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

人工智能的快速发展依赖于现代计算技术。然而，现有的基于冯诺依曼的技术遭受高能耗的数据密集型任务。这种高能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，神经形态计算通过创建用于信息处理的上级节能硬件来提供有前途的技术能力来应对这一挑战。本研究计划利用量子材料中的上级非线性非挥发自旋相关响应。实现神经形态计算的关键挑战是创建具有高能效的人工神经元和突触。自旋电子学研究电子自旋输运和电荷输运之间的相互作用，因此它们自然地耦合电子和磁性配置，从而提供非挥发性和非线性。非易失性自旋电子学存储器设备可以模拟人工突触，并且非线性自旋扭矩纳米振荡器可以模拟人工神经元。拟议的研究活动将为伯明翰亚拉巴马大学的学生提供一个独特的机会，以获得计算技能，并与美国国家标准与技术研究所（NIST）的杰出科学家合作。此外，作为该项目的一部分，PI将开发一个新的关于人工智能的高级物理课程，以及如何用物理设备实现它。这个研究基础设施改善轨道-4 EPSCoR研究员（RII轨道-4）项目将提供奖学金的助理教授和培训的博士后研究员在伯明翰（UAB）的亚拉巴马大学。脑启发的神经形态计算为人工智能应用提供了吸引人的技术能力。自旋电子学设备，耦合电子和磁配置，可以模拟突触和神经元在一个节能紧凑的方式。磁随机存取存储器可以充当突触，自旋扭矩纳米振荡器可以充当神经元。量子材料提供了额外的吸引人的功能，包括更有效地控制磁化和新的功能，由于自旋，轨道和磁化自由度的耦合。理解和建模的微观机制与国家的最先进的第一性原理方法的神经形态自旋电子学与量子材料是这个建议的主要目标。这两个主要的推动力集中在利用量子材料中的上级自旋-轨道电子学特性来制造人工突触和神经元。通过与NIST的专家合作，该项目将应用第一性原理方法来计算自旋轨道耦合量子材料的能带结构和自旋动力学。这使得理解的微观机制的自旋-轨道转矩开关和动力学，并提供了一种途径，以提高的数字的优点。项目成果还将包括如何利用自旋电子器件的这些非线性非易失性特性来模拟神经元和突触进行神经形态计算的说明。这项工作将为使用新兴量子材料（如二维材料和拓扑材料）实现神经形态自旋电子学器件提供物理基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.