A compact model for selectors based on metal doped electrolyte

A selector device that demonstrates high nonlinearity and low switching voltages was fabricated using HfOx as a solid electrolyte doped with Ag electrodes. The electronic conductance of the volatile conductive filaments responsible for the switching was studied under both static and dynamic conditions. A compact model is developed from this study that describes the physical processes of the formation and rupture of the Ag filament(s). A dynamic capacitance model is used to fit the transient current traces under different voltage bias, which enables the extraction of parameters associated with the various parasitic components in the device.

[1]  Peng Lin,et al.  Fully memristive neural networks for pattern classification with unsupervised learning , 2018 .

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

[3]  Huaqiang Wu,et al.  An artificial nociceptor based on a diffusive memristor , 2018, Nature Communications.

[4]  J. Yang,et al.  State Dynamics and Modeling of Tantalum Oxide Memristors , 2013, IEEE Transactions on Electron Devices.

[5]  Byung Joon Choi,et al.  Engineering nonlinearity into memristors for passive crossbar applications , 2012 .

[6]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[7]  R. Symanczyk,et al.  Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm , 2005, IEEE InternationalElectron Devices Meeting, 2005. IEDM Technical Digest..

[8]  Hyunsang Hwang,et al.  Varistor-type bidirectional switch (JMAX>107A/cm2, selectivity∼104) for 3D bipolar resistive memory arrays , 2012, 2012 Symposium on VLSI Technology (VLSIT).

[9]  Kate J. Norris,et al.  Anatomy of Ag/Hafnia‐Based Selectors with 1010 Nonlinearity , 2017, Advanced materials.

[10]  R. Dittmann,et al.  Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges , 2009, Advanced materials.

[11]  Guido Groeseneken,et al.  Ultrathin Metal/Amorphous-Silicon/Metal Diode for Bipolar RRAM Selector Applications , 2014, IEEE Electron Device Letters.

[12]  Jiaming Zhang,et al.  Analogue signal and image processing with large memristor crossbars , 2017, Nature Electronics.

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

[14]  H. Hwang,et al.  Excellent Selector Characteristics of Nanoscale $ \hbox{VO}_{2}$ for High-Density Bipolar ReRAM Applications , 2011, IEEE Electron Device Letters.

[15]  R. Stanley Williams,et al.  An accurate locally active memristor model for S-type negative differential resistance in NbOx , 2016 .

[16]  P. Bafna,et al.  Punchthrough-Diode-Based Bipolar RRAM Selector by Si Epitaxy , 2012, IEEE Electron Device Letters.

[17]  Qing Wu,et al.  A novel true random number generator based on a stochastic diffusive memristor , 2017, Nature Communications.

[18]  J. Yang,et al.  A compact modeling of TiO2-TiO2–x memristor , 2013 .

[19]  T. W. Sigmon,et al.  Diffusion of metals in silicon dioxide , 1983 .

[20]  H. Hwang,et al.  Ultrathin (<10nm) Nb2O5/NbO2 hybrid memory with both memory and selector characteristics for high density 3D vertically stackable RRAM applications , 2012, 2012 Symposium on VLSI Technology (VLSIT).

[21]  S. Jo,et al.  3D-stackable crossbar resistive memory based on Field Assisted Superlinear Threshold (FAST) selector , 2014, 2014 IEEE International Electron Devices Meeting.

[22]  P. Narayanan,et al.  Access devices for 3D crosspoint memorya) , 2014 .

[23]  D. Strukov,et al.  Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal–oxide–metal memristors , 2012, Applied Physics A.

[24]  K. Gopalakrishnan,et al.  Large-scale (512kbit) integration of multilayer-ready access-devices based on mixed-ionic-electronic-conduction (MIEC) at 100% yield , 2012, 2012 Symposium on VLSI Technology (VLSIT).

[25]  Kate J. Norris,et al.  Trilayer Tunnel Selectors for Memristor Memory Cells , 2015, Advanced materials.

[26]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[27]  J. Yang,et al.  Truly Electroforming‐Free and Low‐Energy Memristors with Preconditioned Conductive Tunneling Paths , 2017 .

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

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

[30]  Bernard Nysten,et al.  Surface tension effect on the mechanical properties of nanomaterials measured by atomic force microscopy , 2004 .

[31]  J. Yang,et al.  Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications , 2018 .

[32]  Feng Miao,et al.  A selector device based on graphene–oxide heterostructures for memristor crossbar applications , 2015 .

[33]  Yiran Chen,et al.  Low voltage two-state-variable memristor model of vacancy-drift resistive switches , 2015 .

[34]  Herbert Schroeder,et al.  Comment on “Exponential ionic drift: fast switching and low volatility of thin-film memristors” by D.B. Strukov and R.S. Williams in Appl. Phys. A (2009) 94: 515–519 , 2011 .

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

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

[37]  H. C. Jung,et al.  NbO2-based low power and cost effective 1S1R switching for high density cross point ReRAM Application , 2014, Symposium on VLSI Technology.