An in-depth description of bipolar resistive switching in Cu/HfOx/Pt devices, a 3D kinetic Monte Carlo simulation approach

A simulation tool based on a 3D kinetic Monte Carlo algorithm has been employed to analyse bipolar conductive bridge RAMs fabricated with Cu/HfOx/Pt stacks. Resistive switching mechanisms are described accounting for the electric field and temperature distributions within the dielectric. The formation and destruction of conductive filaments (CFs) are analysed taking into consideration redox reactions and the joint action of metal ion thermal diffusion and electric field induced drift. Filamentary conduction is considered when different percolation paths are formed in addition to other conventional transport mechanisms in dielectrics. The simulator was tuned by using the experimental data for Cu/HfOx/Pt bipolar devices that were fabricated. Our simulation tool allows for the study of different experimental results, in particular, the current variations due to the electric field changes between the filament tip and the electrode in the High Resistance State. In addition, the density of metallic atoms within the CF can also be characterized along with the corresponding CF resistance description.A simulation tool based on a 3D kinetic Monte Carlo algorithm has been employed to analyse bipolar conductive bridge RAMs fabricated with Cu/HfOx/Pt stacks. Resistive switching mechanisms are described accounting for the electric field and temperature distributions within the dielectric. The formation and destruction of conductive filaments (CFs) are analysed taking into consideration redox reactions and the joint action of metal ion thermal diffusion and electric field induced drift. Filamentary conduction is considered when different percolation paths are formed in addition to other conventional transport mechanisms in dielectrics. The simulator was tuned by using the experimental data for Cu/HfOx/Pt bipolar devices that were fabricated. Our simulation tool allows for the study of different experimental results, in particular, the current variations due to the electric field changes between the filament tip and the electrode in the High Resistance State. In addition, the density of metallic atoms within...

[1]  S. Timsit,et al.  Electrical contact resistance: properties of stationary interfaces , 1998, Electrical Contacts - 1998. Proceedings of the Forty-Fourth IEEE Holm Conference on Electrical Contacts (Cat. No.98CB36238).

[2]  Vivek Subramanian,et al.  A Detailed Study of the Forming Stage of an Electrochemical Resistive Switching Memory by KMC Simulation , 2011, IEEE Electron Device Letters.

[3]  Jordi Suñé,et al.  Simulation of thermal reset transitions in resistive switching memories including quantum effects , 2014 .

[4]  T. Hasegawa,et al.  Atomic Switch: Atom/Ion Movement Controlled Devices for Beyond Von‐Neumann Computers , 2012, Advanced materials.

[5]  F. Zeng,et al.  Recent progress in resistive random access memories: Materials, switching mechanisms, and performance , 2014 .

[6]  S. Menzel,et al.  Simulation of multilevel switching in electrochemical metallization memory cells , 2012 .

[7]  M. Lanza,et al.  Resistive switching in hafnium dioxide layers: Local phenomenon at grain boundaries , 2012 .

[8]  Jordi Suñé,et al.  An in-depth study of thermal effects in reset transitions in HfO2 based RRAMs , 2015 .

[9]  D. Ielmini,et al.  Study of Multilevel Programming in Programmable Metallization Cell (PMC) Memory , 2009, IEEE Transactions on Electron Devices.

[10]  K. Aratani,et al.  Process integration of a 27nm, 16Gb Cu ReRAM , 2014, 2014 IEEE International Electron Devices Meeting.

[11]  Jordi Suñé,et al.  A new compact model for bipolar RRAMs based on truncated-cone conductive filaments—a Verilog-A approach , 2016 .

[12]  Masahide Matsumoto,et al.  A 130.7-$\hbox{mm}^{2}$ 2-Layer 32-Gb ReRAM Memory Device in 24-nm Technology , 2014, IEEE Journal of Solid-State Circuits.

[13]  Mireia Bargallo Gonzalez,et al.  A 3D kinetic Monte Carlo simulation study of resistive switching processes in Ni/HfO2/Si-n+-based RRAMs , 2017 .

[14]  G. Gonzalez-Cordero,et al.  In-depth study of the physics behind resistive switching in TiN/Ti/HfO2/W structures , 2017 .

[15]  S. Long,et al.  An in-depth simulation study of thermal reset transitions in resistive switching memories , 2013 .

[16]  Shimeng Yu,et al.  On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology , 2012, IEEE Transactions on Electron Devices.

[17]  Qi Liu,et al.  Uniformity Improvement in 1T1R RRAM With Gate Voltage Ramp Programming , 2014, IEEE Electron Device Letters.

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

[19]  O. Pirrotta,et al.  Microscopic Modeling of HfOx RRAM Operations: From Forming to Switching , 2015, IEEE Transactions on Electron Devices.

[20]  Ee Wah Lim,et al.  Conduction Mechanism of Valence Change Resistive Switching Memory: A Survey , 2015 .

[21]  E. Miranda,et al.  Resistive Switching with Self-Rectifying Tunability and Influence of the Oxide Layer Thickness in Ni/HfO2/n+-Si RRAM Devices , 2017, IEEE Transactions on Electron Devices.

[22]  Fabien Clermidy,et al.  Investigation of Forming, SET, and Data Retention of Conductive-Bridge Random-Access Memory for Stack Optimization , 2015, IEEE Transactions on Electron Devices.

[23]  M. B. González,et al.  A new parameter to characterize the charge transport regime in Ni/HfO 2 /Si-n + -based RRAMs , 2016 .

[24]  C. Cagli,et al.  Cycle-to-Cycle Intrinsic RESET Statistics in ${\rm HfO}_{2}$-Based Unipolar RRAM Devices , 2013, IEEE Electron Device Letters.

[25]  S. Long,et al.  A comprehensive analysis on progressive reset transitions in RRAMs , 2014 .

[26]  Yukio Hayakawa,et al.  An 8 Mb Multi-Layered Cross-Point ReRAM Macro With 443 MB/s Write Throughput , 2012, IEEE Journal of Solid-State Circuits.

[27]  K. Terabe,et al.  Forming and switching mechanisms of a cation-migration-based oxide resistive memory , 2010, Nanotechnology.

[28]  C. Muller,et al.  Self-consistent physical modeling of set/reset operations in unipolar resistive-switching memories , 2011 .

[29]  D. Ielmini,et al.  Analysis and modeling of resistive switching statistics , 2012 .

[30]  Yuchao Yang,et al.  Nanoscale resistive switching devices: mechanisms and modeling. , 2013, Nanoscale.

[31]  Qi Liu,et al.  A cell-based clustering model for the reset statistics in RRAM , 2017 .

[32]  Jordi Suñé,et al.  Voltage and Power-Controlled Regimes in the Progressive Unipolar RESET Transition of HfO2-Based RRAM , 2013, Scientific Reports.

[33]  L. Larcher,et al.  Microscopic Modeling of Electrical Stress-Induced Breakdown in Poly-Crystalline Hafnium Oxide Dielectrics , 2013, IEEE Transactions on Electron Devices.

[34]  Shimeng Yu,et al.  On the Switching Parameter Variation of Metal Oxide RRAM—Part II: Model Corroboration and Device Design Strategy , 2012, IEEE Transactions on Electron Devices.

[35]  M. Lanza.  A Review on Resistive Switching in High-k Dielectrics: A Nanoscale Point of View Using Conductive Atomic Force Microscope , 2014, Materials.

[36]  Jordi Suñé,et al.  $${ SIM}^2{ RRAM}$$SIM2RRAM: a physical model for RRAM devices simulation , 2017 .

[37]  Gennadi Bersuker,et al.  Limitations of Poole–Frenkel Conduction in Bilayer $\hbox{HfO}_{2}/\hbox{SiO}_{2}$ MOS Devices , 2010, IEEE Transactions on Device and Materials Reliability.