An All-Digital Edge Racing True Random Number Generator Robust Against PVT Variations

Yang, Kaiyuan; Blaauw, David; Sylvester, Dennis

doi:10.1109/jssc.2016.2519383

Cited by 110 publications

(14 citation statements)

References 17 publications

(31 reference statements)

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“…The key experimental finding in their work is that when writing "1"s into MTJs with a low current, two or three times smaller than the nominal values, the switching time follows a skewed distribution (mean value of 28-56 ns). Such skewed distribution might be a result of both systematic delay mismatch and random jitter, which is similar to CMOS oscillation collapse time [43]. The stochasticity of the MTJ switching time allows it to record time itself as the random bit.…”

Section: Switching-time Dependent Stochasticitymentioning

confidence: 97%

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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Section: Switching-time Dependent Stochasticitymentioning

confidence: 97%

An Overview of Spintronic True Random Number Generator

Tang²,

Zhao³

et al. 2021

Front. Phys.

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“…Muitos TRNGs são baseados em RO (osciladores em anel), cujo funcionamento é susceptível a variações de processos de fabricação, temperatura, alimentação. Assim, visando manter a segurança do gerador enquanto em funcionamento, alguns trabalhos abordam a implementação de um sistema de supervisão junto ao RNG, como em [10] e [11].…”

Section: Lista De Tabelasunclassified

“…Outra implementação digital é a utilização de jitter em osciladores em anel (RO) como fonte de entropia. Alguns métodos de geração de números aleatórios baseados em jitter em RO [21]: medem a diferença de frequência de dois osciladores em paralelo, ou, o número de ciclos que o RO leva para colapsar [10].…”

Section: Trngunclassified

“…O oscilador em anel, chamado Transition Effect Ring Oscillator (TERO), usado por Yang et al [10] é apresentado na figura 4 contendo um número par de estágios inversores. Figura 6 -TRNG baseado em RO com host processor.…”

Section: Trngunclassified

“…Da mesma forma que na equação (3), cada estágio multiplica o valor em sua entrada por dez e a parte inteira da representação em ponto-fixo é descartada. Buscando usar o mínimo de recursos de hardware possível, especialmente multiplicadores embarcados, a multiplicação por dez foi implementada por duas operações de deslocamento e uma soma, como apresentado na equação (10). Essa implementação pode ser feita uma vez que a equação (11) é verdadeira.…”

Section: Arquitetura Fpgaunclassified

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Gerador de números aleatórios baseado no >i<k>/i<-mapa logístico

Kotaki¹

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Hardware and Information Security Primitives Based on 2D Materials and Devices

Wali

Das

2023

Advanced Materials

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Hardware security is a major concern for the entire semiconductor ecosystem that accounts for billions of dollars in annual losses. Similarly, information security is a critical need for the rapidly proliferating edge devices that continuously collect and communicate a massive volume of data. While silicon‐based complementary metal‐oxide‐semiconductor technology offers security solutions, these are largely inadequate, inefficient, and often inconclusive, as well as resource intensive in time, energy, and cost, leading to tremendous room for innovation in this field. Furthermore, silicon‐based security primitives have shown vulnerability to machine learning (ML) attacks. In recent years, 2D materials such as graphene and transition metal dichalcogenides have been intensely explored to mitigate these security challenges. In this review, 2D‐materials‐based hardware security solutions such as camouflaging, true random number generation, watermarking, anticounterfeiting, physically unclonable functions, and logic locking of integrated circuits (ICs) are summarized with accompanying discussion on their reliability and resilience to ML attacks. In addition, the role of native defects in 2D materials in developing high entropy hardware security primitives is also examined. Finally, the existing challenges for 2D materials, which must be overcome for large‐scale deployment of 2D ICs to meet the security needs of the semiconductor industry, are discussed.

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An All-Digital Edge Racing True Random Number Generator Robust Against PVT Variations

Cited by 110 publications

References 17 publications

An Overview of Spintronic True Random Number Generator

An Overview of Spintronic True Random Number Generator

Gerador de números aleatórios baseado no >i<k>/i<-mapa logístico

Hardware and Information Security Primitives Based on 2D Materials and Devices

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