Strategies to Control the Relaxation Kinetics of Ag‐Based Diffusive Memristors and Implications for Device Operation

Diffusive memristors based on volatile electrochemical metallization (v‐ECM) devices are of broad interest for applications in emerging memory technologies and neuromorphic computing areas due to their interesting features such as thresholding, self‐relaxation, and energy‐efficient switching behaviors. Recently, the nonlinear threshold kinetics and the correlation of filament formation and growth with its relaxation behavior are uncovered from a physical perspective. However, the complexity of the diffusive memristors’ behavior might still hamper a straight transfer into emerging computing applications. Facing this challenge means going beyond the single device level and understanding the impact of other circuitry elements for further optimization of device operation. In this work, the effect of a series resistor on the switching dynamics of Ag/HfO2/Pt diffusive memristor devices by deploying various programming schemes with different resistances is investigated. Furthermore, all results and their implications on devices’ operation are compressively discussed. These findings help to further advance the optimization of the operating condition of diffusive memristors.

[1]  Solomon Amsalu Chekol,et al.  Effect of the Threshold Kinetics on the Filament Relaxation Behavior of Ag‐Based Diffusive Memristors , 2021, Advanced Functional Materials.

[2]  J. Yang,et al.  A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing , 2021, Advanced Electronic Materials.

[3]  J. Joshua Yang,et al.  Timing Selector: Using Transient Switching Dynamics to Solve the Sneak Path Issue of Crossbar Arrays , 2021, Small Science.

[4]  H. Hwang,et al.  An Efficient Approach Based on Tuned Nanoionics to Maximize Memory Characteristics in Ag‐Based Devices , 2021, Advanced Electronic Materials.

[5]  Wei D. Lu,et al.  Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching , 2020, Advanced materials.

[6]  Ming Liu,et al.  Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks. , 2020, Science bulletin.

[7]  Stavros Kitsios,et al.  Investigating the origins of ultra-short relaxation times of silver filaments in forming-free SiO2-based conductive bridge memristors , 2020, Nanotechnology.

[8]  R. Waser,et al.  Design of defect-chemical properties and device performance in memristive systems , 2020, Science Advances.

[9]  Mark Barnell,et al.  Three-dimensional memristor circuits as complex neural networks , 2020, Nature Electronics.

[10]  S. Menzel,et al.  Picosecond multilevel resistive switching in tantalum oxide thin films , 2019, Scientific Reports.

[11]  Kaushik Roy,et al.  Towards spike-based machine intelligence with neuromorphic computing , 2019, Nature.

[12]  C. Cruchaga,et al.  A missense variant in SLC39A8 is associated with severe idiopathic scoliosis , 2018, Nature Communications.

[13]  Weiwei Xia,et al.  Memristor Crossbars with 4.5 Terabits-per-Inch-Square Density and Two Nanometer Dimension , 2018, ArXiv.

[14]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

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

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

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

[18]  Michael N. Kozicki,et al.  Conductive bridging random access memory—materials, devices and applications , 2016 .

[19]  S. Hoffmann‐Eifert,et al.  Tuning the Performance of Pt/HfO2/Ti/Pt ReRAM Devices Obtained from Plasma-Enhanced Atomic Layer Deposition for HfO2 Thin Films , 2016 .

[20]  S. Menzel,et al.  Physics of the Switching Kinetics in Resistive Memories , 2015 .

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  Doo Seok Jeong,et al.  A Review of Three‐Dimensional Resistive Switching Cross‐Bar Array Memories from the Integration and Materials Property Points of View , 2014 .

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

[24]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

[25]  Rainer Waser,et al.  Switching kinetics of electrochemical metallization memory cells. , 2013, Physical chemistry chemical physics : PCCP.

[26]  Jan van den Hurk,et al.  Nanobatteries in redox-based resistive switches require extension of memristor theory , 2013, Nature Communications.

[27]  Masakazu Aono,et al.  Rate-limiting processes in the fast SET operation of a gapless-type Cu-Ta2O5 atomic switch , 2013 .

[28]  Tong-Fang Liu,et al.  Volatile resistive switching in Cu/TaOx/δ-Cu/Pt devices , 2012 .

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

[30]  R. Waser,et al.  Nanoionic transport and electrochemical reactions in resistively switching silicon dioxide. , 2012, Nanoscale.

[31]  Narayan Srinivasa,et al.  A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. , 2012, Nano letters.

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

[33]  R. Waser,et al.  On the stochastic nature of resistive switching in Cu doped Ge0.3Se0.7 based memory devices , 2011 .

[34]  M. Kozicki,et al.  Erratum: Electrochemical metallization memories - Fundamentals, applications, prospects (Nanotechnology (2011) 22 (254003)) , 2011 .

[35]  Frederick T. Chen,et al.  Formation and instability of silver nanofilament in Ag-based programmable metallization cells. , 2010, ACS nano.

[36]  Jae Hyuck Jang,et al.  Atomic structure of conducting nanofilaments in TiO2 resistive switching memory. , 2010, Nature nanotechnology.

[37]  Dmitri B Strukov,et al.  Four-dimensional address topology for circuits with stacked multilayer crossbar arrays , 2009, Proceedings of the National Academy of Sciences.

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

[39]  R. Waser,et al.  Controlled local filament growth and dissolution in Ag–Ge–Se , 2008 .

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

[41]  K. Terabe,et al.  Quantized conductance atomic switch , 2005, Nature.

[42]  J. Robertson High dielectric constant oxides , 2004 .

[43]  Jane P. Chang,et al.  Dielectric property and thermal stability of HfO2 on silicon , 2002 .

[44]  S. Gangopadhyay,et al.  HfO2 gate dielectric with 0.5 nm equivalent oxide thickness , 2002 .

[45]  Juan Carlos Cuevas,et al.  The signature of chemical valence in the electrical conduction through a single-atom contact , 1998, Nature.

[46]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.

[47]  John W. Backus,et al.  Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs , 1978, CACM.