Abstract: DMEA was tasked under Section 843 of the 2019 NDAA with coordinating the establishment of a Pilot Program to test Machine-vision Technologies in the field of counterfeit microelectronic detection. Machine-vision technology includes systems which leverage side-channels and/or machine learning algorithms to assess the authenticity of a microelectronic device. DMEA has contracted CALCE at the University of Maryland to determine the technology readiness levels (TRL) of several such tools and to perform a double blind study for detection of differences between parts. We will review the scope of the blind study and the TRL assessment and what the industry can expect out of this study.

Bio: Dr. Michael H. Azarian is a research scientist at the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland. He holds a Master's and Ph.D. in Materials Science from Carnegie Mellon University, and a Bachelor's degree in Chemical Engineering from Princeton University. Dr. Azarian’s primary research interests are detection, prediction, and analysis of failures in electronic components and assemblies. He has over 150 publications on electronics reliability and packaging, prognostics and health management, and tribology, and holds 5 U.S. patents. Prior to joining CALCE, he spent over a dozen years in the disk drive and fiber optics industries, having worked at Bookham Technology, W.L. Gore, Censtor, and Philips Research Laboratories. Dr. Azarian is chair of the SAE G-19A Test Laboratory Standards Development Committee which is responsible for the AS6171 family of standards on the detection of counterfeit electrical, electronic, and electromechanical parts. He is also co-chairman of the Miscellaneous Techniques sub-group of the G-19A committee. He co-chairs the working group responsible for the IEEE 1624 standard on the organizational reliability capability of suppliers of electronic products. He was previously co-chair for the IEEE 1332 Standard on reliability programs.