increase manufacturing and testing costs?\",\"answer\":\"Advanced nodes are more susceptible to process variations, and post-silicon calibration requires many test measurements and adjustments. This increases test time and reduces profit margins, while conservative programs can also discard otherwise good devices.\"},{\"question\":\"How does the dissertation reduce test cost in high-volume manufacturing?\",\"answer\":\"It proposes an adaptive technique that identifies an optimal operating voltage by leveraging correlations between different test measurements and operating voltages, reducing unnecessary testing effort.\"},{\"question\":\"What machine learning methods are used to address RF transmitter impairments and yield loss?\",\"answer\":\"For RF transmitters, a model classifies and decomposes impairments using signatures on transmitted signal constellation points. For yield loss from conservative test programs, another approach exploits statistical correlation between two key groups of tests to improve recovery.\"}]","Machine Learning-based Solutions for Comprehending and Mitigating Imperfections of Semiconductor Manufacturing and Testing - Dissertation | PDF",1785809359,237,{"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-based-solutions-for-comprehending-and-mitigating-imperfections-of-semiconductor-manufacturing-and-testing-dissertation","",{"@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-based-solutions-for-comprehending-and-mitigating-imperfections-of-semiconductor-manufacturing-and-testing-dissertation/122208/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do process variations and post-silicon calibration increase manufacturing and testing costs?","Question",{"text":75,"@type":76},"Advanced nodes are more susceptible to process variations, and post-silicon calibration requires many test measurements and adjustments. This increases test time and reduces profit margins, while conservative programs can also discard otherwise good devices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation reduce test cost in high-volume manufacturing?",{"text":80,"@type":76},"It proposes an adaptive technique that identifies an optimal operating voltage by leveraging correlations between different test measurements and operating voltages, reducing unnecessary testing effort.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning methods are used to address RF transmitter impairments and yield loss?",{"text":84,"@type":76},"For RF transmitters, a model classifies and decomposes impairments using signatures on transmitted signal constellation points. For yield loss from conservative test programs, another approach exploits statistical correlation between two key groups of tests to improve recovery.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]