TRNG Based on Multiple Entropy Sources Using CTDSM

True random number generators (TRNGs) extract entropy from physical processes to generate statistically independent, non-reproducible bitstreams with cryptographic-grade randomness. Silicon-based TRNGs utilize random noise in the circuit as a source of entropy to emulate an equiprobable Bernoulli random process. This paper proposes using a Continuous Time Delta-Sigma Modulator (CTDSM) to extract entropy from multiple uncorrelated noise sources for TRNG design. A CTDSM can be made entropy-rich by leveraging its negative feedback mechanism and non-idealities due to clock jitter and the quantizer’s metastability. Simulation results of the proposed design, implemented using TSMC 65 nm technology, successfully demonstrate cryptographic-grade randomness by passing all tests recommended in NIST SP 800-22.

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Publication Details

Published
2024-11-07
DOI
https://doi.org/10.1109/apccas62602.2024.10808739
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

TRNG Based on Multiple Entropy Sources Using CTDSM

Naveen Kadayinti, Apurv Pandey
Advanced Algorithms and Applications
article

TRNG Based on Multiple Entropy Sources Using CTDSM

Naveen Kadayinti, Apurv Pandey
article en

Abstract

True random number generators (TRNGs) extract entropy from physical processes to generate statistically independent, non-reproducible bitstreams with cryptographic-grade randomness. Silicon-based TRNGs utilize random noise in the circuit as a source of entropy to emulate an equiprobable Bernoulli random process. This paper proposes using a Continuous Time Delta-Sigma Modulator (CTDSM) to extract entropy from multiple uncorrelated noise sources for TRNG design. A CTDSM can be made entropy-rich by leveraging its negative feedback mechanism and non-idealities due to clock jitter and the quantizer’s metastability. Simulation results of the proposed design, implemented using TSMC 65 nm technology, successfully demonstrate cryptographic-grade randomness by passing all tests recommended in NIST SP 800-22.

Indian Institute of Technology Dharwad (IN)
Science and Engineering Research Board
Affordable and clean energy
Openalex Percentile: Top 71%
Advanced Algorithms and Applications
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