Integrated Circuit Wear out Prediction and Recycling Detection using Radio Frequency Distinct Native Attribute Features

Abstract

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 (e.g., new vs. used) be reliably estimated? Conclusions include: 1) device wear-out does impact RF-DNA, with up to a 16 change in discriminability over the range of accelerated ages considered, 2) continuous(hour-by-hour) age estimation was most challenging and generally not supported, and 3) binary new vs. used age estimation was successful with 78.7 to 99.9 average discriminability for all device-age combinations considered.

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Document Details

Document Type
Technical Report
Publication Date
Dec 22, 2016
Accession Number
AD1031986

Entities

People

  • Randall D Deppensmith

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Air Force
  • Complementary Metal-Oxide Semiconductors
  • Counterfeit Parts
  • Department Of Defense
  • Digital Circuits
  • Electronic Components
  • Electronics Industry
  • Electronics Laboratories
  • Integrated Circuits
  • Logic Gates
  • Machine Learning
  • Modules (Electronics)
  • Semiconductor Devices
  • Semiconductor Manufacturing
  • Semiconductors
  • Supervised Machine Learning
  • Very Large Scale Integration

Readers

  • Integrated Circuit Design and Technology.
  • Radio communications and signal processing.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • Microelectronics