Demonstration of Synaptic Behavior in a Heavy-Metal-Ferromagnetic-Metal-Oxide-Heterostructure-Based Spintronic Device for On-Chip Learning in Crossbar-Array-Based Neural Networks

[1]  Otitoaleke G. Akinola,et al.  Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing , 2022 .

[2]  Hyunsoo Yang,et al.  Spintronic Integrate-Fire-Reset Neuron with Stochasticity for Neuromorphic Computing. , 2022, Nano letters.

[3]  Congli He,et al.  Perpendicular Magnetization Switching Driven by Spin‐Orbit Torque for Artificial Synapses in Epitaxial Pt‐Based Multilayers , 2022, Advanced Electronic Materials.

[4]  S. Piramanayagam,et al.  Efficient spin-orbit torque magnetization switching by reducing domain nucleation energy , 2022, Journal of Magnetism and Magnetic Materials.

[5]  Shishen Yan,et al.  Memristive switching by bulk spin–orbit torque in symmetry-broken ferromagnetic films , 2022, Applied Physics Letters.

[6]  Tao Li,et al.  Synaptic 1/f noise injection for overfitting suppression in hardware neural networks , 2022, Neuromorph. Comput. Eng..

[7]  Divya Kaushik,et al.  On-chip learning of a domain-wall-synapse-crossbar-array-based convolutional neural network , 2022, Neuromorph. Comput. Eng..

[8]  Weisheng Zhao,et al.  High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network , 2022, Advanced science.

[9]  Seung Keun Yoon,et al.  A crossbar array of magnetoresistive memory devices for in-memory computing , 2022, Nature.

[10]  S. N. Piramanayagam,et al.  Emulation of Synaptic Plasticity on Cobalt based Synaptic Transistor for Neuromorphic Computing , 2021, ACS applied materials & interfaces.

[11]  Jae-Min Lee,et al.  Edge AI prospect using the NeuroEdge computing system: Introducing a novel neuromorphic technology , 2021, ICT Express.

[12]  Gouhei Tanaka,et al.  2022 roadmap on neuromorphic computing and engineering , 2021, Neuromorph. Comput. Eng..

[13]  Otitoaleke G. Akinola,et al.  Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing , 2020, 2010.13879.

[14]  Divya Kaushik,et al.  Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar neural network for scalable on-chip learning , 2020, Nanotechnology.

[15]  Chenfei Hu,et al.  Benchmarking of Spin–Orbit Torque Switching Efficiency in Pt Alloys , 2020, Advanced Quantum Technologies.

[16]  M. Stiles,et al.  Neuromorphic spintronics , 2020, Nature Electronics.

[17]  J. Åkerman,et al.  Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing , 2019, Nature Nanotechnology.

[18]  Upasana Sahu,et al.  Spike time dependent plasticity (STDP) enabled learning in spiking neural networks using domain wall based synapses and neurons , 2019, AIP Advances.

[19]  Upasana Sahu,et al.  Comparing domain wall synapse with other Non Volatile Memory devices for on-chip learning in Analog Hardware Neural Network , 2019, AIP Advances.

[20]  Joseph S. Friedman,et al.  Three-terminal magnetic tunnel junction synapse circuits showing spike-timing-dependent plasticity , 2019, Journal of Physics D: Applied Physics.

[21]  Xiaochen Peng,et al.  MLP+NeuroSimV3.0: Improving On-chip Learning Performance with Device to Algorithm Optimizations , 2019, ICONS.

[22]  Simone Finizio,et al.  Magnetic skyrmion artificial synapse for neuromorphic computing , 2019, ArXiv.

[23]  H. Ohno,et al.  Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin–Orbit Torque Switching , 2019, Advanced materials.

[24]  Jeongmin Hong,et al.  A Spin–Orbit‐Torque Memristive Device , 2019, Advanced Electronic Materials.

[25]  Utkarsh Saxena,et al.  On-chip learning for domain wall synapse based Fully Connected Neural Network , 2018, Journal of Magnetism and Magnetic Materials.

[26]  Abu Sebastian,et al.  Tutorial: Brain-inspired computing using phase-change memory devices , 2018, Journal of Applied Physics.

[27]  H. Zeng,et al.  Giant antidamping orbital torque originating from the orbital Rashba-Edelstein effect in ferromagnetic heterostructures , 2018, Nature Communications.

[28]  J. Yang,et al.  Efficient and self-adaptive in-situ learning in multilayer memristor neural networks , 2018, Nature Communications.

[29]  Yan Zhou,et al.  Magnetic skyrmion-based synaptic devices , 2016, Nanotechnology.

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

[31]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[32]  Jeffrey Bokor,et al.  Deterministic Domain Wall Motion Orthogonal To Current Flow Due To Spin Orbit Torque , 2014, Scientific Reports.

[33]  S. Parkin,et al.  Chiral spin torque at magnetic domain walls. , 2013, Nature nanotechnology.

[34]  G. Beach,et al.  Current-driven dynamics of chiral ferromagnetic domain walls. , 2013, Nature materials.

[35]  D. Ralph,et al.  Spin-Torque Switching with the Giant Spin Hall Effect of Tantalum , 2012, Science.

[36]  S. Bandiera,et al.  Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection , 2011, Nature.

[37]  H. Ohno,et al.  A perpendicular-anisotropy CoFeB-MgO magnetic tunnel junction. , 2010, Nature materials.

[38]  Mt Johnson,et al.  Magnetic anisotropy in metallic multilayers , 1996 .

[39]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

[40]  L. You,et al.  Spin Hall effect clocking of nanomagnetic logic without a magnetic field. , 2014, Nature nanotechnology.

[41]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.