Counterfeit Detection: Imaging Based

Instructor 

Dr. Navid Asadi is an Associate Professor in the Electrical and Computer Engineering Department at the University of Florida with an affiliation to the Materials Science and Engineering Department.

Learning Objectives

This micro-certificate is designed to equip participants with both the theoretical knowledge and practical skills required to identify counterfeit integrated circuits (ICs) and printed circuit boards (PCBs) using imaging-based methods. The course integrates optical imaging, microscopy, and X-ray tomography, with emphasis on both destructive and non-destructive approaches. Participants will begin with defect detection in ICs using image processing and neural networks, then transition to PCB-level imaging, where they will learn how to optimize image quality, apply optical microscopy, and use X-ray tomography for multilayer analysis. The course highlights real-world applications in aerospace, defense, automotive, and telecommunication industries, helping learners understand both the technical processes and the broader impact of counterfeit detection on supply chain security. By the end of the program, learners will be able to identify counterfeit signatures, apply imaging trade-offs effectively, and implement algorithmic workflows to improve detection efficiency.

In this certificate course, students will learn the principles and techniques of counterfeit detection using imaging-based methods. The course covers how counterfeit ICs and PCBs can be identified through optical imaging, microscopy, and nondestructive X-ray tomography. Students will also explore how to optimize image quality and employ machine learning for automated detection.

This micro-certificate course is organized into a set of units described below:

• Unit 1: Counterfeit IC Detection: This unit introduces optical imaging techniques for counterfeit IC detection. Students will learn about defect taxonomy, preprocessing methods to remove confounding elements, and the application of artificial neural networks to automate counterfeit identification.
• Unit 2: Counterfeit PCB Detection: This unit focuses on counterfeit PCB detection using both optical and X-ray imaging. It covers image calibration (RGB adjustment, white balance, glare reduction), optical microscopy for structural analysis of ICs/PCBs, and X-ray tomography for nondestructive reverse engineering of multilayer boards. Students will understand how to combine these approaches to detect counterfeits with improved accuracy.

Learning Outcomes:

By completing this micro-certificate, participants will be able to:

• Detect counterfeit IC defects using optical imaging, preprocessing, and machine learning approaches.
• Optimize image quality through RGB calibration, white balance, and glare reduction.
• Apply optical microscopy to analyze IC and PCB structures, adjusting magnification and preparing samples.
• Perform nondestructive analysis of multilayer PCBs using X-ray tomography, including stitching and CAD reconstruction.
• Compare destructive vs. nondestructive imaging methods and select the appropriate approach for detection scenarios.
• Integrate algorithmic workflows for counterfeit detection in both research and industry contexts.

Prerequisites:

• General: Basic knowledge of electronics and PCB design
• Helpful: Introductory understanding of imaging concepts (microscopy, resolution)
• Optional: Prior exposure to machine learning or data analysis methods

Target Audience

Designed for U.S. citizens working in the Department of War, Government, or Government-affiliated employees, industry, as well as college students and faculty. Must register with your organizational email, and will be notified of acceptance within one week of the course start date.

Biography

Navid Asadi is an Associate Professor in the Electrical and Computer Engineering Department at the University of Florida with an affiliation to the Materials Science and Engineering department. He investigates novel techniques for electronics inspection and assurance, system and chip level decomposition and security assessment, anti-reverse engineering, 3D imaging, invasive and semi-invasive methods, supply chain security, etc. Dr. Asadi is director of the Security and Assurance (SCAN) lab house to more than $12M advanced imaging and characterization equipment. He also serves as the associate director of the Florida Semiconductor Institute (FSI), and the Microelectronics Security Training (MEST) center which is a multi-million dollar program to train and reskill the professional engineers in the area of security. Dr. Asadi  has received his NSF CAREER award in 2022 and several best paper awards from IEEE International Symposium on Hardware Oriented Security and Trust (HOST) and the ASME International Symposium on Flexible Automation (ISFA). He was also winner of D.E. Crow Innovation award from University of Connecticut. He is also the general chair of the IEEE Physical Assurance and Inspection of Electronics (PAINE) Conference. His projects are sponsored by various government agencies and industry including but not limited to NSF, AFRL, AFOSR, ONR, SRC, Meta, Cisco, Analog Devices, etc.