Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| _version_ | 1866915208836939776 |
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| author | Oberbauer, Felix Winkel, Tristan Joachim Böhnert, Tim Claro, Marcel S. Benetti, Luana Çaha, Ihsan Francis, Leonard Moradi, Farshad Ferreira, Ricardo Münzenberg, Markus Parvini, Tahereh Sadat |
| author_facet | Oberbauer, Felix Winkel, Tristan Joachim Böhnert, Tim Claro, Marcel S. Benetti, Luana Çaha, Ihsan Francis, Leonard Moradi, Farshad Ferreira, Ricardo Münzenberg, Markus Parvini, Tahereh Sadat |
| contents | Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing Oberbauer, Felix Winkel, Tristan Joachim Böhnert, Tim Claro, Marcel S. Benetti, Luana Çaha, Ihsan Francis, Leonard Moradi, Farshad Ferreira, Ricardo Münzenberg, Markus Parvini, Tahereh Sadat Optics Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures. |
| title | Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing |
| topic | Optics |
| url | https://arxiv.org/abs/2501.00813 |