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Design and Evaluation of Resistive-based Security Primitives (Physically Unclonable Function & True Random Number Generator)


Keywords: Memristor, PUF, TRNG

Abstract: The Physically Unclonable Functions (PUFs) exploit intrinsic manufacturing variability introduced in a device during the fabrication process to generate a signature, unique to each single device. In order to guarantee its security, the generated secret key must be unique from device to device (unclonable), and, for a same device, it must be robust with respect to aging and environmental variations (reproducible).
True Random Number Generators (TRNGs) are used to generate random numbers from a physical process, rather than a computer program. They are implemented by taking advantage of thermal noise or other quantum phenomena and are expected to generate random bits with very high entropy and zero correlation. An on-chip TRNG design should occupy small area, give high bit rate, and have low power consumption, while assuring un-biased bit streams with high entropy per bit and low (no) correlation among them.
The rapid development of low power, high density, high performance SoCs has pushed the embedded memories to their limits and opened the field to the development of emerging memory technologies.
The Resistive Random Access Memory (ReRAM) has emerged as a promising choice for embedded memories due to its reduced read/write latency and high CMOS integration capability. Inner properties of ReRAMs make them suitable for the implementation of basic security primitives such Physically Unclonable Functions (PUFs) and True Random Number Generators (TRNGs). This thesis will explore solutions which exploit (i) the high variability affecting the electrical resistance of the resistive device to build a robust, unclonable and unpredictable PUF, and (ii) the stochastic nature of the write operation in the resistive device to generate randomly distributed numbers.


Thesis director: Giorgio DI NATALE (TIMA - AMfoRS)
Thesis supervisor: Elena-Ioana VATAJELU (TIMA - AMfoRS)
Thesis started on: Oct. 2020
Thesis defence: September 30, 2024
Doctoral School: EEATS

Submitted on January 7, 2022

Updated on July 22, 2024