J. Semicond. > Volume 39 > Issue 12 > Article Number: 124009

Analysis and parameter extraction of memristive structures based on Strukov’s non-linear model

A. Avila Garcia , and L. Ortega Reyes

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Abstract: Diverse models have been proposed for explaining the electrical performance of memristive devices. In principle, the behavior of internal variables associated to each one could be extracted from experimental results. In a former work, thermally grown TiOx memristive structures were built and characterized to obtain the constitutive relationship (magnetic flux versus charge). The aim of this work is to continue that analysis by determining the microscopic parameters within the frame of a simple model. We use the already obtained memristance dependence of time and the basic expressions from the non-linear model proposed by Strukov et al. to compute the state-variable, the mobility of the doping species, the speed of the boundary between the doped and the undoped regions, the voltages and the electric fields on the distinct regions. The power dissipation and its time evolution are also presented. Moreover, a quite different window function from those formerly proposed, which was estimated from experimental data, is also determined. This information provides a straightforward picture of the ionic transport during one cycle of a square voltage waveform within the framework of this simple model. Finally, a quality factor is proposed as the key parameter for actual memristors viewed under the same model.

Key words: memristorsparameter extractionnon-linear modelexperimental window function

Abstract: Diverse models have been proposed for explaining the electrical performance of memristive devices. In principle, the behavior of internal variables associated to each one could be extracted from experimental results. In a former work, thermally grown TiOx memristive structures were built and characterized to obtain the constitutive relationship (magnetic flux versus charge). The aim of this work is to continue that analysis by determining the microscopic parameters within the frame of a simple model. We use the already obtained memristance dependence of time and the basic expressions from the non-linear model proposed by Strukov et al. to compute the state-variable, the mobility of the doping species, the speed of the boundary between the doped and the undoped regions, the voltages and the electric fields on the distinct regions. The power dissipation and its time evolution are also presented. Moreover, a quite different window function from those formerly proposed, which was estimated from experimental data, is also determined. This information provides a straightforward picture of the ionic transport during one cycle of a square voltage waveform within the framework of this simple model. Finally, a quality factor is proposed as the key parameter for actual memristors viewed under the same model.

Key words: memristorsparameter extractionnon-linear modelexperimental window function



References:

[1]

Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80

[2]

Lehtonen E, Laiho M. CNN using memristors for neighborhood connections. 2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA) IEEE, 2010: 1

[3]

Pickett M D, Strukov D B, Borghetti J L, et al. Switching dynamics in titanium dioxide memristive devices. J Appl Phys, 2009, 106(7): 074508

[4]

Kvatinsky S, Friedman E, Kolodny A, et al. TEAM: ThrEshold adaptive memristor model. IEEE Trans Circuits Syst I, 2013, 60(1): 211

[5]

Avila G A, Ortega R L, Romero-Paredes G, et al. Memristive structures based on thermally oxidized TiOx. JMSA, 2017, 3(6): 94

[6]

Messerschmitt F, Kubicek M, Schweiger S, et al. Memristor kinetics and diffusion characteristics for mixed anionic-electronic SrTiO 3-δ bits: the memristor-based cottrell analysis connecting material to device performance. Adv Funct Mater, 2014

[7]

Yang J J, Pickett M D, Li X, Ohlberg D A A, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotech, 2008, 3: 429

[8]

Yang J J, Miao F, Pickett M D, et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 2009, 20: 215201

[9]

Bersuker G, Gilmer D C, Veksler D, et al. Metal oxide resistive memory switching mechanism based on conductive filament properties. J Appl Phys, 2011, 110: 124518

[10]

Buckwell M, Montesi L, Mehonic A, et al. Microscopic and spectroscopic analysis of the nature of conductivity changes during resistive switching in silicon-rich silicon oxide. Phys Status Solidi C, 2015, 12(1/2): 211

[11]

Joglekar Y N, Wolf S J. The elusive memristor: properties of basic electrical circuits. Eur J Phys, 2009, 30: 661

[12]

Biolek Z, Biolek D, Biolkova V. Spice Model of memristor with nonlinear dopant drift. Radioengineering, 2009, 18: 210

[13]

Prodromakis T, Peh B P, Papavassiliou C, et al. A versatile memristor model with nonlinear dopant kinetics. IEEE Trans Electron Devices, 2011, 58(9): 3099

[14]

Kavehei O, Kim Y S, Iqbal A et al. The fourth element: insights into the memristor. IEEE International Conference on Communications, Circuits and Systems ICCCAS, 2009: 921

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McDonald N R, Pino R E, Rozwood P J et al. Analysis of dynamic linear and non-linear memristor device models for emerging neuromorphic computing hardware design. Neural Networks (IJCNN), The 2010 International Joint Conference, 2010: 18

[1]

Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80

[2]

Lehtonen E, Laiho M. CNN using memristors for neighborhood connections. 2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA) IEEE, 2010: 1

[3]

Pickett M D, Strukov D B, Borghetti J L, et al. Switching dynamics in titanium dioxide memristive devices. J Appl Phys, 2009, 106(7): 074508

[4]

Kvatinsky S, Friedman E, Kolodny A, et al. TEAM: ThrEshold adaptive memristor model. IEEE Trans Circuits Syst I, 2013, 60(1): 211

[5]

Avila G A, Ortega R L, Romero-Paredes G, et al. Memristive structures based on thermally oxidized TiOx. JMSA, 2017, 3(6): 94

[6]

Messerschmitt F, Kubicek M, Schweiger S, et al. Memristor kinetics and diffusion characteristics for mixed anionic-electronic SrTiO 3-δ bits: the memristor-based cottrell analysis connecting material to device performance. Adv Funct Mater, 2014

[7]

Yang J J, Pickett M D, Li X, Ohlberg D A A, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotech, 2008, 3: 429

[8]

Yang J J, Miao F, Pickett M D, et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 2009, 20: 215201

[9]

Bersuker G, Gilmer D C, Veksler D, et al. Metal oxide resistive memory switching mechanism based on conductive filament properties. J Appl Phys, 2011, 110: 124518

[10]

Buckwell M, Montesi L, Mehonic A, et al. Microscopic and spectroscopic analysis of the nature of conductivity changes during resistive switching in silicon-rich silicon oxide. Phys Status Solidi C, 2015, 12(1/2): 211

[11]

Joglekar Y N, Wolf S J. The elusive memristor: properties of basic electrical circuits. Eur J Phys, 2009, 30: 661

[12]

Biolek Z, Biolek D, Biolkova V. Spice Model of memristor with nonlinear dopant drift. Radioengineering, 2009, 18: 210

[13]

Prodromakis T, Peh B P, Papavassiliou C, et al. A versatile memristor model with nonlinear dopant kinetics. IEEE Trans Electron Devices, 2011, 58(9): 3099

[14]

Kavehei O, Kim Y S, Iqbal A et al. The fourth element: insights into the memristor. IEEE International Conference on Communications, Circuits and Systems ICCCAS, 2009: 921

[15]

McDonald N R, Pino R E, Rozwood P J et al. Analysis of dynamic linear and non-linear memristor device models for emerging neuromorphic computing hardware design. Neural Networks (IJCNN), The 2010 International Joint Conference, 2010: 18

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A A Garcia, L O Reyes, Analysis and parameter extraction of memristive structures based on Strukov’s non-linear model[J]. J. Semicond., 2018, 39(12): 124009. doi: 10.1088/1674-4926/39/12/124009.

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History

Manuscript received: 10 April 2018 Manuscript revised: 13 October 2018 Online: Accepted Manuscript: 05 November 2018 Uncorrected proof: 26 November 2018 Published: 13 December 2018

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