دورية أكاديمية

Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

التفاصيل البيبلوغرافية
العنوان: Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware
المؤلفون: Long Liu, Di Wang, Dandan Wang, Yan Sun, Huai Lin, Xiliang Gong, Yifan Zhang, Ruifeng Tang, Zhihong Mai, Zhipeng Hou, Yumeng Yang, Peng Li, Lan Wang, Qing Luo, Ling Li, Guozhong Xing, Ming Liu
المصدر: Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
بيانات النشر: Nature Portfolio, 2024.
سنة النشر: 2024
المجموعة: LCC:Science
مصطلحات موضوعية: Science
الوصف: Abstract We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2041-1723
Relation: https://doaj.org/toc/2041-1723
DOI: 10.1038/s41467-024-48631-4
URL الوصول: https://doaj.org/article/52c5fad8428e4fc68256c5a9040f9ad4
رقم الأكسشن: edsdoj.52c5fad8428e4fc68256c5a9040f9ad4
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20411723
DOI:10.1038/s41467-024-48631-4