Recycled FPGA Detection Using Exhaustive LUT Path Delay Characterization and Voltage Scaling

Abstract

Field-programmable gate arrays (FPGAs) have been extensively used because of their lower nonrecurring engineering and design costs, instant availability and reduced visibility of failure, high performance, and power benefits. Reports indicate that previously used or recycled FPGAs are infiltrating the electronics' supply chain and making the security and reliability of the critical systems and networks vulnerable. Current recycled integrated circuit (IC) detection procedures include parametric, functional, and burn-in tests that require golden or reference data. Besides, they are time consuming, require expensive equipment, and do not focus on FPGAs. In this article, we propose two recycled FPGA detection methods based on supervised and unsupervised machine learning algorithms. We develop a sophisticated ring oscillator (RO) design to exploit the degradation of lookup tables (LUTs) and use them in the proposed methods. In the supervised method, a one-class classifier is trained with RO frequencies, kurtosis, and skewness data obtained from unused FPGAs, which differentiates unused and aged FPGAs. The unsupervised method uses $k$-means clustering and Silhouette value analysis to detect suspect recycled components with very little (if any) golden information. In addition, we introduce a voltage scaling-assisted RO frequency measurement technique that improves the classification. The proposed methods are examined for Spartan-3A and Spartan-6 FPGAs, and the result shows that both methods are effective in detecting recycled FPGAs, which experience accelerated aging for at least 12 h equivalent to 70 days in real-time age.