True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices
Open accessAbstract
Scalable, low-power random number generator (RNG) blocks are essential for encryption in today's communication systems. To allow for true RNG, a system must display an inherently-random physical phenomenon, such as the timing of individual fluctuations in random telegraph noise or the random trapping/detrapping phenomena in dielectrics. In this work, a true RNG based on set variability in a resistive switching memory (RRAM) is demonstrated. The RNG relies on a single RRAM device, which is repeatedly programmed at a constant voltage close to the nominal set voltage. Due to the statistical variability of the set voltage, set transition takes place only in 50% of the applied pulses, thus resulting in a bimodal distribution of resistance. The bimodal distribution of analog resistance is finally converted into a 0/1 distribution of output voltage values through digital regeneration with a CMOS inverter.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Compared with previous TRNGs based on non-volatile memristors, the self-OFF-switching behavior in the diffusive memristor greatly reduces energy consumption because no RESET process is required.
“Compared with previous TRNGs based on non-volatile memristors30, 31, the self-OFF-switching behavior in the diffusive memristor greatly reduces the energy consumption since no RESET process is required.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [30]
Most previous approaches utilizing switching variations to build TRNGs have defined a threshold value for some switching characteristic (e.g., SET voltage or read current).
“Most previous approaches utilizing switching variations to build TRNGs have defined a threshold value for some switching characteristic (for example, SET voltage30 or read current39).”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [30]
The citation lists haven’t been fetched yet
References 24 · Cited by 147
- Authors
- Balatti, S., Ambrogio, S., Wang, Z., & Ielmini, D.
- Affiliations
- Politecnico di Milano (IT)
- Venue
- IEEE Journal on Emerging and Selected Topics in Circuits and Systems
- Published
- 2015-06
- Volume
- 5
- Issue
- 2
- Pages
- 214-221
- ISSN
- 2156-3357 (ISSN-L)
- 2156-3365
- OpenAlex
- W1982630937
- Cited by
- 147
- References
- 24
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 17 · 2023: 14 · 2022: 11 · 2021: 16 · 2020: 19 · 2019: 14 · 2018: 14 · 2017: 12 · 2016: 14
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Journal on Emerging and Selected Topics in Circuits and Systems (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- 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 › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- true random number generation, resistive random access memory, random telegraph noise, CMOS inverter, encryption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
