Counterfeit electronic components create risks when incorporated into critical systems and infrastructures since they are often below specification and/or of substandard quality. There are several different counterfeit types in the supply chain today: recycled, remarked, overproduced, out-of-spec/defective, cloned, forged documentation, and tampered. Most of the counterfeit components in the market today are chips that are not equipped with mechanisms to aid in detection of each counterfeit type. Thus, detection relies mostly on physical inspection and electrical testing. In this work, the main objective is to improve the effectiveness of physical inspection tests which appears to be the most promising way of detecting all types of counterfeits. In this project, exhaustive characterization of counterfeit and authentic ICs is being performed in order to develop targeted defect taxonomies and a database of defect measurements for the research community. For the first time, counterfeit defects are quantified in order to develop algorithms that automatically classify suspect ICs as counterfeit or authentic In addition the PIs are developing novel non-destructive characterization techniques which shall reduce test time significantly. Artifacts that will be shared include sample images and measurements of all defects found as well as aggregated information related to frequency of defects, mapping of defects to counterfeit type, etc. Since the proposed methods will be able to detect counterfeits of all types of microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), commercial-off-the-shelf (COTS), and analog devices, many critical applications within automotive, military, medical, and transportation systems will benefit from this research. Finally, benefits for society include trustworthy electronics for healthcare and dependable computing platforms for intelligence, weather forecasting, financial transactions, military systems, cyber physical systems, and so forth.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Communication Foundations (CCF)
Type
Standard Grant (Standard)
Application #
1423282
Program Officer
Almadena Y. Chtchelkanova
Project Start
Project End
Budget Start
2014-08-01
Budget End
2015-10-31
Support Year
Fiscal Year
2014
Total Cost
$425,000
Indirect Cost
Name
University of Connecticut
Department
Type
DUNS #
City
Storrs
State
CT
Country
United States
Zip Code
06269