Title :
Counterfeit electronics: A rising threat in the semiconductor manufacturing industry
Author :
Ke Huang ; Carulli, John M. ; Makris, Yiorgos
Author_Institution :
Dept. of Electr. Eng., Univ. of Texas at Dallas, Richardson, TX, USA
Abstract :
As the supply chain of electronic circuits grows more complex, with parts coming from different suppliers scattered across the globe, counterfeit integrated circuits (ICs) are becoming a serious challenge which calls for immediate solutions. Counterfeiting includes re-labeling legitimate chips or illegitimately replicating chips and deceptively selling them as made by the legitimate manufacturer, or simply selling fake chips. Counterfeiting also includes providing defective parts or simply previously used parts recycled from scrapped assemblies. Obviously, there is a multitude of legal and financial implications involved in such activities and even if these devices initially work, they may have reduced lifetime and may pose reliability risks. In this tutorial, we provide a comprehensive review of existing techniques which seek to prevent and/or detect counterfeit integrated circuits. Various approaches are discussed and an advanced machine learning-based method employing parametric measurements is described in detail.
Keywords :
VLSI; circuit analysis computing; integrated circuit manufacture; integrated circuit reliability; learning (artificial intelligence); ICs; VLSI; advanced machine learning-based method; counterfeit electronics; counterfeit integrated circuit detection; electronic circuits; parametric measurements; reliability risks; scrapped assembly; semiconductor manufacturing industry; very large scale integration; Aging; Authentication; Counterfeiting; Integrated circuits; Reliability; Support vector machines; Training;
Conference_Titel :
Test Conference (ITC), 2013 IEEE International
Conference_Location :
Anaheim, CA
DOI :
10.1109/TEST.2013.6651880