Investigation of Forming, SET, and Data Retention of Conductive-Bridge Random-Access Memory for Stack Optimization

In this paper, we investigate in depth Forming, SET, and Retention of conductive-bridge random-access memory (CBRAM). A kinetic Monte Carlo model of the CBRAM has been developed considering ionic hopping and chemical reaction dynamics. Based on inputs from ab initio calculations and the physical properties of the materials, the model offers the simulation of both the Forming/SET and the Data Retention operations. It aims to create a bond between the physics at atomic level and the device behavior. From the model and experimental results obtained on decananometric devices, we propose an understanding of the physical mechanisms involved in the CBRAM operations. Using the consistent Forming/SET and Data Retention model, we obtained good agreement with the experimental data. Finally, the impact of each layer of the CBRAM on the Forming/SET behavior is decorrelated, allowing an optimization of the performance.

[1]  G. Ghibaudo,et al.  Experimental and theoretical understanding of Forming, SET and RESET operations in Conductive Bridge RAM (CBRAM) for memory stack optimization , 2014, 2014 IEEE International Electron Devices Meeting.

[2]  M. Kozicki,et al.  Effects of cooperative ionic motion on programming kinetics of conductive-bridge memory cells , 2012 .

[3]  Keith J. Laidler,et al.  DEVELOPMENT OF TRANSITION-STATE THEORY , 1983 .

[4]  J. Cluzel,et al.  Impact of SET and RESET conditions on CBRAM high temperature data retention , 2014, 2014 IEEE International Reliability Physics Symposium.

[5]  Influence of metal electrode stoichiometry on the electron barrier height at CuxTe1−x/Al2O3 interfaces for CBRAM applications , 2014 .

[6]  Guido Groeseneken,et al.  Filament observation in metal-oxide resistive switching devices , 2013 .

[7]  Alessandro Calderoni,et al.  A copper ReRAM cell for Storage Class Memory applications , 2014, 2014 Symposium on VLSI Technology (VLSI-Technology): Digest of Technical Papers.

[8]  L. Goux,et al.  Field-driven ultrafast sub-ns programming in W\Al2O3\Ti\CuTe-based 1T1R CBRAM system , 2012, 2012 Symposium on VLSI Technology (VLSIT).

[9]  J. Guy,et al.  Investigation of the physical mechanisms governing data-retention in down to 10nm nano-trench Al2O3/CuTeGe conductive bridge RAM (CBRAM) , 2013, 2013 IEEE International Electron Devices Meeting.

[10]  S. Z. Rahaman,et al.  Low current (5 pA) resistive switching memory using high-к Ta2O5 solid electrolyte , 2009, 2009 Proceedings of the European Solid State Device Research Conference.

[11]  R. Waser,et al.  Integration of GexSe1-x in crossbar arrays for non-volatile memory applications , 2009 .

[12]  L. Goux,et al.  Modeling of Copper Diffusion in Amorphous Aluminum Oxide in CBRAM Memory Stack , 2012 .

[13]  S. Balatti,et al.  Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part II: Modeling , 2012, IEEE Transactions on Electron Devices.

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

[15]  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.

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

[18]  J. McPherson,et al.  Complementary model for intrinsic time-dependent dielectric breakdown in SiO2 dielectrics , 2000 .