A Fully Digital True Random Number Generator With Entropy Source Based in Frequency Collapse

Serrano, Ronaldo; Duran, Ckristian; Hoang, Trong-Thuc; Sarmiento, Marco; Nguyen, Khai-Duy; Tsukamoto, Akira; Suzaki, Kuniyasu; Pham, Cong‐Kha

doi:10.1109/access.2021.3099534

Cited by 20 publications

(23 citation statements)

References 15 publications

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“…This work is a continuation of the work presented in [32], when the problems and solutions to implement the TRNG based on frequency collapse in Field Programmable Gate Array (FPGA) is shown. The main contribution of the current work is the demonstration of the robustness and healthy of the TRNG based on the same physical phenomena in an ASIC implementation.…”

Section: Introductionmentioning

confidence: 92%

“…Second, the jitter is measured using a physical phenomenon caused for the jitter accumulation in a multi-modal Ring Oscillator (RO). The entropy is digitized using the time of a frequency collapse [32]- [36]. However, the mismatch can affect the frequency collapse, generating dependencies used for Physical Unclonable Function (PUF) applications [37].…”

Section: Introductionmentioning

confidence: 99%

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A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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True Random Number Generator (TRNG) is used in many applications, generally for generating random cryptography keys. In this way, the trust of the cryptography system depends on the quality of the random numbers generated. However, the entropy fluctuations produced by external perturbations generate some false positives in the random sequence. These false positives can generate a disastrous scenario, depending on the application. This work presents the results of different tests to demonstrate the robustness and health of the TRNG based on frequency collapse. The TRNG passed all entropy tests provided for NIST SP800-90B and AIS31. The entropy test denotes a 0.9789 minimum entropy normalized and 7.998 Shannon entropy. In addition, the TRNG passes the health tests provided for NIST SP800-90B. The health test shows a number of identical values I v = 0%, I v−1 < 0.004% and a maximum cutoff value M C v = 10 with LM C v = 13 in the repetition count and adaptive proportion tests, respectively. The implementation passed all the statistical tests provided for NIST SP800-22 and AIS20. Besides, the implementation passes the different tests with Process, Voltage, and Temperature (PVT) variations. The TRNG is implemented in a 0.18µm General Purpose (GP) CMOS technology, occupying 25600µm 2 with four entropy sources. Finally, the implementation presents a 7.3 until 9.2-Mb/s of bit rate, 0.56 until 1.88-mW of power consumption, and 77.2 until 204.3-pJ/bit of energy per bit using an entropy source with 16 and 2 delay stages, respectively.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

et al. 2022

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“…This is further verified by the results of NIST SP 800-22 statistical test suite results given in Table S1. To benchmark the performance of the TRNG demonstrated in this work, the proposed device is compared with other TRNGs in the literature, [3][4][5][6]9,11,23,[30][31][32][33][34] which employ different physical processes in various platforms for random number generation, the results of which are given in Table S2.…”

Section: Randomness Analysis Using Statistical Measuresmentioning

confidence: 99%

“…Quantum TRNGs rely on the probabilistic nature of the quantum events for randomness, while classical TRNGs such as TRNGs based on classical chaos rely on the indeterminism caused by finite measurement accuracy and the high sensitivity to initial conditions as the source of randomness. Utilizing these processes, TRNGs based on chaotic lasers, 3 multi-modal ring oscillators, 4 random Raman fiber lasers, 5 memristors, [6][7][8] amplified spontaneous emission, 9 photon arrival time measurements, 10 superparamagnetic tunnel junctions, 11 carbon nanotube transistors, 12 etc. have been demonstrated recently.…”

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confidence: 99%

A High-Quality Entropy Source Using van der Waals Heterojunction for True Random Number Generation

Abraham,

Watanabe,

Taniguchi

et al. 2022

Preprint

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Generators of random sequences used in high-end applications such as cryptography rely on entropy sources for their indeterminism. Physical processes governed by the laws of quantum mechanics are excellent sources of entropy available in nature.However, extracting enough entropy from such systems for generating truly random sequences is challenging while maintaining the feasibility of the extraction procedure for real-world applications. Here, we present a compact and an all-electronic van der Waals (vdW) heterostructure-based device capable of detecting discrete charge fluctuations for extracting entropy from physical processes and use it for the generation 1

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“…The secure system typically implements a standalone TRNG, exploiting the different physical phenomena to obtain random numbers. For example, entropy sources based on a chaotic system [20]- [22], charge trapping in FinFet [23], the frequency collapse in multimodal ring oscillator [24]- [26], and metastability in latches [27]- [30]. However, the new approaches unified the TRNG with the memories used in the system, reducing the area overhead.…”

Section: Introductionmentioning

confidence: 99%