A 28NM Integrated True Random Number Generator Harvesting Entropy from MRAM

Yang, Kaiyuan; Dong, Qing; Wang, Zhehong; Shih, Yu-Jen; Chih, Yu-Der; Chang, Jonathan; Blaauw, David; Svlvester, Dennis

doi:10.1109/vlsic.2018.8502431

Cited by 46 publications

(21 citation statements)

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“…The weaknesses of the thermal noise-based designs are its sensitivity to noise-based attacks, compromising the randomness of the output, as well as issues with the design complexity required for post-processing and tuning of the system 10,[23][24][25][26] . Many other entropy sources have been used for TRNGs, such as the timing of time dependent dielectric breakdown (TDDB) 27 , Photonics [28][29][30][31] , and current variation or switching time of emerging technologies such as ReRAM [32][33][34] . TDDB-based designs suffer from high complexity of circuit design and processing circuits which limit their use in low-area, low-power applications.…”

mentioning

confidence: 99%

Random-telegraph-noise-enabled true random number generator for hardware security

Brown

Zhang

Zhou

et al. 2020

Sci Rep

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The future security of Internet of Things is a key concern in the cyber-security field. One of the key issues is the ability to generate random numbers with strict power and area constrains. “True Random Number Generators” have been presented as a potential solution to this problem but improvements in output bit rate, power consumption, and design complexity must be made. In this work we present a novel and experimentally verified “True Random Number Generator” that uses exclusively conventional CMOS technology as well as offering key improvements over previous designs in complexity, output bitrate, and power consumption. It uses the inherent randomness of telegraph noise in the channel current of a single CMOS transistor as an entropy source. For the first time multi-level and abnormal telegraph noise can be utilised, which greatly reduces device selectivity and offers much greater bitrates. The design is verified using a breadboard and FPGA proof of concept circuit and passes all 15 of the NIST randomness tests without any need for post-processing of the generated bitstream. The design also shows resilience against machine learning attacks performed by the LSTM neural network.

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mentioning

confidence: 99%

Random-telegraph-noise-enabled true random number generator for hardware security

Brown

Zhang

Zhou

et al. 2020

Sci Rep

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“…The physical nature of this method needs either peripheral circuits or a sophisticated anti-bias design, both of which are relatively expensive and complex to manufacture. Hence, Yang et al proposed another TRNG design based on the fact that MTJ switching time itself can be a practical entropy source, and the proposal was verified with a commercial MRAM die [42].…”

Section: Switching-time Dependent Stochasticitymentioning

confidence: 99%

“…Up till now, four main types of strategies have been proposed to realize MTJ-TRNGs with sufficient randomness, fast speed, low energy, and compact area. As described in the last section, the TRNG performances in most of these spintronic devices are tested under simulation except [31,42] where the circuitboard of the MRAM die was used for verification. Bearing this fact in mind, the comparison of the best performances of each strategy is summarized in Table 1.…”

Section: Comparison and Perspectivementioning

confidence: 99%

An Overview of Spintronic True Random Number Generator

Tang²,

Zhao³

et al. 2021

Front. Phys.

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A True Random Number Generator is an essential component in data encryption, hardware security, physical unclonable functions, and statistical analyses. Conventional CMOS devices usually exploit the thermal noise or jitter to generate randomness, which suffers from high energy consumption, slow bit generating rate, large area, and over-complicated circuit. In this mini review, we introduce the novel physical randomness generating mechanism based on the stochastic switching behavior of magnetic tunnel junctions. As compared to CMOS technologies, the random number generator based on spintronic devices can have many inherent advantages, such as simpler structure, compact area, higher throughput, and better energy-efficiency. Here, we review and compare various existing schemes at the device and circuit levels to achieve high performance magnetic tunnel junctions based on a True Random Number Generator. Future research trends and challenges are also discussed to stimulate more works in this area.

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“…Another way to produce random numbers is by using the random switching behavior of Magnetic Tunnel Junctions under low write current [22].…”

Section: ) Mrammentioning

confidence: 99%

Entropy and Randomness: From Analogic to Quantum World

Simion

2020

IEEE Access

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This work is dedicated to the construction and evaluation of random number generators used in cryptography. The critical element on which the security of information is based is the cryptographic key (usually a binary sequence). In order to be resistant to brute force attacks it is necessary that it be made up of random variables with a certain degree of randomness and independence. Formally, this comes back to generate the cryptographic key through the systems which ensures a certain minimum level of entropy. The observer has access to a sample, of a certain size, and based on it he will estimate the minimum value of the entropy, in the situations in which the variables resulting from the measurement process are independent. In the situation where these variables are not independent, complex mathematical procedures also allow estimation of the minimum entropy. This article is a review of how mathematical entropy can be estimated and evaluated, of the construction mode (from technologies based on analogue procedures: thermal noise in a transistor to modern procedures: quantum devices), as well as to evaluate the security of binary sequence generators used for generating cryptographic keys or critical security parameters related to new technologies based on quantum principles. The techniques and methods used to generate binary random values as well as the methods of statistical and informational validation (Shannon entropy) are exemplified in this paper.

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A 28NM Integrated True Random Number Generator Harvesting Entropy from MRAM

Cited by 46 publications

References 1 publication

Random-telegraph-noise-enabled true random number generator for hardware security

Random-telegraph-noise-enabled true random number generator for hardware security

An Overview of Spintronic True Random Number Generator

Entropy and Randomness: From Analogic to Quantum World

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