The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
On the physical properties of memristive, memcapacitive and meminductive systems
Open accessAbstract
We discuss the physical properties of realistic memristive, memcapacitive and meminductive systems. In particular, by employing the well-known theory of response functions and microscopic derivations, we show that resistors, capacitors and inductors with memory emerge naturally in the response of systems-especially those of nanoscale dimensions-subjected to external perturbations. As a consequence, since memristances, memcapacitances and meminductances are simply response functions, they are not necessarily finite. This means that, unlike what has always been argued in some literature, diverging and non-crossing input-output curves of all these memory elements are physically possible in both quantum and classical regimes. For similar reasons, it is not surprising to find memcapacitances and meminductances that acquire negative values at certain times during dynamics, while the passivity criterion of memristive systems imposes always a non-negative value on the resistance at any given time. We finally show that ideal memristors, namely those whose state depends only on the charge that flows through them (or on the history of the voltage), are subject to very strict physical conditions and are unable to protect their memory state against the unavoidable fluctuations, and therefore are susceptible to a stochastic catastrophe. Similar considerations apply to ideal memcapacitors and meminductors.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
““memristors” understood merely as nonlinear resistors with memory have been described by Kubo theory in the 1950s14.”
From The Missing Memristor has Not been Found (2015) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Our perspective rests on the fact that memristive, memcapacitive, and meminductive devices and systems are simply generalizations of the traditional resistors, capacitors, and inductors to the case of memory response [6].”
From Comment on 'If it's pinched it's a memristor' (2019) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 5×
“By ideal memristors we mean those whose resistance depends only on the charge that flows in the system (or on the history of the voltage) [1, 3].”
BackgroundMethodInfluentialKubo, R. (1957) · Journal of the Physical Society of Japan · doi:10.1143/jpsj.12.570 · Cited by 9,441 · cited 10×
“function [19] (whether treating the electron problem quantum-”
BackgroundMethodInfluentialIrreversibility and heat generation in the computing process
Landauer, R. (1961) · IBM Journal of Research and Development · doi:10.1147/rd.53.0183 · Cited by 4,980
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923 · cited 1×
“From experiments, however, we know that the switching actually occurs as soon as the applied voltage or current exceeds its threshold value [31, 32, 23].”
BackgroundHandbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences
Gardiner, C. W. (1985) · Springer series in synergetics · doi:10.1007/978-3-662-02452-2 · Cited by 3,748
Thermal Agitation of Electric Charge in Conductors
Nyquist, H. (1928) · Physical Review · doi:10.1103/physrev.32.110 · Cited by 3,264 · cited 1×
“In particular, the (intrinsic) thermal agitation of electrons inside any voltage-controlled memristor is responsible for the well-known Johnson–Nyquist noise [28, 29] (voltage fluctuations).”
BackgroundCan programming be liberated from the von Neumann style?
Backus, J. (1978) · Communications of the ACM · doi:10.1145/359576.359579 · Cited by 2,607 · cited 1×
“Non-traditional computing represents an important research direction with the aim of solving the ‘von Neumann bottleneck’ that plagues our computer architectures [11].”
BackgroundMemristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604
‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature · doi:10.1038/nature08940 · Cited by 1,929
Research works printed but not linked: 3
- Ashcroft N W, 1976, Solid State Physics
- Gilbert T L, 1955, Phys. Rev., 100, 1243
- Bigi I I, 1999, CP Violation
Cited by
Sorted by citations
Items 101–132 of 132
Switching Synchronization and Metastable States in 1D Memristive Networks
Slipko, V. A., & Pershin, Y. V. (2019) · Springer eBooks · doi:10.1007/978-3-319-76375-0_33
Self-citationMemory Effect in the Charge Transport in Strongly Disordered Antimony Films
Орлова, Н. Н., Bozhko, S. I., & Deviatov, E. V. (2020) · Journal of Experimental and Theoretical Physics · doi:10.1134/s1063776120040111
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0008
The Physics of computing and computing with Physics
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0001
Application to combinatorial optimization problems
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0009
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0013
Universal MemComputing machine
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0005
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0002
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0011
Application to Machine Learning and Quantum Mechanics
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0010
Dynamical systems picture of computing
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0003
Self-organizing gates and circuits
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0007
Approach to equilibrium and the ordered state of DMMs
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0012
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.003.0006
Device Applications of Solution Processed MIR Semiconductor Nanocrystal Thin Films
Cryer, M. (2019) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.17138687
Ventra, M. D., & Pershin, Y. V. (2023) · SpringerBriefs in physics · doi:10.1007/978-3-031-25625-7_1
Self-citationPhysical Constraints for Ideal Memelements
Ventra, M. D., & Pershin, Y. V. (2023) · SpringerBriefs in physics · doi:10.1007/978-3-031-25625-7_3
Self-citationGonzales, C., & Gonzales, C. (2023) · doi:10.6035/14104.2023.804973
Chen, E. T., & Thornton, J. T. (2025) · ECS Sensors Plus · doi:10.1149/2754-2726/adbe8c
“41 – 43 The i-V curve at 10 kHz shows a capacitive nature without a memristive loop.”
A Simple Memristor with Passive Devices
Tama, R., & Summatta, C. (2025) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon64081.2025.10987890
28 302Nano- und Molekularelektronik
Hartmann, U. (2025) · De Gruyter eBooks · doi:10.1515/9783486855449-006
Zlochevskiy, I. I., & Zav’yalov, D. (2025) · Izvestiya of Saratov University Physics · doi:10.18500/1817-3020-2025-25-4-449-459
Taha, G. M., Ibrahim, N. A., Kamel, S. M., Khalil, R., & Baseer, R. A. (2026) · Journal of Applied Polymer Science · doi:10.1002/app.70376
Isah, A. (2026) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.70333
Memory effects in pulsed optomechanical systems
Tapia-Maureira, H., He, B., Ventra, M. D., & Norambuena, A. (2026) · Physical Review Applied · doi:10.1103/mxbn-15ry
“(3) reduces to y ( t ) = f ( s , x, t ) x ( t ) , consistent with classical memristive systems, if x ( t ) is the voltage, y ( t ) the current, and f ( · ) the conductance [30, 45].”
Local learning with highly analog memory devices
Bennett, C. H. (2018) · theses.fr (ABES) · doi:10.70675/e49026a5z4027z44f2za795z67ee03a38150
“In fact, this simplified formluation is very similar to Pershin and DiVentra’s formulation of memristive, memcapactive, and mem-inductive systems from condensed matter theory (Kubo response theory) [52].”
MethodRelaxor ferroelectrics for neuromorphic computing
Cheng, L. (2024) · theses.fr (ABES) · doi:10.70675/fbc0cc50z8b92z47f4z9ee2z91e531f5d3a0
Metadata
- Authors
- Di Ventra, M., & Pershin, Y. V.
- Affiliations
- University of South Carolina (US)
- Venue
- Nanotechnology
- Publisher
- IOP Publishing
- Published
- 2013-05-24
- Volume
- 24
- Issue
- 25
- Pages
- 255201
- ISSN
- 0957-4484 (ISSN-L)
- 1361-6528
- OpenAlex
- W2135517727
- Semantic Scholar
- c36f81401d2c03a40a32c218b3d5f1e048af075d
- Cited by
- 130
- References
- 29
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 7 · 2023: 8 · 2022: 26 · 2021: 8 · 2020: 7 · 2019: 7 · 2018: 13 · 2017: 13 · 2016: 9 · 2015: 9 · 2014: 5 · 2013: 2
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nanotechnology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0802830, 1202383)
- University of California, San Diego
- Division of Materials Research
- Division of Electrical, Communications and Cyber Systems (ECCS-1202383)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Advanced Thermodynamics and Statistical Mechanics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- memristors, memcapacitor, meminductor, memory elements, response functions, nanosystems, input-output curves
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
