A 0.43pJ/bit true random number generator

Kuan, Ting-Kuei; Chiang, Yu-Hsuan; Liu, Shen-Iuan

doi:10.1109/asscc.2014.7008853

Cited by 19 publications

(20 citation statements)

References 7 publications

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“…There are numerous spatiotemporal phenomena in hardware, specially at deep-micron or nanometer scales, that have been used as sources of randomness, and chaotic systems [3][4][5][6]. In CMOS, randomness and jitter in oscillators output [2,7], jitter in digital systems [8,9], metastability in common mode comparators as well as well-known sampling uncertainty of D flip-flops [4], metastability in latch circuits [10], themal noise [1], and edge racing in even-stage inverter rings [3] are just a couple of examples. Oscillator-based and metastable TRNGs have the simplest and largest circuits, receptively with oscillator-based TRNGs suffering from reported poor randomness [11].…”

Section: Introductionmentioning

confidence: 99%

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Kim

Nili

Truong

et al. 2019

IEEE Trans. Circuits Syst. I

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Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solidstate physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.

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Section: Introductionmentioning

confidence: 99%

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Kim

Nili

Truong

et al. 2019

IEEE Trans. Circuits Syst. I

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“…In this class of TRNGs, the source of randomness is obtained from the measurement of intrinsically random physical phenomena including radioactive decay, photon detection, and various sources of electronic noise in semiconductor devices (e.g., thermal, diffusion, shot, avalanche, flicker, and generation/recombination noises) [95,145,[145][146][147][148][149][150][151]. In the same class of TRNGs, we can also include other approaches proposed in literature, using antennas, sensors, and transducers to retrieve stochastic signals from different sources like, for example, lasers, noisy images taken with digital cameras, the Sun radiation, or the atmosphere dynamics [152][153][154].…”

Section: Circuits To Measure Other Stochastic Physical Processesmentioning

confidence: 99%

“…Furthermore, in these TRNGs, the stochastic signal at the source can have equivalent amplitudes as lower as few tens of microvolts, and a special care has to be taken in the design to make the device robust with respect to circuit mismatches, electromagnetic couplings with the neighbor circuitry, unforeseen aging effects, temperature, and supply-voltage variations. A TRNG exploiting electronic noise and metastability, generating one random bit DOUT each clock period (phase (a) and phase (b)) [145]. Schematic representation of the core structure of a TRNG exploiting the measurement of a stochastic physical process.…”

Section: Circuits To Measure Other Stochastic Physical Processesmentioning

confidence: 99%

“…For the sake of an example, in Figure 17, the core subcircuits of a TRNG exploiting both electronic noise and metastability are shown [145]. Furthermore, Figure 18 shows the block diagram of a TRNG exploiting a mixture of the three sources of randomness mentioned in Section 6: electronic noise, chaos, and oscillators sampling [93].…”

Section: Circuits To Measure Other Stochastic Physical Processesmentioning

confidence: 99%

“…Figure 17. A TRNG exploiting electronic noise and metastability, generating one random bit DOUT each clock period (phase (a) and phase (b)) [145]. The VAR blocks are digitally controlled networks of varactors, to finely adjust the statistical biasing of the generated random sequences.…”

Section: Circuits To Measure Other Stochastic Physical Processesmentioning

confidence: 99%

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Embedded electronic circuits for cryptography, hardware security and true random number generation: an overview

Acosta

Addabbo

Tena-Sánchez

2016

Circuit Theory & Apps

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We provide an overview of selected crypto-hardware devices, with a special reference to the lightweight electronic implementation of encryption/decryption schemes, hash functions, and true random number generators. In detail, we discuss the hardware implementation of the chief algorithms used in private-key cryptography, public-key cryptography, and hash functions, discussing some important security issues in electronic crypto-devices, related to side-channel attacks (SCAs), fault injection attacks, and the corresponding design countermeasures that can be taken. Finally, we present an overview about the hardware implementation of true random number generators, discussing the chief electronic sources of randomness and the types of post-processing techniques used to improve the statistical characteristics of the generated random sequences.A. J. ACOSTA, T. ADDABBO AND E. TENA-SÁNCHEZ resource consumption. This matter sets the point for the lightweight cryptography, that is, the subfield of cryptography aiming to provide solutions tailored for resource-constrained devices.According to Elsevier Scopus, the largest database of research peer-reviewed literature, since 2010, about 40k documents are returned if searching the keyword 'cryptography' [6]. A huge subset of these papers deals with conceptual, algorithmic, software, hardware solutions that may be taken into account in lightweight cryptography. In the face of such a vast literature, in this work, we provide a brief overview of selected crypto-hardware devices, with a special reference to the lightweight electronic implementation of encryption/decryption schemes, hash functions, and true random number generators (TRNGs).This paper is organized as in the following. In Section 2, we introduce some terminology and present an overview of the hardware implementation of the chief algorithms used in private-key cryptography, public-key cryptography (PKC), and hash functions. In Sections 3 and 4, we introduce some important security concerns about electronic crypto-devices, discussing SCAs, fault injection attacks, and the corresponding countermeasures that can be taken in the hardware design. Finally, Sections 5-8 are devised to provide an overview about the electronic implementation of TRNGs. In detail, in Sections 5 and 6, we discuss about security flaws, statistical defects, and predictability of TRNGs, presenting the chief sources of randomness used nowadays for their hardware implementation. In Sections 7 and 8, we discuss an overview of the different post-processing techniques aimed to improve the statistical characteristics of the generated random sequences and the evaluation methods used to assess the reliability of cryptographic TRNGs. Conclusion and References close the paper. CRYPTOGRAPHIC ALGORITHMSCryptographic algorithms aim to convert secret data into an unreadable code for non authorized persons, protecting secret information from theft or alteration and also enabling authentication. For better understanding, in the next sections, we define the following...

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A true random number generator with high bit rate and low energy efficiency

Cheng

Zhang

Zhu

et al. 2023

Circuit Theory & Apps

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Summary In this paper, a novel feedback architecture of true random number generator based on tetrahedral ring oscillator is proposed. The random raw bits are fed back to produce control voltages, which control the transmission gates in ring oscillators. Since random fluctuations of the control voltages increase the phase jitter of the oscillator, this architecture improves the bit rate and randomness of random sequence. Besides, a post‐processor is designed to enhance the randomness further. The circuit is implemented in TSMC 40‐nm complementary metal oxide semiconductor (CMOS) process, and it takes up an area of 85 × 51 μm. Measurement results show that the random number sequences pass the statistical randomness tests, with a low power consumption of 67 μW, high speed of 45 Mbps, and low energy efficiency of 1.48 pJ/bit.

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A 0.43pJ/bit true random number generator

Cited by 19 publications

References 7 publications

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Embedded electronic circuits for cryptography, hardware security and true random number generation: an overview

A true random number generator with high bit rate and low energy efficiency

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