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Systematic Correlation and Cell Neighborhood Analysis of SRAM PUF for Robust and Unique Key Generation

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Abstract

A physical unclonable function (PUF) is a structure that produces a unique response, with an issued challenge (input), which can be used as an identifier or a cryptographic key. SRAM PUFs create unique responses upon power up as certain SRAM cells output a “1” or “0” with high probability due to uncontrollable process variations. A current challenge in SRAM PUFs is their sensitivity to temperature and voltage variations as well as aging. It is always challenging to make SRAM PUFs reliable and unique with algorithms that isolate stable and uncorrelated bits quickly with minimal testing (enrollment). In this paper, we explore the selection of stable and uncorrelated bits through enrollment under different conditions (temperature and voltage) and also by exploiting previously undiscovered interactions between neighboring SRAM cells. We propose neighbor influenced cell selection algorithm (NICSA) with the help of metrics that analyze the impact of each neighboring cell and each enrollment condition. The proposed NICSA helps to identify the “best” cells and conditions for stable bit selection. Besides reliability, SRAM PUF can be less unique due to systematic correlation among chips. We study the systematic correlation between SRAMs power-up values to find the uncorrelated cells among chips for better uniqueness. We have analyzed data from 5 ISSI, 3 IDT, and 3 Cypress SRAMs and our metrics identify the best neighborhood size (16 stable neighbors) and best enrollment condition pair high temperature, high voltage, and low temperature for NICSA.

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Acknowledgments

This work was supported in part by the National Science Foundation (NSF) under grant CNS-1561023 and by the Semiconductor Research Corporation (SRC) under contracts 2572 and 2648.

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Authors and Affiliations

  1. University of Alabama in Huntsville, Huntsville, AL, USA

    M. Tauhidur Rahman

  2. University of Florida, Gainesville, FL, USA

    Zimu Guo, Jackson Carroll, Domenic Forte & Mark Tehranipoor

  3. University of Connecticut, Storrs, CT, USA

    Alison Hosey

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  1. M. Tauhidur Rahman
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  2. Alison Hosey
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Correspondence to M. Tauhidur Rahman.

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Rahman, M.T., Hosey, A., Guo, Z. et al. Systematic Correlation and Cell Neighborhood Analysis of SRAM PUF for Robust and Unique Key Generation. J Hardw Syst Secur 1, 137–155 (2017). https://doi.org/10.1007/s41635-017-0012-3

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  • DOI: https://doi.org/10.1007/s41635-017-0012-3

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