Design Considerations for Memristive Crossbar Physical Unclonable Functions

Abstract

Hardware security has emerged as a field concerned with issues such as integrated circuit (IC) counterfeiting, cloning, piracy, and reverse engineering. Physical unclonable functions (PUF) are hardware security primitives useful for mitigating such issues by providing hardware-specific fingerprints based on intrinsic process variations within individual IC implementations. As technology scaling progresses further into the nanometer region, emerging nanoelectronic technologies, such as memristors or RRAMs (resistive random-access memory), have become interesting options for emerging computing systems. In this article, using a comprehensive temperature dependent model of an HfO x (hafnium-oxide) memristor, based on experimental measurements, we explore the best region of operation for a memristive crossbar PUF (XbarPUF). The design considered also employs XORing and a column shuffling technique to improve reliability and resilience to machine learning attacks. We present a detailed analysis for the noise margin and discuss the scalability of the XbarPUF structure. Finally, we present results for estimates of area, power, and delay alongside security performance metrics to analyze the strengths and weaknesses of the XbarPUF. Our XbarPUF exhibits nearly ideal (near 50%) uniqueness, bit-aliasing and uniformity, good reliability of 90% and up (with 100% being ideal), a very small footprint, and low average power consumption ≈104μW.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 21, 2017
Source ID
10.1145/3094414

Entities

People

  • Garrett S. Rose
  • Harika Manem
  • Karsten Beckmann
  • Md. Badruddoja Majumder
  • Mesbah Uddin
  • Nathaniel C Cady
  • Zahiruddin Alamgir

Organizations

  • Air Force Office of Scientific Research
  • SUNY Polytechnic Institute
  • University of Tennessee

Tags

Readers

  • Cybersecurity.
  • Integrated Circuit Design and Technology.

Technology Areas

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