RII Track-4:NSF: Spin-orbitronics in quantum materials for energy-efficient neuromorphic computing

RII Track-4：NSF：量子材料中的自旋轨道电子学用于节能神经形态计算

• 批准号: 2229498
• 负责人: Fei Xue
• 金额: $ 26.43万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229498&HistoricalAwards=false
• 关键词: RII; Track; NSF; Spin; orbitronics

The rapid development of artificial intelligence relies on modern computing technologies. However, the existing von Neumann-based technology suffers from high energy consumption for data-intensive tasks. This high energy consumption may limit the future adoption of artificial intelligence technologies. Inspired by the biological brain, neuromorphic computing provides the promising technological capability to tackle this challenge by creating superior energy-efficient hardware for information processing. This research project exploits the superior nonlinear nonvolatile spin-related responses in quantum materials. The key challenge to implementing neuromorphic computing is to create artificial neurons and synapses with great energy efficiency. Spintronics study the interplay between electron spin transport and charge transport, so they naturally couple electronic and magnetic configurations, thus offering non-volatility and nonlinearity. Nonvolatile spintronics memory devices can emulate artificial synapses and nonlinear spin-torque nano-oscillators can emulate artificial neurons. The proposed research activities will provide a unique opportunity for students at the University of Alabama at Birmingham to gain computation skills and collaborate with distinguished scientists at the National Institute of Standards and Technology (NIST). Additionally, as a part of this project, the PI will develop a new advanced physics course about artificial intelligence and how to implement it with physical devices. This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant Professor and training for a postdoctoral fellow at the University of Alabama at Birmingham (UAB). Brain-inspired neuromorphic computing offers appealing technology capability for artificial intelligence applications. Spintronics devices, which couple electronic and magnetic configurations, can emulate synapses and neurons in an energy-efficient compact manner. The magnetic random-access memory can serve as the synapses and the spin-torque nano-oscillators can serve as the neurons. The quantum materials provide additional appealing features including more efficient control of magnetization and new functionalities due to the coupling of spin, orbital, and magnetization degrees of freedom. Understanding and modeling microscopic mechanisms with state-of-art first-principles methods of neuromorphic spintronics with quantum materials is the main goal of this proposal. The two main thrusts focus on utilizing the superior spin-orbitronics properties in quantum materials for artificial synapses and neurons. By collaborating with the experts at the NIST, the project will apply first-principles methods to calculate the band structures and spin dynamics in spin-orbit coupled quantum materials. This allows for the understanding of the microscopic mechanism of spin-orbit torque switching and dynamics and provides a pathway to improve the figure of merits. Project outcomes will also include illustration of how to utilize these nonlinear nonvolatile properties of spintronics devices into emulating neurons and synapses for neuromorphic computing. The proposed work will provide the physics foundation for implementing neuromorphic spintronics devices with emerging quantum materials such as two-dimensional materials and topological materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能的快速发展依赖于现代计算技术。但是，现有的基于冯·诺伊曼（Von Neumann）的技术在数据密集型任务中遭受了高能量消耗。这种高能消耗可能会限制人工智能技术的未来采用。受生物大脑的启发，神经形态计算提供了有希望的技术能力，可以通过创建用于信息处理的卓越节能硬件来应对这一挑战。该研究项目利用了量子材料中的上非线性非挥发性反应。实施神经形态计算的主要挑战是创建具有巨大能量效率的人工神经元和突触。 SpinTronics研究了电子自旋传输和电荷传输之间的相互作用，因此它们自然磨合电子和磁性构型，从而提供非挥发性和非线性。非挥发性的记忆设备可以效仿人造突触，而非线性自旋刺激纳米振荡器可以效仿人造神经元。拟议的研究活动将为伯明翰阿拉巴马大学的学生提供一个独特的机会，以获得计算技能，并与国家标准技术研究所（NIST）的杰出科学家合作。此外，作为该项目的一部分，PI将开发有关人工智能以及如何使用物理设备实施的新的高级物理课程。该研究基础设施改进Track-4 Epscor Research Fellows（RII Track-4）项目将为伯明翰伯明翰大学（UAB）的一名博士后研究员提供助理教授和培训。脑启发的神经形态计算为人工智能应用提供了吸引力的技术能力。旋转电子和磁性构型的SpinTronics设备可以以节能的紧凑方式模仿突触和神经元。磁随机记忆可以用作突触，旋转刺激纳米振荡器可以用作神经元。由于自旋，轨道和磁化自由度的耦合，量子材料提供了其他吸引人的特征，包括对磁化的更有效控制和新功能。使用量子材料的神经形态旋转型的最先进的第一原理方法来理解和建模微观机制是该提议的主要目标。这两个主要的推力着重于利用人工突触和神经元的量子材料中的上自旋能力特性。通过与NIST的专家合作，该项目将采用第一原理方法来计算旋转轨道耦合量子材料中的带结构和旋转动力学。这允许理解自旋轨道扭矩开关和动力学的显微镜机制，并提供了提高优点图的途径。项目结果还将包括如何利用Spintronics设备的这些非线性非易失性特性来模拟神经元和神经形态计算的突触。拟议的工作将提供物理基金会，以使用新兴的量子材料（例如二维材料和拓扑材料）实施神经形态的旋转型设备。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Angular dependence of spin-orbit torque in monolayer Fe3GeTe2

• DOI: 10.1103/physrevb.108.144422
• 发表时间: 2023-07
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: F. Xue;M. Stiles;P. Haney