Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor
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Abstract
Abstract The intrinsic stochasticity of the memristor can be used to generate true random numbers, essential for non-decryptable hardware-based security devices. Here we propose a novel and advanced method to generate true random numbers utilizing the stochastic oscillation behavior of a NbOx mott memristor, exhibiting self-clocking, fast and variation tolerant characteristics. The random number generation rate of the device can be at least 40 kbs-1, which is the fastest record compared with previous volatile memristor-based TRNG devices. Also, its dimensionless operating principle provides high tolerance against both ambient temperature variation and device-to-device variation, enabling robust security hardware applicable in harsh environments.
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tRNGs such as RRAM or MRAM are widely studied given their inherent switching stochasticity as an entropy source, which relies on material-induced variations.
“For example, tRNGs such as RRAM or MRAM, which are not Si-based complementary metal-oxide semiconductor (CMOS) devices, are widely studied given their inherent switching stochasticity as an entropy source, which relies on material-induced variations (11–18).”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [18]
The Introduction (page not specified) describes TRNG research using stochastic switching in magnetic and resistive memory devices, including variation in switching voltages or programmed states. It does not establish that the randomness in both RRAM- and MRAM-based TRNGs specifically relies on material-induced variations; the paper identifies thermal fluctuations as the randomness source for its own device. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTheir lack of compatibility with CMOS fabrication standards makes it difficult to apply them directly to the current CMOS logic platform used for mass production.
“Moreover, their lack of compatibility with CMOS fabrication standards makes it difficult to apply them directly to the current CMOS logic platform used for mass production.”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [18]
The Introduction discusses earlier true-random-number-generator approaches, and Methods > Device fabrication describes the paper’s NbOx device. Neither addresses whether the devices lack CMOS fabrication compatibility or whether that prevents their direct use in mass-produced CMOS logic. Searches for CMOS compatibility, integration, and fabrication standards found no passage making that point; page numbers were not available in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThese tRNGs require a few entropy extraction circuits, each of which consumes power at the milliwatt level.
“However, these tRNGs require a few entropy extraction circuits, each of which consumes power at the milliwatt level.”
From Cryptographic transistor for true random number generator with low power consumption (2024) · cites this paper as [18]
In Results and discussion, ‘A self-clocking TRNG device’ (page unavailable), the paper says previous TRNGs required an external clock generator consuming about several hundred mW. It does not say that they require several entropy-extraction circuits or give milliwatt-level power consumption for each such circuit (searched: ‘entropy extraction circuits’ and related power terms). (LLM, conf=0.74)
Experimental — accuracy not yet measured
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Low‐Power Optoelectronic Hybrid Perovskite‐Based Memristor for Ternary Hardware Security Monitoring
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Stochastic Photoresponse‐Driven Perovskite TRNGs for Secure Encryption Systems
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Self-citationSnS2 memtransistor-based Lorenz chaotic system for true random number generation
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Poly 3-methylthiophene based memristor device for neuromorphic computing
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Fully Resistive In‐Memory Cryptographic Engine with Dual‐Polarity Memristive Crossbar Array
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Cryptographic triboelectric random number generator with gentle breezes of an entropy source
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Enhancing Johnson-Nyquist noise for high-performance Mott memristor-based oscillatory TRNG
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Over 1011-fold variability for a base-12 PUF using particle-damaged graphene
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Role of heat in post-silicon electronics
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Sequential Bayesian Inference and Monte-Carlo Sampling Using Memristor Stochasticity
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- Authors
- Kim, G., In, J. H., Kim, Y. S., Rhee, H., Park, W., Song, H., Park, J., & Kim, K. M.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Kim, Kyung Min
- ORCID
- Venue
- Nature Communications
- Published
- 2021-05-18
- Volume
- 12
- Issue
- 1
- Pages
- 2906-2906
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8131590
- OpenAlex
- W3112007195
- Cited by
- 126
- References
- 41
- Citations by year
- 2026: 23 · 2025: 27 · 2024: 32 · 2023: 29 · 2022: 11 · 2021: 3
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Trade, Industry and Energy (20003655, 20003789)
- Korea Advanced Institute of Science and Technology
- Korea Semiconductor Research Consortium
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Physical Unclonable Functions (PUFs) and Hardware Security
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Keywords
- true random number generation, memristors, Mott transition, variation tolerance, temperature variation, device-to-device variation, hardware security, hardware-based cryptography
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