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<p>Genuine stochastic information represented by true random numbers (TRNs) is essential for entropy-associated applications such as cryptography and energy-based computing. They demand specialized hardware called true random number generators (TRNGs) capable of rapid, energy-efficient TRN generation. In this study, we enhance Johnson-Nyquist noise to demonstrate the fastest, most energy-efficient memristor-based TRNG. The TRNG comprises an NbO<sub>x</sub>-based negative resistance oscillator, a T flip-flop for digitalization, and a heater as a noise source. The heater enhances Johnson-Nyquist noise, achieving a TRNG speed of 100 kbit/s, 2.5× faster than without the heater. Furthermore, we propose a TRNG array that utilizes heat across the array for energy-efficient, parallel TRN generation. The 128-sized TRNG array is expected to achieve 0.65 μJ/bit at 1.28 Mbit/s, demonstrating significant improvements in speed and efficiency. By focusing on noise engineering rather than conventional material- or circuit-based methods, our approach enables broader applications in entropy-based computing.</p>

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Enhancing Johnson-Nyquist noise for high-performance Mott memristor-based oscillatory TRNG

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Gwangmin Kim,
  • Jae Hyun In,
  • Hakseung Rhee,
  • Woojoon Park,
  • Hanchan Song,
  • Kyung Min Kim

摘要

Genuine stochastic information represented by true random numbers (TRNs) is essential for entropy-associated applications such as cryptography and energy-based computing. They demand specialized hardware called true random number generators (TRNGs) capable of rapid, energy-efficient TRN generation. In this study, we enhance Johnson-Nyquist noise to demonstrate the fastest, most energy-efficient memristor-based TRNG. The TRNG comprises an NbOx-based negative resistance oscillator, a T flip-flop for digitalization, and a heater as a noise source. The heater enhances Johnson-Nyquist noise, achieving a TRNG speed of 100 kbit/s, 2.5× faster than without the heater. Furthermore, we propose a TRNG array that utilizes heat across the array for energy-efficient, parallel TRN generation. The 128-sized TRNG array is expected to achieve 0.65 μJ/bit at 1.28 Mbit/s, demonstrating significant improvements in speed and efficiency. By focusing on noise engineering rather than conventional material- or circuit-based methods, our approach enables broader applications in entropy-based computing.