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Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering

Air Force Institute of Technology

Theses/Dissertations

2016

RF-DNA

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Integrated Circuit Wear-Out Prediction And Recycling Detection Using Radio-Frequency Distinct Native Attribute Features, Randall D. Deppensmith Dec 2016

Integrated Circuit Wear-Out Prediction And Recycling Detection Using Radio-Frequency Distinct Native Attribute Features, Randall D. Deppensmith

Theses and Dissertations

Radio Frequency Distinct Native Attribute (RF-DNA) has shown promise for detecting differences in Integrated Circuits(IC) using features extracted from a devices Unintentional Radio Emissions (URE). This ability of RF-DNA relies upon process variation imparted to a semiconductor device during manufacturing. However, internal components in modern ICs electronically age and wear out over their operational lifetime. RF-DNA techniques are adopted from prior work and applied to MSP430 URE to address the following research goals: 1) Does device wear-out impact RF-DNA device discriminability?, 2) Can device age be continuously estimated by monitoring changes in RF-DNA features?, and 3) Can device age state …


Comparison Of Radio Frequency Distinct Native Attribute And Matched Filtering Techniques For Device Discrimination And Operation Identification, Barron D. Stone Mar 2016

Comparison Of Radio Frequency Distinct Native Attribute And Matched Filtering Techniques For Device Discrimination And Operation Identification, Barron D. Stone

Theses and Dissertations

The research presented here provides a comparison of classification, verification, and computational time for three techniques used to analyze Unintentional Radio- Frequency (RF) Emissions (URE) from semiconductor devices for the purposes of device discrimination and operation identification. URE from ten MSP430F5529 16-bit microcontrollers were analyzed using: 1) RF Distinct Native Attribute (RFDNA) fingerprints paired with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification, 2) RF-DNA fingerprints paired with Generalized Relevance Learning Vector Quantized-Improved (GRLVQI) classification, and 3) Time Domain (TD) signals paired with matched filtering. These techniques were considered for potential applications to detect counterfeit/Trojan hardware infiltrating supply chains and to defend …