Three-terminal magnetic tunnel junction synapse circuits showing spike-timing-dependent plasticity

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.

[1]  Wesley H. Brigner,et al.  SPICE-Only Model for Spin-Transfer Torque Domain Wall MTJ Logic , 2019, IEEE Transactions on Electron Devices.

[2]  Christopher H. Bennett,et al.  Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron , 2019, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[3]  Huichu Liu,et al.  Synchronous Circuit Design With Beyond-CMOS Magnetoelectric Spin–Orbit Devices Toward 100-mV Logic , 2019, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[4]  J. Yang,et al.  Emerging Memory Devices for Neuromorphic Computing , 2019, Advanced Materials Technologies.

[5]  Supriyo Datta,et al.  Composable Probabilistic Inference Networks Using MRAM-based Stochastic Neurons , 2018, ACM J. Emerg. Technol. Comput. Syst..

[6]  Joseph S. Friedman,et al.  Magnetic domain wall neuron with lateral inhibition , 2018, Journal of Applied Physics.

[7]  Hideo Ohno,et al.  Perspective: Spintronic synapse for artificial neural network , 2018, Journal of Applied Physics.

[8]  I. Radu,et al.  Chain of magnetic tunnel junctions as a spintronic memristor , 2018, Journal of Applied Physics.

[9]  Hyunsang Hwang,et al.  Reliable Multivalued Conductance States in TaO x Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis. , 2018, ACS applied materials & interfaces.

[10]  Kaushik Roy,et al.  Neuromorphic computing enabled by physics of electron spins: Prospects and perspectives , 2018 .

[11]  Seung Hwan Lee,et al.  Reservoir computing using dynamic memristors for temporal information processing , 2017, Nature Communications.

[12]  Kaushik Roy,et al.  Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing , 2017, ArXiv.

[13]  Park Byung-Gook,et al.  Integrate-and-Fire (I&F) Neuron Circuit Using Resistive-Switching Random Access Memory (RRAM) , 2017 .

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

[15]  D. Stevens,et al.  Simultaneous Membrane Capacitance Measurements and TIRF Microscopy to Study Granule Trafficking at Immune Synapses. , 2017, Methods in molecular biology.

[16]  Michael L. Dustin,et al.  The Immune Synapse , 2017, Methods in Molecular Biology.

[17]  M. Cecchini,et al.  Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease , 2016, Scientific Reports.

[18]  Damien Querlioz,et al.  Spintronic Nanodevices for Bioinspired Computing , 2016, Proceedings of the IEEE.

[19]  Hyunsoo Yang,et al.  Spin orbit torques and Dzyaloshinskii-Moriya interaction in dual-interfaced Co-Ni multilayers , 2016, Scientific Reports.

[20]  S. Yuasa,et al.  A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy , 2016, Scientific Reports.

[21]  Kaushik Roy,et al.  Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning , 2016, Scientific Reports.

[22]  Fabien Alibart,et al.  Exploiting the short-term to long-term plasticity transition in memristive nanodevice learning architectures , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[23]  Sarma B. K. Vrudhula,et al.  Demonstration of spike timing dependent plasticity in CBRAM devices with silicon neurons , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[24]  Youguang Zhang,et al.  Spin wave based synapse and neuron for ultra low power neuromorphic computation system , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[25]  Yu Wang,et al.  All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[26]  W. Gerstner,et al.  Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules , 2016, Front. Neural Circuits.

[27]  C. A. Ross,et al.  Logic circuit prototypes for three-terminal magnetic tunnel junctions with mobile domain walls , 2016, Nature Communications.

[28]  Kaushik Roy,et al.  Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[29]  Farnood Merrikh-Bayat,et al.  Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors , 2015, Scientific Reports.

[30]  Dmitri E. Nikonov,et al.  Spin-Orbit Logic with Magnetoelectric Nodes: A Scalable Charge Mediated Nonvolatile Spintronic Logic , 2015, 1512.05428.

[31]  Caroline A. Ross,et al.  Micromagnetic modeling of domain wall motion in sub-100-nm-wide wires with individual and periodic edge defects , 2015 .

[32]  Mahdi Kiani,et al.  Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs , 2015, Front. Neurosci..

[33]  Pabitra Nath,et al.  Ground and river water quality monitoring using a smartphone-based pH sensor , 2015 .

[34]  Yu Wang,et al.  Energy-efficient neuromorphic computation based on compound spin synapse with stochastic learning , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[35]  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.

[36]  Dmitri E. Nikonov,et al.  Benchmarking of Beyond-CMOS Exploratory Devices for Logic Integrated Circuits , 2015, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[37]  Dominique Vuillaume,et al.  Filamentary switching: synaptic plasticity through device volatility. , 2015, ACS nano.

[38]  Florentin Wörgötter,et al.  The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences , 2014, BMC Neuroscience.

[39]  Jeremy Hsu,et al.  IBM's new brain [News] , 2014 .

[40]  Wulfram Gerstner,et al.  Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition , 2014 .

[41]  Christian Tetzlaff,et al.  The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences , 2014, BMC Neuroscience.

[42]  F. García-Sánchez,et al.  The design and verification of MuMax3 , 2014, 1406.7635.

[43]  A. Brataas,et al.  Spin-orbit torques in action. , 2014, Nature nanotechnology.

[44]  J. Kim,et al.  Neuromorphic speech systems using advanced ReRAM-based synapse , 2013, 2013 IEEE International Electron Devices Meeting.

[45]  F. Zeng,et al.  Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system , 2013 .

[46]  Dmitri E. Nikonov,et al.  Overview of Beyond-CMOS Devices and a Uniform Methodology for Their Benchmarking , 2013, Proceedings of the IEEE.

[47]  Kaushik Roy,et al.  Spin based neuron-synapse module for ultra low power programmable computational networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[48]  C. Ross,et al.  Low Energy Magnetic Domain Wall Logic in Short, Narrow, Ferromagnetic Wires , 2012, IEEE Magnetics Letters.

[49]  Young Keun Kim,et al.  Effects of notch shape on the magnetic domain wall motion in nanowires with in-plane or perpendicular magnetic anisotropy , 2012 .

[50]  Jae-Eung Oh,et al.  Publisher’s Note: “Reduction of internal polarization fields in InGaN quantum wells by InGaN/AlGaN ultra-thin superlattice barriers with different indium” [J. Appl. Phys. 110, 123108 (2011)] , 2012 .

[51]  Olivier Bichler,et al.  Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction , 2011, 2011 International Electron Devices Meeting.

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

[53]  Yali Amit,et al.  Capacity analysis in multi-state synaptic models: a retrieval probability perspective , 2011, Journal of Computational Neuroscience.

[54]  Aristide Lemaître,et al.  Track heating study for current-induced domain wall motion experiments , 2010 .

[55]  Nicolas Brunel,et al.  Frontiers in Computational Neuroscience Computational Neuroscience , 2022 .

[56]  Gayle M. Wittenberg,et al.  Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules , 2010, Front. Comput. Neurosci..

[57]  J. Kotaleski,et al.  Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches , 2010, Nature Reviews Neuroscience.

[58]  Mathias Kläui,et al.  Selective domain wall depinning by localized Oersted fields and Joule heating , 2008 .

[59]  S. Parkin,et al.  Magnetic Domain-Wall Racetrack Memory , 2008, Science.

[60]  C. Rettner,et al.  Current-Controlled Magnetic Domain-Wall Nanowire Shift Register , 2008, Science.

[61]  Geoffrey S. D. Beach,et al.  Current-induced domain wall motion , 2008 .

[62]  Xiaolin Zhang,et al.  A Mathematical Model of a Neuron with Synapses based on Physiology , 2008 .

[63]  A. Fert,et al.  The emergence of spin electronics in data storage. , 2007, Nature materials.

[64]  T. Ono,et al.  Current-driven domain-wall motion in magnetic wires with asymmetric notches , 2005, cond-mat/0510404.

[65]  Shufeng Zhang,et al.  Current-driven domain-wall depinning , 2005 .

[66]  H. Shouval,et al.  Stochastic properties of synaptic transmission affect the shape of spike time-dependent plasticity curves. , 2005, Journal of neurophysiology.

[67]  James Sneyd,et al.  Tutorials in Mathematical Biosciences II , 2005 .

[68]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[69]  Florentin Wörgötter,et al.  Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics , 2003, NIPS.

[70]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[71]  M J Donahue,et al.  OOMMF User's Guide, Version 1.0 , 1999 .

[72]  C. Shatz.  The developing brain. , 1992, Scientific American.

[73]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[74]  J. Konorski.  Conditioned reflexes and neuron organization. , 1948 .