[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125185-en":3,"doc-seo-125185-105":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},125185,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","On-chip Age Estimation Using Machine Learning - Paper","The semiconductor supply chain spreads globally to satisfy high demand for integrated circuits, but escalating device utilization increases e-waste and raises risks to IC security and reliability, especially counterfeit parts such as recycled and remarked components. The study harvests BTI and HCI aging effects by tracking frequency and discharge time changes alongside drain current and sub-threshold leakage over an IC lifetime to estimate IC age. Cadence simulations use 22-nm CMOS ring oscillators and GlobalFoundries aging models, then apply SVR with training features including temperature, τdv, f, process variation, and aging time. Results show high estimation accuracy across 13- and 51-stage ROs.","Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  \nDigital Object Identifier 10.1109/ACCESS.2024.0429000  \nOn-chip Age Estimation Using Machine Learning  \nTURKI ALNUAYRI 1,2, SAQIB KHURSHEED2, and DANIELE ROSSI3  \n1Department of Computer Engineering, Taibah University, Medina, 42353, Saudi Arabia ([e-mail: T.Alnuayri@liverpool.ac.uk & Tnuayri@taibahu.edu.sa](e-mail: T.Alnuayri@liverpool.ac.uk & Tnuayri@taibahu.edu.sa)) 2Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK (e-mail: [S.Khursheed@liverpool.ac.uk](S.Khursheed@liverpool.ac.uk))  \n3Department of Information Engineering, University of Pisa, Pisa, 56122, Italy (e-mail: [daniele.rossi1@unipi.it](daniele.rossi1@unipi.it))  \nCorresponding author: Turki Alnuayri ([e-mail: T.Alnuayri@liverpool.ac.uk](e-mail: T.Alnuayri@liverpool.ac.uk)).  \nThis work was supported in part by the Department of Computer Engineering, Taibah University, Saudi Arabia; in part by the Department of Electrical Engineering and Electronics; in part by the ODA Research Seed Funding, University of Liverpool, U.K.; in part by Italian Ministry of Education and Research in the framework of the CrossLab and FoReLab Projects (Departments of Excellence); and in part by the Department of Information Engineering, University of Pisa, Italy.  \n ABSTRACT The semiconductor supply chain industry is spread worldwide to reduce costs and meet the high demand for integrated circuits (ICs) in electronic systems. The high utilisation of electronic devices in the next decade is forecasted to reach trillions, increasing the already high volume of e-waste. It will lead to concerns about the security and reliability of ICs, particularly those exposed to counterfeiting, i.e., recycled and remarked ICs. This paper harvests aging degradation induced by bias temperature instability (BTI) and hot carrier injection (HCI), observing frequency (f ) and discharge time (τdv ) affected by changes in drain current and sub-threshold leakage current over the lifetime of an IC to estimate the IC age. This is carried out using Cadence simulations, implementing 13-and 51-stage ring oscillators (ROs) using a 22-nm CMOS technology and aging model provided by GlobalFoundries (GF) . The machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, τdv ,f , aging time and inter-die and intra-die process variation (PV) . The data sampling is performed over a simulated 12-year period with representative temperatures between 20◦ C up to 100◦ C and with additional testing data from 25◦ C up to 75◦ C. Incorporating the PV effect with the SVR model allows the proposed SVR model tobe adopted in practical IC implementation. The results demonstrate high accuracy in aging estimation by SVR with/without PV effects. The proposed SVR model detects the age of an IC with an error accuracy between 0.206 and 0.667 (deviation of 74.16 and 240.12 days), and 0.091 and 0.237 (deviation of 32.76 and 85.32 days) based on the Root Mean Square Error (RMSE) for 13-and 51-satge RO, respectively. It outperforms the state-of-the-art IC age prediction models even when learning and validating the model with aging and PV.  \n INDEX TERMS Bias temperature instability (NBTI/PBTI), counterfeit ICs, e-waste, green ICT, hot carrier injection (HCI), IC Age estimation, ML for IC age prediction, subthreshold leakage and drain current.  \nI. INTRODUCTION  \nTHE distribution of the semiconductor industry across the  \nglobe jeopardises the security and reliability of electronic systems as it exposes it to the risk of counterfeiting, which is a growing threat for modern ICs. The semiconductor market has experienced astonishing growth in recent decades, and it is expected to persistently increase from $573.44 billion in 2022 to a remarkable $1380.79 billion by 2029 [1] . The high demand on the semiconductor supply chain to","cbCaityDLmGntYRp","https://ap.wps.com/l/cbCaityDLmGntYRp","pdf",4967985,1,19,"English","en",105,"# Abstract\n# Introduction\n## Related Work","[{\"question\":\"为什么需要进行片上（on-chip）IC寿命/年龄估计？\",\"answer\":\"因为半导体供应链分布全球且易被伪造，使用回收或“翻新/标注”的IC可能导致电路性能与寿命不匹配，从而影响系统安全与可靠性。\"},{\"question\":\"文中如何从IC上提取用于估计年龄的信息？\",\"answer\":\"通过采集BTI和HCI诱导的老化退化，并观察频率和放电时间在寿命过程中的变化，同时关联漏电流（亚阈值漏电）与栅极/漏极相关电流特征。\"},{\"question\":\"使用了什么机器学习方法来完成年龄估计？\",\"answer\":\"采用支持向量回归（SVR），训练时引入温度、τdv、f、老化时间以及器件间/器件内工艺过程变异（PV）等特征，并在不同环振器规模下评估性能。\"}]","On-chip Age Estimation Using Machine Learning - Paper | PDF",1785897263,48,{"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},"on-chip-age-estimation-using-machine-learning-paper","",{"@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/on-chip-age-estimation-using-machine-learning-paper/125185/",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","2026-08-05",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},"为什么需要进行片上（on-chip）IC寿命/年龄估计？","Question",{"text":75,"@type":76},"因为半导体供应链分布全球且易被伪造，使用回收或“翻新/标注”的IC可能导致电路性能与寿命不匹配，从而影响系统安全与可靠性。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文中如何从IC上提取用于估计年龄的信息？",{"text":80,"@type":76},"通过采集BTI和HCI诱导的老化退化，并观察频率和放电时间在寿命过程中的变化，同时关联漏电流（亚阈值漏电）与栅极/漏极相关电流特征。",{"name":82,"@type":73,"acceptedAnswer":83},"使用了什么机器学习方法来完成年龄估计？",{"text":84,"@type":76},"采用支持向量回归（SVR），训练时引入温度、τdv、f、老化时间以及器件间/器件内工艺过程变异（PV）等特征，并在不同环振器规模下评估性能。","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]