Quantitative measure of hysteresis for memristors through explicit dynamics

We introduce a mathematical framework for the analysis of the input–output dynamics of externally driven memristors. We show that, under general assumptions, their dynamics comply with a Bernoulli differential equation and hence can be nonlinearly transformed into a formally solvable linear equation. The Bernoulli formalism, which applies to both charge- and flux-controlled memristors when either current or voltage driven, can, in some cases, lead to expressions of the output of the device as an explicit function of the input. We apply our framework to obtain analytical solutions of the i–v characteristics of the recently proposed model of the Hewlett–Packard memristor under three different drives without the need for numerical simulations. Our explicit solutions allow us to identify a dimensionless lumped parameter that combines device-specific parameters with properties of the input drive. This parameter governs the memristive behaviour of the device and, consequently, the amount of hysteresis in the i–v. We proceed further by defining formally a quantitative measure for the hysteresis of the device, for which we obtain explicit formulas in terms of the aforementioned parameter, and we discuss the applicability of the analysis for the design and analysis of memristor devices.

[1]  Matthew D. Pickett,et al.  Two‐ and Three‐Terminal Resistive Switches: Nanometer‐Scale Memristors and Memistors , 2011 .

[2]  Guanrong Chen,et al.  Generating Multiscroll Chaotic Attractors: Theories, Methods and Applications , 2006 .

[3]  Tae Hee Kim,et al.  Nanoparticle assemblies as memristors. , 2009, Nano letters.

[4]  A. J. Payne,et al.  A Bernoulli Cell-Based Investigation of the Non-Linear Dynamics in Log-Domain Structures , 2000 .

[5]  J. Yang,et al.  Switching dynamics in titanium dioxide memristive devices , 2009 .

[6]  Stephen J. Wolf,et al.  The elusive memristor: properties of basic electrical circuits , 2008, 0807.3994.

[7]  J. Yang,et al.  Electrical transport and thermometry of electroformed titanium dioxide memristive switches , 2009 .

[8]  R. Williams,et al.  Exponential ionic drift: fast switching and low volatility of thin-film memristors , 2009 .

[9]  Yuriy V. Pershin,et al.  Memory effects in complex materials and nanoscale systems , 2010, 1011.3053.

[10]  T. Apostol Mathematical Analysis , 1957 .

[11]  Warren Robinett,et al.  Memristor-CMOS hybrid integrated circuits for reconfigurable logic. , 2009, Nano letters.

[12]  Xinghuo Yu,et al.  Design and analysis of multiscroll chaotic attractors from saturated function series , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  L. Chua Memristor-The missing circuit element , 1971 .

[14]  R. Williams,et al.  How We Found The Missing Memristor , 2008, IEEE Spectrum.

[15]  Ronald Tetzlaff,et al.  Abel Dynamics of Titanium Dioxide Memristor Based on Nonlinear Ionic Drift Model , 2011 .

[16]  Bernabé Linares-Barranco,et al.  On neuromorphic spiking architectures for asynchronous STDP memristive systems , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[17]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[18]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[19]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[20]  Massimiliano Di Ventra,et al.  Memristive model of amoeba learning. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Chagaan Baatar,et al.  Cellular Nanoscale Sensory Wave Computing , 2009 .

[22]  Mauricio Barahona,et al.  Device Properties of Bernoulli Memristors , 2012, Proceedings of the IEEE.

[23]  Y. Pershin,et al.  Spin Memristive Systems: Spin Memory Effects in Semiconductor Spintronics , 2008, 0806.2151.

[24]  Massimiliano Di Ventra,et al.  Phase-transition driven memristive system , 2009, 0901.0899.

[25]  E.M. Drakakis,et al.  Memristors and Bernoulli dynamics , 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).

[26]  L.O. Chua,et al.  Memristive devices and systems , 1976, Proceedings of the IEEE.

[27]  R. Williams,et al.  Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior. , 2009, Small.

[28]  Leon O. Chua,et al.  Device modeling via nonlinear circuit elements , 1980 .

[29]  Paul F. Byrd,et al.  Handbook of elliptic integrals for engineers and scientists , 1971 .

[30]  B. Widrow,et al.  Birth, Life, and Death in Microelectronic Systems , 1961, IRE Transactions on Military Electronics.

[31]  David M. Auslander,et al.  The Memristor: A New Bond Graph Element , 1972 .

[32]  A. Polyanin,et al.  Handbook of Exact Solutions for Ordinary Differential Equations , 1995 .

[33]  M. Sahimi,et al.  Electric currents in networks of interconnected memristors. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  George Oster,et al.  A note on memristors , 1974 .