Methods to Assess Sensitivity Limits of Second Order Effects Measurements

Abstract

Sensitivity limits of a specific second order effects(2OE) testing method were studied with respect to the detection of a specific set of circuit modifications using flash-enabled FPGAs as a testbed. The 2OE method tested was an expanded variant of Sandia's Power Spectrum Analysis (PSA) and the circuit modifications to be studied were hardware Trojans of different size and with controlled changes to physical layout on the test FPGA. The focus of this work was data analysis comparing the utility of traditional Hotelling t expn 2 and corresponding p-values as separability metrics to confusion matrix analysis derived from machine learning classifiers based on logistic regression. The goal of the analyses was to optimize data collection for 2OE features that improve separability while clarifying the limitations of the method with respect to identifying the specific risk defined by the part/problem pair. The optimized 2OE methods easily identified physical differences in FPGAs (associated with manufacture changes related to lot dates) and certain hardware Trojan varieties were clearly identified as separable while others proved more difficult for the 2OE features analyzed. This work provides a path for2OE methods optimization and quantification of utility to mitigate specific risks.

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Document Details

Document Type
Technical Report
Publication Date
Mar 29, 2021
Accession Number
AD1137023

Entities

People

  • Brendan Foran
  • Carl T. Boone
  • Dmitry Veksler
  • Garret Chan
  • Salam Zantout
  • Sean C. Stuart
  • Vikram Rao

Organizations

  • The Aerospace Corporation

Tags

Communities of Interest

  • Advanced Electronics
  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Data Analysis
  • Data Mining
  • Data Reduction
  • Data Sets
  • Dimensionality Reduction
  • Field Programmable Gate Arrays
  • Frequency
  • Identification
  • Information Science
  • Machine Learning
  • Power Spectra
  • Square Waves
  • Supervised Machine Learning
  • Test Beds
  • Test Equipment
  • Test Methods

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms