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Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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A Lightweight and High Yield Complementary Metal-Oxide Semiconductor True Random Number Generator with Lightweight

Chi Trung Ngo1, Hyun Woo Ko1, Ji Woo Choi1

  • 1School of Electrical Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

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|December 17, 2024
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Summary

This study presents a new true random number generator (TRNG) using a ring oscillator and a lightweight Photon hash function post-processing. This approach significantly improves the number of TRNGs passing NIST randomness tests while reducing power and area.

Keywords:
IoTlightweight cryptographyphotontrue random number generator

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Area of Science:

  • Integrated Circuit Design
  • Hardware Security
  • Cryptography

Background:

  • True Random Number Generators (TRNGs) are crucial for secure cryptographic applications.
  • Existing TRNG architectures often face challenges in meeting stringent randomness test requirements.
  • Post-processing is essential to enhance the quality of random bits generated by TRNGs.

Purpose of the Study:

  • To propose a novel TRNG architecture based on a customized ring oscillator (RO).
  • To investigate the effectiveness of a lightweight Photon hash function for post-processing TRNG outputs.
  • To evaluate the performance, area, and power consumption of the integrated TRNG design.

Main Methods:

  • A customized current-starved ring oscillator (RO) was designed to generate jitter noise.
  • A wave converter was employed to derive random outputs from the RO's jitter.
  • A lightweight Photon hash function was implemented as a post-processing algorithm for the TRNG.
  • The TRNG was fabricated using a 28 nm CMOS process for performance evaluation.

Main Results:

  • Initial TRNG samples showed limitations in passing NIST SP 800-22 randomness tests.
  • The application of the Photon hash function post-processing significantly improved the pass rate by 50%.
  • The integrated design achieved a throughput of 0.0142 Mbps with a power consumption of 31.12 mW and occupied 16,498 µm².

Conclusions:

  • The proposed TRNG architecture combined with lightweight Photon post-processing offers a viable solution for generating high-quality random numbers.
  • This approach demonstrates a substantial reduction in area (5x) and power consumption (65%) compared to conventional DRBG post-processing.
  • The findings highlight the potential of lightweight cryptographic primitives for efficient hardware TRNG design.