DocumentCode :
1757463
Title :
Recycled IC Detection Based on Statistical Methods
Author :
Ke Huang ; Yu Liu ; Korolija, Nenad ; Carulli, John M. ; Makris, Yiorgos
Author_Institution :
Dept. of Electr. & Comput. Eng., San Diego State Univ., San Diego, CA, USA
Volume :
34
Issue :
6
fYear :
2015
fDate :
42156
Firstpage :
947
Lastpage :
960
Abstract :
We introduce two statistical methods for identifying recycled integrated circuits (ICs) through the use of one-class classifiers and degradation curve sensitivity analysis. Both methods rely on statistically learning the parametric behavior of known new devices and using it as a reference point to determine whether a device under authentication has previously been used. The proposed methods are evaluated using actual measurements and simulation data from digital and analog devices, with experimental results confirming their effectiveness in distinguishing between new and aged ICs and their superiority over previously proposed methods.
Keywords :
counterfeit goods; identification technology; integrated circuits; recycling; sensitivity analysis; statistical analysis; aged IC; authentication; degradation curve sensitivity analysis; one-class classifiers; recycled IC detection; recycled integrated circuits; statistical methods; Aging; Degradation; Integrated circuits; Kernel; Stress; Support vector machines; Training; Degradation curve sensitivity analysis (DCSA); Recycled IC detection; degradation curve sensitivity analysis; one-class classifier; one-class classifier (OCC); parametric burn-in test; recycled integrated circuit (IC) detection;
fLanguage :
English
Journal_Title :
Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0070
Type :
jour
DOI :
10.1109/TCAD.2015.2409267
Filename :
7055850
Link To Document :
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