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.
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““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]
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“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]
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Cites
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“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].”
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“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].”
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Self-citationColloquium: Heat flow and thermoelectricity in atomic and molecular junctions
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“Moreover, memcapacitors and meminductors can store energy in the electric and magnetic field [12], respectively, in addition to information, therefore opening new venues in the technologically important area of energy storage, distribution and manipulation [13].”
BackgroundMemory effects in complex materials and nanoscale systems
Pershin, Y. V., & Di Ventra, M. (2011) · Advances In Physics · doi:10.1080/00018732.2010.544961 · Cited by 823
Self-citationElectrical Transport in Nanoscale Systems
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“These elements, when combined in complex circuits, can perform logic [4] and non-traditional computing operations [5, 6, 7, 8, 9, 10] in a massively-parallel fashion and on the same physical platform where storing occurs, a striking resemblance with similar functionalities of our brain.”
BackgroundAn Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse
Alibart, F., Pleutin, S., Guérin, D., Novembre, C., Lenfant, S., Lmimouni, K., Gamrat, C., & Vuillaume, D. (2009) · Advanced Functional Materials · doi:10.1002/adfm.200901335 · Cited by 392 · cited 1×
“These elements, when combined in complex circuits, can perform logic [4] and non-traditional computing operations [5, 6, 7, 8, 9, 10] in a massively-parallel fashion and on the same physical platform where storing occurs, a striking resemblance with similar functionalities of our brain.”
BackgroundBeyond von Neumann—logic operations in passive crossbar arrays alongside memory operations
Linn, E., Rosezin, R., Tappertzhofen, S., Böttger, U., & Waser, R. M. (2012) · Nanotechnology · doi:10.1088/0957-4484/23/30/305205 · Cited by 374
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Self-citationNeuromorphic, Digital, and Quantum Computation With Memory Circuit Elements
Pershin, Y. V., & Ventra, M. D. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2166369 · Cited by 266 · cited 1×
“computing, such as the one that can be performed with memory elements [7, 9].”
BackgroundSelf-citationSolving mazes with memristors: A massively parallel approach
Pershin, Y. V., & Ventra, M. D. (2011) · Physical Review E · doi:10.1103/physreve.84.046703 · Cited by 163
Self-citationMemory materials: a unifying description
Ventra, M. D., & Pershin, Y. V. (2011) · Materials Today · doi:10.1016/s1369-7021(11)70299-1 · Cited by 105
Self-citationSolid-state memcapacitive system with negative and diverging capacitance
Martinez-Rincon, J., Di Ventra, M., & Pershin, Y. V. (2010) · Physical Review B · doi:10.1103/physrevb.81.195430 · Cited by 99 · cited 2×
“An example of negative capacitance is given in figure 1, see also [26].”
BackgroundSelf-citationPutting Memory Into Circuit Elements: Memristors, Memcapacitors, and Meminductors [Point of View]
Ventra, M. D., Pershin, Y. V., & Chua, L. O. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2022882 · Cited by 89
Self-citationIncompleteness of the Landauer formula for electronic transport
Vignale, G., & Ventra, M. D. (2009) · Physical Review B · doi:10.1103/physrevb.79.014201 · Cited by 69 · cited 1×
“similar derivation can be found in [20])”
Fundamental Issues and Problems in the Realization of MemristorsPreprint
Meuffels, P., & Soni, R. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.7319 · Cited by 51 · cited 2×
“To understand the reasoning behind the conclusion in [30], let us consider the switching of a memristor at constant temperature and pressure.”
BackgroundLagrange formalism of memory circuit elements: Classical and quantum formulations
Cohen, G., Pershin, Y. V., & Ventra, M. D. (2012) · Physical Review B · doi:10.1103/physrevb.85.165428 · Cited by 48 · cited 1×
“Moreover, memcapacitors and meminductors can store energy in the electric and magnetic field [12], respectively, in addition to information, therefore opening new venues in the technologically important area of energy storage, distribution and manipulation [13].”
BackgroundSelf-citationComplex dynamics and scale invariance of one-dimensional memristive networks
Pershin, Y. V., Slipko, V. A., & Ventra, M. D. (2013) · Physical Review E · doi:10.1103/physreve.87.022116 · Cited by 31
Self-citation
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Cited by
Sorted by citations
Nanoionic memristive phenomena in metal oxides: the valence change mechanism
Dittmann, R., Menzel, S., & Waser, R. M. (2021) · Advances In Physics · doi:10.1080/00018732.2022.2084006 · Cited by 208
Universal Memcomputing Machines
Traversa, F. L., & Ventra, M. D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2391182 · Cited by 150
“The whole concept of memcomputing can be realized in practice by utilizing physical properties of many materials and systems which show a certain degree of time non-locality (memory) in their response functions at particular frequencies and strengths of the inputs [14]–[16].”
BackgroundExperimental photonic quantum memristor
Spagnolo, M., Morris, J., Piacentini, S., Antesberger, M., Massa, F., Crespi, A., Ceccarelli, F., Osellame, R., & Walther, P. J. (2022) · Nature Photonics · doi:10.1038/s41566-022-00973-5 · Cited by 141
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Vongehr, S., & Meng, X. (2015) · Scientific Reports · doi:10.1038/srep11657 · Cited by 131
A Novel Window Function for Memristor Model With Application in Programming Analog Circuits
Zha, J., Huang, H., & Liu, Y. (2015) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2015.2505959 · Cited by 124
“On the other hand, in [17]–[19], some significant limitations of ideal memristor models were discussed.”
Background
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
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