Simulation of multilevel switching in electrochemical metallization memory cells

Menzel, Stephan; Böttger, U.; Waser, Rainer

doi:10.1063/1.3673239

Cited by 162 publications

(143 citation statements)

References 21 publications

(34 reference statements)

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“…At time t nuc the filamentary growth starts. For the simulation of the filament growth we extend our dynamic switching model 42 to cover the nonlinear ionic current transport at high electric fields.…”

Section: Theory and A Simulation Modelmentioning

confidence: 99%

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Switching kinetics of electrochemical metallization memory cells

Menzel

Tappertzhofen

Waser

et al. 2013

Phys. Chem. Chem. Phys.

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175

161

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The strongly nonlinear switching kinetics of electrochemical metallization memory (ECM) cells are investigated using an advanced 1D simulation model. It is based on the electrochemical growth and dissolution of a Ag or Cu filament within a solid thin film and accounts for nucleation effects, charge transfer, and cation drift. The model predictions are consistent with experimental switching results of a time range of 12 orders of magnitude obtained from silver iodide (AgI) based ECM cells. By analyzing the simulation results the electrochemical processes limiting the switching kinetics are revealed. This study provides new insights into the understanding of the limiting electrochemical processes determining the switching kinetics of ECM cells.

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Section: Theory and A Simulation Modelmentioning

confidence: 99%

“…scaling limitations, 39 programming kinetics 37,40,41 and multilevel switching. 9,39,42,43 In the context of commercial applications, the switching speed is crucial for device operation and the same rate limiting factors e.g. interfacial processes, nucleation, transport etc.…”

Section: Introductionmentioning

confidence: 99%

Switching kinetics of electrochemical metallization memory cells

Menzel

Tappertzhofen

Waser

et al. 2013

Phys. Chem. Chem. Phys.

Self Cite

175

161

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“…Alternatively, some models are too computationally intensive, e.g., due to necessity of solving coupled differential equations [8, 16, 23-25, 27, 31, 32, 40] or running molecular dynamic simulations [9,35,38]. Several compact (SPICE) models, which are the most suitable for largescale simulations, have been also very recently proposed for valence change [1,17,36,39,45] and electrochemical resistive switching devices [29,50]. Unfortunately, models for at least the former type of devices are still not sufficiently accurate and require further improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Because resistive switching devices have memory, the current i(t 0 ) should also depend on the history of applied voltage bias before time t 0 , or, equivalently, on the memory state variable vector w at time t 0 . Such memory state variables represent certain physical parameters, which are changing upon switching the device, e.g., radius and/or length of switching filament [13,29,36,50]. A very convenient method for capturing the complete I-V behavior is to use a set of two equations describing memristive system [11].…”

Section: Introductionmentioning

confidence: 99%

Phenomenological modeling of memristive devices

2015

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We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model predicts most accurately I-V relation at small non-disturbing electrical stresses, which is often the most critical range of operation for circuit modeling. While the choice of fitting functions is motivated by the switching and conduction mechanisms of particular titanium dioxide devices, the proposed modeling methodology is general enough to be applied to different types of memory devices which feature smooth non-abrupt resistance switching.

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“…Nonvolatile readout is performed at V ≪ V th 18 . Nanofilament formation in solid state electrolytes 4,7,16,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] are commonly attributed to oxidation, electric-field-driven ionic migration and reduction, involving a positively charged active electrode supplying the mobile ions and a negatively charged inert electrode, where reduction can take place initializing the filament growth. At opposite polarity, the filament is dissolved.…”

mentioning

confidence: 99%

Asymmetry-Induced Resistive Switching in Ag-Ag2S-Ag Memristors Enabling a Simplified Atomic-Scale Memory Design

Halbritter¹

2016

Proceedings of the 2nd World Congress on New Technologies

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Prevailing models of resistive switching arising from electrochemical formation of conducting filaments across solid state ionic conductors commonly attribute the observed polarity of the voltage-biased switching to the sequence of the active and inert electrodes confining the resistive switching memory cell. Here we demonstrate stable switching behaviour in metallic Ag-Ag 2 S-Ag nanojunctions at room temperature exhibiting similar characteristics. Our experimental results and numerical simulations reveal that the polarity of the switchings is solely determined by the geometrical asymmetry of the electrode surfaces. By the lithographical design of a proof of principle device we demonstrate the merits of simplified fabrication of atomic-scale, robust planar Ag 2 S memory cells.As ongoing miniaturization reaches the fundamental limitations of silicon-based complementary metal-oxide-semiconductor (CMOS) technology, the demand for alternative material platforms delivering faster, smaller, yet highly integrable logical and memory units is increasing. Self-assembled nanostructures exhibiting tunable electrical properties are primary candidates. Conducting nanofilaments formed or destroyed by reversible solid state electrochemical reactions in ionic conducting media situated between metallic electrodes have demonstrated reproducible logical and non-volatile resistance switching random access memory (ReRAM) operations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] . The resistance of such a two-terminal memristor 17 , is altered above a threshold bias (V th ) of a few hundred mV. Nonvolatile readout is performed at V ≪ V th 18 . Nanofilament formation in solid state electrolytes 4,7,16,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] are commonly attributed to oxidation, electric-field-driven ionic migration and reduction, involving a positively charged active electrode supplying the mobile ions and a negatively charged inert electrode, where reduction can take place initializing the filament growth. At opposite polarity, the filament is dissolved. While offering extremely large R OFF /R ON switching ratios, devices operated in this regime can only perform at reduced switching speeds due to their fundamental RC limitations. Once such a metallic nanofilament bridging the two electrodes is fully developed, smaller but orders of magnitude faster resistance changes can be observed as the filament diameter is modulated 15,18,[36][37][38][39][40] . Our present study focuses on the latter regime.The central question of our Letter concerns the role of the inert electrode and consequently, the polarity of the set/reset transitions. Depending on the ionic mobility and redox rates, nucleation and subsequent filament formation have been observed either at the inert or at the active electrode surfaces by in-situ methods 23,31,32,41 . After an initial nucleation phase further reduction takes place directly along the growing filament consisting of the elemental metal of the active electrode. Thus, we antic...

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Simulation of multilevel switching in electrochemical metallization memory cells

Cited by 162 publications

References 21 publications

Switching kinetics of electrochemical metallization memory cells

Switching kinetics of electrochemical metallization memory cells

Phenomenological modeling of memristive devices

Asymmetry-Induced Resistive Switching in Ag-Ag2S-Ag Memristors Enabling a Simplified Atomic-Scale Memory Design

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