Field Free Spin-Orbit Torque Controlled Synapse and Stochastic Neuron Devices for Spintronic Boltzmann Neural Networks
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912632594759680 |
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| author | Lone, Aijaz H. Tang, Meng Florica, Camelia He, Bin Xu, Jingkai Zhang, Xixiang Setti, Gianluca |
| author_facet | Lone, Aijaz H. Tang, Meng Florica, Camelia He, Bin Xu, Jingkai Zhang, Xixiang Setti, Gianluca |
| contents | Spintronics offers a promising approach to energy efficient neuromorphic computing by integrating the functionalities of synapses and neurons within a single platform. A major challenge, however, is achieving field-free spin orbit torque SOT control over both synaptic and neuronal devices using an industry-adopted spintronic materials stack. In this study, we present field-free SOT spintronic synapses utilizing a CoFeB ferromagnetic thin film system, where asymmetrical device design and specifically added lateral notches in the CoFeB thin film facilitate effective domain wall DW nucleation, movement, and pinning and depinning. This method yields multiple analog, nonvolatile resistance states with enhanced linearity and symmetry, resulting in programmable and stable synaptic weights. We provide a systematic measurement approach to improve the linearity and symmetry of the synapses. Additionally, we demonstrate nanoscale magnetic tunnel junctions MTJs that function as SOT-driven stochastic neurons, exhibiting current-tunable, Boltzmann-like probabilistic switching behavior, which provides an intrinsic in-hardware Gibbs sampling capability. By integrating these synapses and neurons into a Boltzmann machine complemented by a classifier layer, we achieve recognition accuracies greater than 98 percent on the MNIST dataset and 86 percent on Fashion MNIST. This work establishes a framework for field free synaptic and neuronal devices, setting the stage for practical, materials-compatible, and all spintronic neuromorphic computing hardware. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_05616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Field Free Spin-Orbit Torque Controlled Synapse and Stochastic Neuron Devices for Spintronic Boltzmann Neural Networks Lone, Aijaz H. Tang, Meng Florica, Camelia He, Bin Xu, Jingkai Zhang, Xixiang Setti, Gianluca Applied Physics Disordered Systems and Neural Networks Materials Science Spintronics offers a promising approach to energy efficient neuromorphic computing by integrating the functionalities of synapses and neurons within a single platform. A major challenge, however, is achieving field-free spin orbit torque SOT control over both synaptic and neuronal devices using an industry-adopted spintronic materials stack. In this study, we present field-free SOT spintronic synapses utilizing a CoFeB ferromagnetic thin film system, where asymmetrical device design and specifically added lateral notches in the CoFeB thin film facilitate effective domain wall DW nucleation, movement, and pinning and depinning. This method yields multiple analog, nonvolatile resistance states with enhanced linearity and symmetry, resulting in programmable and stable synaptic weights. We provide a systematic measurement approach to improve the linearity and symmetry of the synapses. Additionally, we demonstrate nanoscale magnetic tunnel junctions MTJs that function as SOT-driven stochastic neurons, exhibiting current-tunable, Boltzmann-like probabilistic switching behavior, which provides an intrinsic in-hardware Gibbs sampling capability. By integrating these synapses and neurons into a Boltzmann machine complemented by a classifier layer, we achieve recognition accuracies greater than 98 percent on the MNIST dataset and 86 percent on Fashion MNIST. This work establishes a framework for field free synaptic and neuronal devices, setting the stage for practical, materials-compatible, and all spintronic neuromorphic computing hardware. |
| title | Field Free Spin-Orbit Torque Controlled Synapse and Stochastic Neuron Devices for Spintronic Boltzmann Neural Networks |
| topic | Applied Physics Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2510.05616 |