[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120340-en":3,"doc-seo-120340-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120340,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",6,"Technology","Electronics authentication using electrical measurements and machine learning - Research paper - Nondestructive counterfeit detection","The problem of counterfeiting in electronics remains critical because suspicious items may not always be inauthentic, making reliable identification difficult. The study presents nondestructive detection of counterfeit devices using electrical measurements only, enabling machine-learning-assisted classification to distinguish genuine from fake electronics. Physical de-processing is used as final confirmation. The approach targets practical authentication needs by leveraging electrical signatures to support low-cost acceptance testing without damaging devices or driving high costs.","Microelectronics Reliability 168 (2025) 115652  \nContents lists available at ScienceDirect  \nMicroelectronics Reliability  \njournal [homepage: www.elsevier.com/locate/microrel](homepage: www.elsevier.com/locate/microrel)  \n| Research paper\u003Cbr>Electronics authentication using electrical measurements and machine learning☆\u003Cbr>S. Carta a, A. Urrub, M. Musab, P. Andronicob, G. Mura a,*\u003Cbr>a Department of Electrical and Electronic Eng., University of Cagliari, Cagliari, Italy b Nurjana Technologies srl, Via Mario Betti 27/29, Elmas, Cagliari, Italy |  |\n| --- | --- |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Counterfeit electronics Fake electronics\u003Cbr>Electrical measurements Non-destructive detection Machine-learning algorithms Fake amplifiers | A B S T R A C T |\n|  | The problem of counterfeiting in electronics is not recent but still critical today. Identifying counterfeit devices can be a complex task since not all suspicious items are necessarily inauthentic. The paper deals with the nondestructive detection of counterfeiting in electronics by using only electrical measurements. This approach paves the way for machine learning classification-assisted counterfeit detection through electrical measurements. Physical de-processing provides the final confirmation. |\n\n1. Introduction  \nCounterfeiting in the electronics industry is a major issue that can lead to serious problems such as personal injuries, mission failures, and reduced system reliability. Although the problem of counterfeit electronics has existed for some time, it remains a critical issue today [1–5].  \nIt is essential to abstain from purchasing from unauthorized distributors to avoid the risk of counterfeit parts. However, when obsolescence forces the need to obtain parts from other sources, i.e. when manufacturers and their authorized wholesalers no longer provide them, problems can arise in avoiding counterfeit parts. This is especially true when replacing components that were made several years ago.  \nMany different techniques can be used to address various types of counterfeiting, and detection and avoidance methods are continually evolving [6–8] to stay ahead of counterfeiters who may adapt their strategies.  \nThere is an increasing need for affordable and non-destructive methods of detecting counterfeit parts mixed in the same delivery packaging without wasting material or incurring high costs. Moreover, developing a unique detection method that comprehensively captures the entire taxonomy is complex. In recent years, extensive research efforts have focused on automating the detection process by using machine learning algorithms [9]. For instance, in [10], the packaging encapsulant material is chemically characterized to detect counterfeits. In [11], x-ray computed tomography, image processing, and machine  \nlearning algorithms are used to detect die-face delamination in a 3D image. Additionally, scratches are identified through artificial neural networks on an IC packaging image acquired using an optical microscope [12]. In [13], counterfeit ICs are detected through a one-class support vector machine classifier trained using only parametric measurements of brand-new devices in production. Terahertz signals and terahertz time-domain spectroscopy-based machine learning approach is conducted to demonstrate the capabilities of this non-destructive physical inspection method for counterfeit IC detection [14,15].  \nPublished approaches cannot detect accurately all the types of counterfeits, but they partially have the potential to prevent fakes.  \nElectrical measurements represent an important non-destructive step in the verification process because they can determine whether the devices are functionally conformed to the datasheet and are conclusive in identifying the failure modes in defective units.  \nOur research compared the electrical characteristics ofa wide variety of real (not simulated) devices acquired from the grey market with a large set of genuine devices ","cbCaislcUvEApFle","https://ap.wps.com/l/cbCaislcUvEApFle","pdf",3305959,1,"English","en",105,"# Introduction\n## Background and motivation\n## Related detection approaches\n## Role of electrical measurements and ML\n# Experimental\n## Devices under test","[{\"question\":\"Why is counterfeit electronics detection difficult?\",\"answer\":\"Not all suspicious items are necessarily inauthentic, so identification can be complex and uncertain. The paper emphasizes that the problem remains critical despite existing efforts.\"},{\"question\":\"What is the proposed method for detecting counterfeit electronics?\",\"answer\":\"The approach uses nondestructive electrical measurements combined with machine-learning classification to distinguish counterfeit devices from genuine ones. Physical de-processing is applied for final confirmation.\"},{\"question\":\"What kinds of devices are used for the study?\",\"answer\":\"The experiments use LMxxx power amplifiers in a plastic dual-inline package, selected as a proof-of-concept example. The work compares grey-market devices against genuine devices sourced from an authorized distributor.\"}]","Electronics authentication using electrical measurements and machine learning - Research paper - Nondestructive counterfeit detection | PDF",1785729561,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"electronics-authentication-using-electrical-measurements-and-machine-learning-research-paper-nondestructive-counterfeit-detection","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/electronics-authentication-using-electrical-measurements-and-machine-learning-research-paper-nondestructive-counterfeit-detection/120340/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is counterfeit electronics detection difficult?","Question",{"text":74,"@type":75},"Not all suspicious items are necessarily inauthentic, so identification can be complex and uncertain. The paper emphasizes that the problem remains critical despite existing efforts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is the proposed method for detecting counterfeit electronics?",{"text":79,"@type":75},"The approach uses nondestructive electrical measurements combined with machine-learning classification to distinguish counterfeit devices from genuine ones. Physical de-processing is applied for final confirmation.",{"name":81,"@type":72,"acceptedAnswer":82},"What kinds of devices are used for the study?",{"text":83,"@type":75},"The experiments use LMxxx power amplifiers in a plastic dual-inline package, selected as a proof-of-concept example. 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