Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.

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

[2]  Runchen Fang,et al.  A CMOS-compatible electronic synapse device based on Cu/SiO2/W programmable metallization cells , 2016, Nanotechnology.

[3]  Shimeng Yu,et al.  Fully parallel write/read in resistive synaptic array for accelerating on-chip learning , 2015, Nanotechnology.

[4]  Jacques-Olivier Klein,et al.  Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[5]  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).

[6]  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.

[7]  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.

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

[9]  Ryutaro Yasuhara,et al.  Quantitative method for estimating characteristics of conductive filament in ReRAM , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[10]  Chung Lam,et al.  Experimental demonstration of array-level learning with phase change synaptic devices , 2013, 2013 IEEE International Electron Devices Meeting.

[11]  Ming-Jinn Tsai,et al.  Improvement of Resistive Switching Characteristics by Thermally Assisted Forming Process for $\hbox{SiO}_{2}$-Based Structure , 2013, IEEE Electron Device Letters.

[12]  L. Goux,et al.  Endurance/Retention Trade-off on $\hbox{HfO}_{2}/\hbox{Metal}$ Cap 1T1R Bipolar RRAM , 2013, IEEE Transactions on Electron Devices.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Z. Wei,et al.  Conductive filament scaling of TaOx bipolar ReRAM for long retention with low current operation , 2012, 2012 Symposium on VLSI Technology (VLSIT).

[15]  O. Richard,et al.  10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation , 2011, 2011 International Electron Devices Meeting.

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

[17]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

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