Modelling of nanostructured TiO2-based memristors

The fourth fundamental circuit element memristor completes the missing link between charge and magnetic flux. It consists of the function of the resistor as well as memory in nonlinear fashion. The property of the memristor depends on the magnitude and direction of applied potential. This unique property makes it the primitive building block for many applications such as resistive memories, soft computing, neuromorphic systems and chaotic circuits etc. In this paper we report TiO2-based nanostructured memristor modelling. The present memristor model is constructed in MATLAB environment with consideration of the linear drift model of memristor. The result obtained from the linear drift model is well matched with earlier reported results by other research groups.

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

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

[3]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[4]  G. Snider,et al.  Self-organized computation with unreliable, memristive nanodevices , 2007 .

[5]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[6]  A. Sawa.  Resistive switching in transition metal oxides , 2008 .

[7]  Leon O. Chua,et al.  Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors , 2009, Proceedings of the IEEE.

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

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

[10]  Bharathwaj Muthuswamy,et al.  Memristor-Based Chaotic Circuits , 2009 .

[11]  Dalibor Biolek,et al.  SPICE Model of Memristor with Nonlinear Dopant Drift , 2009 .

[12]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

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

[14]  E. Lehtonen,et al.  CNN using memristors for neighborhood connections , 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).

[15]  Bharathwaj Muthuswamy,et al.  Implementing Memristor Based Chaotic Circuits , 2010, Int. J. Bifurc. Chaos.

[16]  György Cserey,et al.  Macromodeling of the Memristor in SPICE , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  Farnood Merrikh-Bayat,et al.  Bottleneck of using single memristor as a synapse and its solution , 2010, ArXiv.

[18]  Leon O. Chua.  Resistance switching memories are memristors , 2011 .

[19]  Karel Zaplatilek,et al.  Memristor modeling in MATLAB® &Simulink® , 2011 .

[20]  Farnood Merrikh-Bayat,et al.  Programming of memristor crossbars by using genetic algorithm , 2011, WCIT.

[21]  Zhigang Zeng,et al.  Exponential synchronization of memristor-based recurrent neural networks with time delays , 2011, Neurocomputing.

[22]  황철성,et al.  Nanofilamentary resistive switching in binary oxide system , 2011 .

[23]  Zhigang Zeng,et al.  Synchronization control of a class of memristor-based recurrent neural networks , 2012, Inf. Sci..

[24]  Uri C. Weiser,et al.  TEAM: ThrEshold Adaptive Memristor Model , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.