This course was developed as a MEST Micro-Certificate. MEST Micro-Certificates offer short, self-paced learning opportunities through focused training modules and webinars on specialized technical topics. Participants who successfully complete the program requirements earn a certificate of completion.
These programs are designed for U.S. citizens, including Department of War and other government personnel, government-affiliated employees, industry professionals, college students, and faculty.
A counterfeit Integrated Circuit (IC) is an unauthorized or fake replica of a genuine chip, often produced or relabeled to mimic authentic components. These counterfeit ICs can exhibit poor reliability, degraded performance, or hidden malicious functionality, posing serious risks to consumer safety, national security, and product integrity. They may fail prematurely or compromise system operations, especially in critical applications like aerospace, healthcare, or defense. Detection techniques include electrical testing, X-ray imaging, scanning electron microscopy (SEM), spectroscopy, and authentication through physical unclonable functions (PUFs) or supply chain tracking. Robust detection ensures trustworthiness and resilience in electronic systems. This course offers practical learning to implement counterfeit FPGA detection using circuit-based analysis and machine learning.
By taking this certificate course, students will learn what counterfeit IC is, what circuit components can be added with regular circuit to detect counterfeit later in life cycle, and how to detect it.
This micro-certificate course is organized into a set of units described below.
Unit 1: Recycled FPGA Detection: There are two lectures in this unit. One Introduces RO-based odometer and how to integrate it to detect recycled FPGA. Another one shows how machine learning can be used to along with RO to detect recycled FPGA more precisely.
Unit 2: Research Series on Counterfeit Detection: It contains two lectures on recent developments on counterfeit IC detection using test techniques and resilient approaches.
Format: Online, asynchronous
Platform: nanoHUB Learning Management System
Estimated Time: 15 hours
Level: Advanced Undergraduate/Graduate Student
Prerequisites: A background in basic FPGA design will be helpful
Instructor: Professor Mark M. Tehranipoor, University of Florida
Course Objectives:
Explain counterfeit IC concepts
Identify circuit components suitable for counterfeit detection
Design an RO-based odometer circuit for recycled FPGA detection
Apply machine-learning algorithms to FPGA counterfeit detection
Compare recent research series on counterfeit detection techniques
Request Course Enrollment
To enroll in this course, you must have a nanoHUB account. Create one here: https://nanohub.org/register/
Request membership in the MEST group in nanoHUB at https://nanohub.org/groups/mest