Improved Synaptic Behavior of CBRAM Using Internal Voltage Divider for Neuromorphic Systems

In this paper, we demonstrate the linear conductance-change characteristics of a conductive-bridging RAM (CBRAM) to be employed as an artificial synapse device in neuromorphic systems. The CBRAM with a bilayer electrolyte structure (<inline-formula> <tex-math notation="LaTeX">${\mathrm {Cu/Cu}}_{{2}-{x}}\text{S}$ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\mathrm {WO}_{{3}-{x}}$ </tex-math></inline-formula>/W) exhibits analog switching behavior during the depression process due to the well-controlled dissolution of the conductive filament. To analyze the origin of this motion, we investigate the effective voltage applied to <inline-formula> <tex-math notation="LaTeX">$\mathrm {Cu}_{{2}-{x}}\text{S}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathrm {WO}_{{3}-{x}}$ </tex-math></inline-formula>. Our findings reveal that <inline-formula> <tex-math notation="LaTeX">$\mathrm {Cu}_{{2}-{x}}\text{S}$ </tex-math></inline-formula>, acting as a voltage divider, helps in suppressing the large voltage drop in <inline-formula> <tex-math notation="LaTeX">$\mathrm {WO}_{{3}-{x}}$ </tex-math></inline-formula>, where the formation/dissolution of filament occurs. Furthermore, due to the diode-like characteristics of <inline-formula> <tex-math notation="LaTeX">$\mathrm {Cu}_{{2}-{x}}\text{S}$ </tex-math></inline-formula> and the division of voltage drop between <inline-formula> <tex-math notation="LaTeX">$\mathrm {WO}_{{3}-{x}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathrm {Cu}_{{2}-{x}}\text{S}$ </tex-math></inline-formula>, an optimum programming energy is applied to <inline-formula> <tex-math notation="LaTeX">$\mathrm {WO}_{{3}-{x}}$ </tex-math></inline-formula> during the depression process. This leads to linear conductance-change characteristics under identical pulses. However, abrupt conductance-change characteristics are observed during the potentiation process. Thus, we use only the device characteristics of the depression part for the neuromorphic system. An excellent classification accuracy is achieved due to the linear conductance-change characteristics and optimized pulse conditions.

[1]  Adrian M. Ionescu,et al.  Energy efficient computing and sensing in the Zettabyte era: From silicon to the cloud , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[2]  Manan Suri,et al.  Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices , 2017 .

[3]  Jae-Joon Kim,et al.  Input Voltage Mapping Optimized for Resistive Memory-Based Deep Neural Network Hardware , 2017, IEEE Electron Device Letters.

[4]  Pritish Narayanan,et al.  Neuromorphic computing using non-volatile memory , 2017 .

[5]  R. Degraeve,et al.  Retention, disturb and variability improvements enabled by local chemical-potential tuning and controlled Hour-Glass filament shape in a novel W\WO3\Al2O3\Cu CBRAM , 2016, 2016 IEEE Symposium on VLSI Technology.

[6]  Yoon-Ha Jeong,et al.  Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems , 2015, IEEE Electron Device Letters.

[7]  H. Kim,et al.  RRAM-based synapse for neuromorphic system with pattern recognition function , 2012, 2012 International Electron Devices Meeting.

[8]  Shimeng Yu,et al.  Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[9]  Pritish Narayanan,et al.  Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element , 2014, IEEE Transactions on Electron Devices.

[10]  Progressive vs. abrupt reset behavior in conductive bridging devices: A C-AFM tomography study , 2014, 2014 IEEE International Electron Devices Meeting.

[11]  R. Degraeve,et al.  Programming-conditions solutions towards suppression of retention tails of scaled oxide-based RRAM , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[12]  G. W. Burr,et al.  Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.

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

[14]  Dirk Wouters,et al.  Influence of the Cu-Te composition and microstructure on the resistive switching of Cu-Te/Al2O3/Si cells , 2011 .

[15]  I. Yokota.  On the Electrical Conductivity of Cuprous Sulfide: A Diffusion Theory , 1953 .

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

[17]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[18]  Hyunsang Hwang,et al.  TiOx-Based RRAM Synapse With 64-Levels of Conductance and Symmetric Conductance Change by Adopting a Hybrid Pulse Scheme for Neuromorphic Computing , 2016, IEEE Electron Device Letters.

[19]  H. Hwang,et al.  Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems , 2016, IEEE Electron Device Letters.

[20]  Adam Z. Stieg,et al.  Neuromorphic Atomic Switch Networks , 2012, PloS one.

[21]  Communication—Excellent Threshold Selector Characteristics of Cu2S-Based Atomic Switch Device , 2017 .

[22]  Taher Daud,et al.  Solid‐state thin‐film memistor for electronic neural networks , 1990 .

[23]  Hyunsang Hwang,et al.  Improved Synapse Device With MLC and Conductance Linearity Using Quantized Conduction for Neuromorphic Systems , 2018, IEEE Electron Device Letters.

[24]  Shimeng Yu,et al.  Modeling the switching dynamics of programmable-metallization-cell (PMC) memory and its application as synapse device for a neuromorphic computation system , 2010, 2010 International Electron Devices Meeting.

[25]  G. Burr,et al.  Highly-scalable novel access device based on Mixed Ionic Electronic conduction (MIEC) materials for high density phase change memory (PCM) arrays , 2010, 2010 Symposium on VLSI Technology.

[26]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[27]  H. Hwang,et al.  Optimized Programming Scheme Enabling Linear Potentiation in Filamentary HfO2 RRAM Synapse for Neuromorphic Systems , 2016, IEEE Transactions on Electron Devices.

[28]  C. M. Lilley,et al.  Surface and size effects on the electrical properties of Cu nanowires , 2008 .

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

[30]  Masakazu Aono,et al.  A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing , 2013, Nanotechnology.

[31]  G. Schindler,et al.  Scaling laws for the resistivity increase of sub-100 nm interconnects , 2003, International Conference on Simulation of Semiconductor Processes and Devices, 2003. SISPAD 2003..

[32]  Hyunsang Hwang,et al.  Introduction of WO3 Layer in a Cu-Based Al2O3 Conductive Bridge RAM System for Robust Cycling and Large Memory Window , 2016, IEEE Journal of the Electron Devices Society.

[33]  A. Strachan,et al.  Structural and electronic properties of copper-doped chalcogenide glasses , 2017 .