Noise cancellation of memristive neural networks

This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.

[1]  Gregory S. Snider,et al.  ‘Memristive’ switches enable ‘stateful’ logic operations via material implication , 2010, Nature.

[2]  E. Lehtonen,et al.  CNN using memristors for neighborhood connections , 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).

[3]  J. Yang,et al.  High switching endurance in TaOx memristive devices , 2010 .

[4]  Jun Wang,et al.  A systematic method for analyzing robust stability of interval neural networks with time-delays based on stability criteria , 2014, Neural Networks.

[5]  Chuandong Li,et al.  Bogdanov-Takens bifurcation in a single inertial neuron model with delay , 2012, Neurocomputing.

[6]  Nathaniel C. Cady,et al.  Ion implantation synthesized copper oxide-based resistive memory devices , 2011 .

[7]  Zhigang Zeng,et al.  Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators , 2014, IEEE Transactions on Fuzzy Systems.

[8]  J. Liu,et al.  High‐Performance Programmable Memory Devices Based on Co‐Doped BaTiO3 , 2011, Advanced materials.

[9]  Zhigang Zeng,et al.  Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays , 2013, Neural Networks.

[10]  R. Zhao,et al.  Electrochemical Metallization Resistive Memory Devices Using $\hbox{ZnS-SiO}_{2}$ as a Solid Electrolyte , 2012, IEEE Electron Device Letters.

[11]  Bart J. Kooi,et al.  Polarity-dependent reversible resistance switching in Ge-Sb-Te phase-change thin films , 2007 .

[12]  Guy Rachmuth,et al.  Transistor analogs of emergent iono‐neuronal dynamics , 2008, HFSP journal.

[13]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[14]  R. Dittmann,et al.  Coexistence of Filamentary and Homogeneous Resistive Switching in Fe‐Doped SrTiO3 Thin‐Film Memristive Devices , 2010, Advanced materials.

[15]  Chuandong Li,et al.  Neural network for solving convex quadratic bilevel programming problems , 2014, Neural Networks.

[16]  D. B. Strukov,et al.  Programmable CMOS/Memristor Threshold Logic , 2013, IEEE Transactions on Nanotechnology.

[17]  Byoung Hun Lee,et al.  Excellent resistive switching in nitrogen-doped Ge2Sb2Te5 devices for field-programmable gate array configurations , 2011 .

[18]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[19]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[20]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[21]  A. Ayatollahi,et al.  Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits , 2009, 2009 European Conference on Circuit Theory and Design.

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

[23]  Bart J. Kooi,et al.  Polarity-dependent resistance switching in GeSbTe phase-change thin films: The importance of excess Sb in filament formation , 2009 .

[24]  A. Thomas,et al.  The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System , 2012, Advanced materials.

[25]  Huai-Ning Wu,et al.  Neural Network Based Online Simultaneous Policy Update Algorithm for Solving the HJI Equation in Nonlinear $H_{\infty}$ Control , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Hiroshi Koyama,et al.  Resistive switching properties of high crystallinity and low-resistance Pr0.7Ca0.3MnO3 thin film with point-contacted Ag electrodes , 2007 .

[27]  Huai-Ning Wu,et al.  Approximate Optimal Control Design for Nonlinear One-Dimensional Parabolic PDE Systems Using Empirical Eigenfunctions and Neural Network , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  X. Liao,et al.  Edge detection of noisy images based on cellular neural networks , 2011 .

[29]  Cheol Seong Hwang,et al.  A Resistive Memory in Semiconducting BiFeO3 Thin‐Film Capacitors , 2011, Advanced materials.

[30]  Warren Robinett,et al.  Memristor-CMOS hybrid integrated circuits for reconfigurable logic. , 2009, Nano letters.

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

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

[33]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[34]  Qingshan Liu,et al.  A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[35]  L. Chua Memristor-The missing circuit element , 1971 .

[36]  He Tian,et al.  Resistive switching behavior in diamond-like carbon films grown by pulsed laser deposition for resistance switching random access memory application , 2012 .

[37]  Shimeng Yu,et al.  Conduction mechanism of TiN/HfOx/Pt resistive switching memory: A trap-assisted-tunneling model , 2011 .

[38]  Sung-Mo Kang,et al.  Analysis of Passive Memristive Devices Array: Data-Dependent Statistical Model and Self-Adaptable Sense Resistance for RRAMs , 2012, Proceedings of the IEEE.

[39]  J. Grollier,et al.  A ferroelectric memristor. , 2012, Nature materials.

[40]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[41]  Qingshan Liu,et al.  A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming , 2008, IEEE Transactions on Neural Networks.

[42]  U. Böttger,et al.  Beyond von Neumann—logic operations in passive crossbar arrays alongside memory operations , 2012, Nanotechnology.

[43]  Albert Y. Zomaya Handbook of Nature-Inspired and Innovative Computing - Integrating Classical Models with Emerging Technologies , 2006 .

[44]  Hyunsang Hwang,et al.  Effect of interfacial oxide layer on the switching uniformity of Ge2Sb2Te5-based resistive change memory devices , 2011 .

[45]  Pinaki Mazumder,et al.  CMOS and Memristor-Based Neural Network Design for Position Detection , 2012, Proceedings of the IEEE.

[46]  D. Rhodes,et al.  Superconductivity with extremely large upper critical fields in Nb$_{2}$Pd$_{0.81}$S$_{5}$ , 2013 .

[47]  Wu Yiqun,et al.  Reversible Resistance Switching Effect in Amorphous Ge1Sb4Te7 Thin Films without Phase Transformation , 2009 .

[48]  G. Snider,et al.  Self-organized computation with unreliable, memristive nanodevices , 2007 .

[49]  Jae Cheol Shin,et al.  Sub-100 nm Si nanowire and nano-sheet array formation by MacEtch using a non-lithographic InAs nanowire mask , 2012, Nanotechnology.

[50]  Shimeng Yu,et al.  Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory , 2011 .

[51]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[52]  Greg Snider,et al.  Instar and outstar learning with memristive nanodevices , 2011, Nanotechnology.

[53]  X. Miao,et al.  Ultrafast Synaptic Events in a Chalcogenide Memristor , 2013, Scientific Reports.

[54]  D. Querlioz,et al.  Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices , 2013, IEEE Transactions on Nanotechnology.

[55]  Jun Wang,et al.  Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays , 2013, Neural Networks.

[56]  Insung Kim,et al.  Low temperature solution-processed graphene oxide/Pr0.7Ca0.3MnO3 based resistive-memory device , 2011 .