Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices

Silver/copper-filament-based resistive switching memory relies on the formation and disruption of a metallic conductive filament (CF) with relatively large surface-to-volume ratio. The nanoscale CF can spontaneously break after formation, with a lifetime ranging from few microseconds to several months, or even years. Controlling and predicting the CF lifetime enables device engineering for a wide range of applications, such as non-volatile memory for data storage, tunable short/long term memory for synaptic neuromorphic computing, and fast selection devices for crosspoint arrays. However, conflictive explanations for the CF retention process are being proposed. Here we show that the CF lifetime can be described by a universal surface-limited self-diffusion mechanism of disruption of the metallic CF. The surface diffusion process provides a new perspective of ion transport mechanism at the nanoscale, explaining the broad range of reported lifetimes, and paving the way for material engineering of resistive switching device for memory and computing applications.Resistive random-access memory is operated based on the formation and disruption of nanoscale conductive filaments, but a mechanistic understanding of this process remains unclear. Here, Wang et al. develop a surface-diffusion model to describe lifetime of filaments ranging from microseconds to years.

[1]  Yuchao Yang,et al.  Nonvolatile resistive switching in single crystalline ZnO nanowires. , 2011, Nanoscale.

[2]  F. M. Lee,et al.  An ultra high endurance and thermally stable selector based on TeAsGeSiSe chalcogenides compatible with BEOL IC Integration for cross-point PCM , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[3]  W. Steinhögl,et al.  Size-dependent resistivity of metallic wires in the mesoscopic range , 2002 .

[4]  I. Goldthorpe,et al.  The dependence of silver nanowire stability on network composition and processing parameters , 2017 .

[5]  Jang-Sik Lee,et al.  Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics. , 2018, ACS nano.

[6]  D. Ielmini,et al.  Enhancing the Matrix Addressing of Flexible Sensory Arrays by a Highly Nonlinear Threshold Switch , 2018, Advanced materials.

[7]  Lucel Sirghi,et al.  Volume of a nanoscale water bridge. , 2006, Langmuir : the ACS journal of surfaces and colloids.

[8]  Ib Chorkendorff,et al.  Direct observations of oxygen-induced platinum nanoparticle ripening studied by in situ TEM. , 2010, Journal of the American Chemical Society.

[9]  Jun Sun,et al.  Liquid-like pseudoelasticity of sub-10-nm crystalline silver particles. , 2014, Nature materials.

[10]  Qing Luo,et al.  Cu BEOL compatible selector with high selectivity (>107), extremely low off-current (∼pA) and high endurance (>1010) , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[11]  Dewei Chu,et al.  Silver Nanowire Networks : From Transparent Electrodes to Resistive Switching Devices , 2017 .

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

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

[14]  M. Kramer,et al.  Highly optimized embedded-atom-method potentials for fourteen fcc metals , 2011 .

[15]  Wei Lu,et al.  Real-Time Observation of the Electrode-Size-Dependent Evolution Dynamics of the Conducting Filaments in a SiO2 Layer. , 2017, ACS nano.

[16]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[17]  D. Ielmini,et al.  Resistive Switching Device Technology Based on Silicon Oxide for Improved ON–OFF Ratio—Part I: Memory Devices , 2018, IEEE Transactions on Electron Devices.

[18]  Qi Liu,et al.  Real‐Time Observation on Dynamic Growth/Dissolution of Conductive Filaments in Oxide‐Electrolyte‐Based ReRAM , 2012, Advanced materials.

[19]  Daniele Ielmini,et al.  Resistive Switching Device Technology Based on Silicon Oxide for Improved ON–OFF Ratio—Part II: Select Devices , 2018, IEEE Transactions on Electron Devices.

[20]  G. C. Kuczynski,et al.  Self-Diffusion in Sintering of Metallic Particles , 1949 .

[21]  A. Bessonov,et al.  Layered memristive and memcapacitive switches for printable electronics. , 2015, Nature materials.

[22]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[23]  A novel nanoscale‐crossbar resistive switching memory using a copper chemical displacement technique , 2017 .

[24]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[25]  Alessandro Calderoni,et al.  Statistical Fluctuations in HfOx Resistive-Switching Memory: Part I - Set/Reset Variability , 2014, IEEE Transactions on Electron Devices.

[26]  T. Hasegawa,et al.  Nanometer-scale switches using copper sulfide , 2003 .

[27]  Daniele Ielmini,et al.  Resistive switching memories based on metal oxides: mechanisms, reliability and scaling , 2016 .

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

[29]  M. Lanza,et al.  Graphene‐Coated Atomic Force Microscope Tips for Reliable Nanoscale Electrical Characterization , 2013, Advanced materials.

[30]  William Thomson,et al.  4. On the Equilibrium of Vapour at a Curved Surface of Liquid. , 1872 .

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

[32]  D. Ielmini,et al.  Size-Dependent Retention Time in NiO-Based Resistive-Switching Memories , 2010, IEEE Electron Device Letters.

[33]  E. H. Sondheimer,et al.  The mean free path of electrons in metals , 1952 .

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

[35]  Qi Liu,et al.  Evolution of conductive filament and its impact on reliability issues in oxide-electrolyte based resistive random access memory , 2015, Scientific Reports.

[36]  Stefano Ambrogio,et al.  Analytical Modeling of Current Overshoot in Oxide-Based Resistive Switching Memory (RRAM) , 2016, IEEE Electron Device Letters.

[37]  Hyunsang Hwang,et al.  Field-induced nucleation in threshold switching characteristics of electrochemical metallization devices , 2017 .

[38]  Wei D. Lu,et al.  Electrochemical dynamics of nanoscale metallic inclusions in dielectrics , 2014, Nature Communications.

[39]  Fekadu Gochole Aga,et al.  Retention modeling for ultra-thin density of Cu-based conductive bridge random access memory (CBRAM) , 2016 .

[40]  S. Ambrogio,et al.  Statistical Fluctuations in HfOx Resistive-Switching Memory: Part II—Random Telegraph Noise , 2014, IEEE Transactions on Electron Devices.

[41]  Feng Miao,et al.  Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors , 2016, Nature Communications.

[42]  Alessandro Calderoni,et al.  Voltage-Controlled Cycling Endurance of HfOx-Based Resistive-Switching Memory , 2015, IEEE Transactions on Electron Devices.

[43]  Qi Liu,et al.  Breaking the Current‐Retention Dilemma in Cation‐Based Resistive Switching Devices Utilizing Graphene with Controlled Defects , 2018, Advanced materials.

[44]  F. Emmerling,et al.  Formation mechanism of colloidal silver nanoparticles: analogies and differences to the growth of gold nanoparticles. , 2012, ACS nano.

[45]  J. Shapter,et al.  Carbon nanotube modified probes for stable and high sensitivity conductive atomic force microscopy , 2016, Nanotechnology.

[46]  Conyers Herring,et al.  Effect of Change of Scale on Sintering Phenomena , 1950 .

[47]  J. Seol,et al.  Nanometer‐Scale Phase Transformation Determines Threshold and Memory Switching Mechanism , 2017, Advanced materials.

[48]  Antoine Triller,et al.  Synaptic stability and plasticity in a floating world , 2010, Current Opinion in Neurobiology.

[49]  R. Waser,et al.  Atomically controlled electrochemical nucleation at superionic solid electrolyte surfaces. , 2012, Nature materials.

[50]  Suman Datta,et al.  Fundamental mechanism behind volatile and non-volatile switching in metallic conducting bridge RAM , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[51]  D. Ielmini,et al.  Modeling the Universal Set/Reset Characteristics of Bipolar RRAM by Field- and Temperature-Driven Filament Growth , 2011, IEEE Transactions on Electron Devices.

[52]  F. A. Nichols,et al.  Morphological Changes of a Surface of Revolution due to Capillarity‐Induced Surface Diffusion , 1965 .

[53]  H. Hwang,et al.  Analysis of copper ion filaments and retention of dual-layered devices for resistance random access memory applications , 2009 .

[54]  Hyunsang Hwang,et al.  Investigation of State Stability of Low-Resistance State in Resistive Memory , 2010, IEEE Electron Device Letters.

[55]  Klaus Rademann,et al.  Formation mechanism of silver nanoparticles stabilized in glassy matrices. , 2012, Journal of the American Chemical Society.

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

[57]  Sir William Thomson F.R.S..  LX. On the equilibrium of vapour at a curved surface of liquid , 1871 .

[58]  Stefano Ambrogio,et al.  Impact of the Mechanical Stress on Switching Characteristics of Electrochemical Resistive Memory , 2014, Advanced materials.

[59]  Yichun Liu,et al.  Nonvolatile/volatile behaviors and quantized conductance observed in resistive switching memory based on amorphous carbon , 2015 .

[60]  S. Dahl,et al.  Ostwald ripening in a Pt/SiO2 model catalyst studied by in situ TEM , 2011 .

[61]  Chun-Chieh Huang,et al.  Retention mechanism of Cu-doped SiO2-based resistive memory , 2011 .

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

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

[64]  Young Jae Kwon,et al.  Nociceptive Memristor , 2018, Advanced materials.

[65]  W. Mullins.  Theory of Thermal Grooving , 1957 .