Tunnel junction based memristors as artificial synapses

We prepared magnesia, tantalum oxide, and barium titanate based tunnel junction structures and investigated their memristive properties. The low amplitudes of the resistance change in these types of junctions are the major obstacle for their use. Here, we increased the amplitude of the resistance change from 10% up to 100%. Utilizing the memristive properties, we looked into the use of the junction structures as artificial synapses. We observed analogs of long-term potentiation, long-term depression and spike-time dependent plasticity in these simple two terminal devices. Finally, we suggest a possible pathway of these devices toward their integration in neuromorphic systems for storing analog synaptic weights and supporting the implementation of biologically plausible learning mechanisms.

[1]  A. Hippel Ferroelectricity, Domain Structure, and Phase Transitions of Barium Titanate , 1950 .

[2]  W. E. Beadle,et al.  Switching properties of thin Nio films , 1964 .

[3]  W. Brinkman,et al.  Tunneling Conductance of Asymmetrical Barriers , 1970 .

[4]  Yoshihiro Ishibashi,et al.  Note on Ferroelectric Domain Switching , 1971 .

[5]  M. Julliere Tunneling between ferromagnetic films , 1975 .

[6]  G. M. Rose,et al.  Induction of hippocampal long-term potentiation using physiologically patterned stimulation , 1986, Neuroscience Letters.

[7]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[8]  S. Tam,et al.  An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.

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

[10]  J. Brant Arseneau,et al.  VLSI and neural systems , 1990 .

[11]  Sung Wook Park,et al.  Effects of oxidation conditions on the properties of tantalum oxide films on silicon substrates , 1992 .

[12]  Yoshihiro Ishibashi,et al.  Study on D-E Hysteresis Loop of TGS Based on the Avrami-Type Model , 1994 .

[13]  Sang-Sub Kim,et al.  Structural characterization of epitaxial BaTiO3 thin films grown by sputter deposition on MgO(100) , 1995 .

[14]  I. Chen,et al.  Fatigue of Pb(Zr0.53Ti0.47)O3 ferroelectric thin films , 1998 .

[15]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[16]  Jagadeesh S. Moodera,et al.  Spin polarized tunneling in ferromagnetic junctions , 1999 .

[17]  Davide Badoni,et al.  Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation , 2000, Neural Computation.

[18]  M. Hehn,et al.  Tantalum oxide as an alternative low height tunnel barrier in magnetic junctions , 2001 .

[19]  J. Gilman,et al.  Nanotechnology , 2001 .

[20]  Paul E. Hasler,et al.  Biological learning modeled in an adaptive floating-gate system , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[21]  A. Tagantsev,et al.  Non-Kolmogorov-Avrami switching kinetics in ferroelectric thin films , 2002 .

[22]  H. Kubota,et al.  Size dependence of switching field of magnetic tunnel junctions down to 50 nm scale , 2003 .

[23]  Thomas Mikolajick,et al.  Material Aspects in Emerging Nonvolatile Memories , 2004 .

[24]  G. Reiss,et al.  Aluminum oxidation by a remote electron cyclotron resonance plasma in magnetic tunnel junctions , 2003 .

[25]  Vittorio Dante,et al.  A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory , 2003, IEEE Trans. Neural Networks.

[26]  Y. Huai,et al.  Observation of spin-transfer switching in deep submicron-sized and low-resistance magnetic tunnel junctions , 2004, cond-mat/0504486.

[27]  M. Fiebig Revival of the magnetoelectric effect , 2005 .

[28]  E. Tsymbal,et al.  Applied physics. Tunneling across a ferroelectric. , 2006, Science.

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

[30]  Gert Cauwenberghs,et al.  Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses , 2007, IEEE Transactions on Neural Networks.

[31]  Walter Senn,et al.  Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.

[32]  Jiyoung Kim,et al.  Random and localized resistive switching observation in Pt/NiO/Pt , 2007 .

[33]  Xinman Chen,et al.  Resistive switching behavior of Pt/Mg0.2Zn0.8O/Pt devices for nonvolatile memory applications , 2008 .

[34]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[35]  S. Takahashi,et al.  Lower-current and fast switching of a perpendicular TMR for high speed and high density spin-transfer-torque MRAM , 2008, 2008 IEEE International Electron Devices Meeting.

[36]  G. Reiss,et al.  Direct imaging of the structural change generated by dielectric breakdown in MgO based magnetic tunnel junctions , 2008, 0806.2028.

[37]  H. N. Lee,et al.  Nonlinear dynamics of domain-wall propagation in epitaxial ferroelectric thin films. , 2009, Physical review letters.

[38]  Mario Pannunzi,et al.  Classification of Correlated Patterns with a Configurable Analog VLSI Neural Network of Spiking Neurons and Self-Regulating Plastic Synapses , 2009, Neural Computation.

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

[40]  Giacomo Indiveri,et al.  Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[41]  Wei Wu,et al.  A hybrid nanomemristor/transistor logic circuit capable of self-programming , 2009, Proceedings of the National Academy of Sciences.

[42]  Andy Thomas,et al.  Current induced resistance change of magnetic tunnel junctions with ultra-thin MgO tunnel barriers , 2008, 0807.4422.

[43]  V. Garcia,et al.  Giant tunnel electroresistance for non-destructive readout of ferroelectric states , 2009, Nature.

[44]  G. Reiss,et al.  Electric breakdown in ultrathin MgO tunnel barrier junctions for spin-transfer torque switching , 2009, 0907.3579.

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

[46]  P. Krzysteczko,et al.  Memristive switching of MgO based magnetic tunnel junctions , 2009, 0907.3684.

[47]  Ralph Etienne-Cummings,et al.  A CMOS switched capacitor implementation of the Mihalas-Niebur neuron , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[48]  Bernabé Linares-Barranco,et al.  Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses , 2009 .

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

[50]  Rainer Waser,et al.  Complementary resistive switches for passive nanocrossbar memories. , 2010, Nature materials.

[51]  Paul E. Hasler,et al.  Floating gate synapses with spike time dependent plasticity , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

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

[53]  P Fons,et al.  Interfacial phase-change memory. , 2011, Nature nanotechnology.

[54]  R. Williams,et al.  Sub-nanosecond switching of a tantalum oxide memristor , 2011, Nanotechnology.

[55]  Shubha Ramakrishnan,et al.  Floating Gate Synapses With , 2011 .

[56]  A. Thomas,et al.  Improved reliability of magnetic field programmable gate arrays through the use of memristive tunnel junctions , 2011 .

[57]  Hao Yan,et al.  Programmable nanowire circuits for nanoprocessors , 2011, Nature.

[58]  M. Kozicki,et al.  Electrochemical metallization memories—fundamentals, applications, prospects , 2011, Nanotechnology.

[59]  Yuriy V. Pershin,et al.  Memory effects in complex materials and nanoscale systems , 2010, 1011.3053.

[60]  Kinam Kim,et al.  A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures. , 2011, Nature materials.

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

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

[63]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[64]  Paul E. Hasler,et al.  STDP-enabled learning on a reconfigurable neuromorphic platform , 2013, 2013 European Conference on Circuit Theory and Design (ECCTD).

[65]  A. Thomas,et al.  Memristor-based neural networks , 2013 .

[66]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[67]  Chung Lam,et al.  Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array , 2014, Front. Neurosci..

[68]  T. Mikolajick,et al.  Exploiting Memristive BiFeO3 Bilayer Structures for Compact Sequential Logics , 2014 .

[69]  Leon O. Chua,et al.  If it’s pinched it’s a memristor , 2014 .

[70]  Chiara Bartolozzi,et al.  Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems , 2014, Proceedings of the IEEE.

[71]  F. Zeng,et al.  Recent progress in resistive random access memories: Materials, switching mechanisms, and performance , 2014 .

[72]  René Schüffny,et al.  Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS , 2014, Front. Neurosci..

[73]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.