Experimental evaluation of the dynamic route map in the reset transition of memristive ReRAMs

Abstract In this paper, we analyze the reset transition in bipolar TiN/Ti/HfO2 (10 nm)/Al2O3(2 nm)/W ReRAM devices using a tool that allows studying the temporal behaviour of these devices. This tool, the Dynamic Route Map (DRM), provides information about the temporal evolution of the state variable that governs the behaviour of the device, thus allowing an increased insight into resistive switching processes. Here, we show that this DRM is a powerful tool, that may help explaining some non intuitive behaviours of memristors, like the difference in the reset voltage when the inputs are from different frequency or shape. Using this tool, this fact can be explained as a different trajectory on a unique surface defining the device. As a first step, we have used two different models, one based on a physical description, and another one based on the mathematical definition of memristor as a non linear relation between charge and flux. We check that similar DRM can be obtained from both models. Additionally, several series of set-reset transitions have been measured using voltage ramps of different slopes. From the measured transitions, the corresponding resistance has been extracted and, assuming conductive filaments (CF) as the switching mechanism, the corresponding CF radius has been calculated. Using these data, we show that explanations from the model are also supported when using experimental data, thus proving the validity of the approach.

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

[2]  L. Chua,et al.  Continuous and Differentiable Approximation of a TaO Memristor Model for Robust Numerical Simulations , 2017 .

[3]  Victor Erokhin,et al.  Organic Memristor Based Elements for Bio-inspired Computing , 2017 .

[4]  Miquel Roca,et al.  Design and implementation of passive memristor emulators using a charge-flux approach , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[5]  Christofer Toumazou,et al.  Two centuries of memristors. , 2012, Nature materials.

[6]  R. Picos,et al.  SPICE model for unipolar RRAM based on a flux-controlled memristor , 2015, 2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).

[7]  Leon O. Chua,et al.  If it’s pinched it’s a memristor , 2014 .

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

[9]  Leon O. Chua,et al.  Five non-volatile memristor enigmas solved , 2018, Applied Physics A.

[10]  Rodrigo Picos,et al.  A Simple Piecewise Model of Reset/Set Transitions in Bipolar ReRAM Memristive Devices , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Francisco Jimenez-Molinos,et al.  Exploring resistive switching‐based memristors in the charge–flux domain: A modeling approach , 2018, Int. J. Circuit Theory Appl..

[13]  Spyros Stathopoulos,et al.  Multibit memory operation of metal-oxide bi-layer memristors , 2017, Scientific Reports.

[14]  Encyklopädie der mathematischen Wissenschaften , 1902 .

[15]  J. B. Roldan,et al.  A physically based circuit model to account for variability in memristors with resistive switching operation , 2016, 2016 Conference on Design of Circuits and Integrated Systems (DCIS).

[16]  Hyongsuk Kim,et al.  Memristor-based multilevel memory , 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).

[17]  Rodrigo Picos,et al.  Emulating memristors in a digital environment using stochastic logic , 2018, 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST).

[18]  Francisco Jimenez-Molinos,et al.  A SPICE Compact Model for Unipolar RRAM Reset Process Analysis , 2015, IEEE Transactions on Electron Devices.

[19]  Mauricio Barahona,et al.  Window functions and sigmoidal behaviour of memristive systems , 2016, Int. J. Circuit Theory Appl..

[20]  D. Ielmini,et al.  Physical models of size-dependent nanofilament formation and rupture in NiO resistive switching memories , 2011, Nanotechnology.

[21]  Fernando Corinto,et al.  Flux–Charge Memristor Model for Phase Change Memory , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

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

[23]  Ronald Tetzlaff,et al.  A Simple Memristor Model for Neuromorphic ReRAM Devices , 2020, 2020 IEEE International Symposium on Circuits and Systems (ISCAS).

[24]  Rawan Naous,et al.  Stochasticity Modeling in Memristors , 2016, IEEE Transactions on Nanotechnology.

[25]  S. Cardoso,et al.  Bipolar resistive switching in Si/Ag nanostructures , 2017 .

[26]  Kyoung-Rok Cho,et al.  Memristor MOS Content Addressable Memory (MCAM): Hybrid Architecture for Future High Performance Search Engines , 2010, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[27]  Damien Querlioz,et al.  Spintronic Nanodevices for Bioinspired Computing , 2016, Proceedings of the IEEE.

[28]  Ali Khiat,et al.  Memristive integrative sensors for neuronal activity , 2015, ArXiv.

[29]  Francisco Jimenez-Molinos,et al.  Semiempirical Modeling of Reset Transitions in Unipolar Resistive-Switching Based Memristors , 2015, ArXiv.

[30]  Leon O. Chua,et al.  A Theoretical Approach to Memristor Devices , 2015, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[31]  J. B. Roldan,et al.  Exploring ReRAM-based memristors in the charge-flux domain, a modeling approach , 2015, 2015 International Conference on Memristive Systems (MEMRISYS).

[32]  Leon O. Chua,et al.  A Purely Digital Memristor Emulator based on a Flux-Charge Model , 2018, 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[33]  Ronald Tetzlaff,et al.  Memristors and Memristive Systems , 2014 .

[34]  Themistoklis Prodromakis,et al.  A Memristor SPICE Model Accounting for Synaptic Activity Dependence , 2015, PloS one.

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

[36]  Yusuf Leblebici,et al.  Memristive-Biosensors: A New Detection Method by Using Nanofabricated Memristors , 2012 .

[37]  H. Poincaré,et al.  Les méthodes nouvelles de la mécanique céleste , 1899 .

[38]  Fernando Corinto,et al.  A two-transistor non-ideal memristor emulator , 2016, 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST).

[39]  Rodrigo Picos,et al.  A Simple Quasi-Static Compact Model of Bipolar ReRAM Memristive Devices , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[40]  Hongyan Wang,et al.  An organic nonvolatile resistive switching memory device fabricated with natural pectin from fruit peel , 2017 .

[41]  Dalibor Biolek,et al.  Evaluation of memristor models for large crossbar structures , 2016, 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA).

[42]  Rodrigo Picos,et al.  A Non-Quasi Static Model for Reset Voltage Variation in Memristive Devices , 2019, 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[43]  G. Tallarida,et al.  Formation and disruption of conductive filaments in a HfO2/TiN structure , 2014, Nanotechnology.

[44]  Sung-Mo Kang,et al.  Compact Models for Memristors Based on Charge-Flux Constitutive Relationships , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[45]  Leon O. Chua,et al.  Everything You Wish to Know About Memristors But Are Afraid to Ask , 2015 .

[46]  N. Christoforou,et al.  State of the art of metal oxide memristor devices , 2016 .

[47]  Ali Khiat,et al.  A compact Verilog-A ReRAM switching model , 2017, ArXiv.