CAREER: Toward energy-efficient bio-inspired magnonic processing with nanomagnetic arrays

该项目由材料研究部的凝聚态物理项目和刺激竞争性研究的既定计划（EPSCoR）共同资助。非技术描述：人工智能（AI）的蓬勃发展使强大的工具和应用程序的创建成为可能，这在几年前是不可想象的。然而，随着人工智能和机器学习的快速发展，相关的能源成本和温室气体排放正在爆炸式增长。这种不可持续的趋势可能会阻止社会实现净零未来。因此，迫切需要低功耗计算和人工智能处理的范式转变。该项目通过提供有关磁性纳米结构中基本激发的基础知识和技术来应对这一历史性挑战，以创建一个从大脑中获得灵感的变革性计算方案。当前的计算架构依赖于数据在单独的存储器和处理器之间的不断穿梭，这是非常低效的。此外，当前的计算平台基于电子电荷的流动，导致焦耳加热形式的耗散。为了规避这些问题，研究小组的目标是利用相互作用的纳米磁体网络中的动态进行生物启发处理，方法是：A）缓解处理器-存储器信息传输瓶颈; B）基于波而不是移动电荷来传输和处理数据。该项目的教育推广部分促进公众更多地参与科学研究。教育目标旨在参与波物理学的多层次学习：1）为终身学习者开发新课程，2）通过创建可访问的波演示，为与不同学生群体合作的学校教师开发培训计划。技术描述：自旋波和它们的量子-磁振子-是磁性系统的基本集体激发。磁振子可以在不移动电荷的情况下传输和处理信息，因此，磁振子设备的耗散性可能比电子设备小。纳米磁性阵列类似于神经网络，在同一个单元中提供记忆和计算能力：它们可以保留存储在其磁化方向上的信息，并通过磁振子激发来处理这些信息。该项目通过确定相互作用的纳米磁体的光刻定义阵列中的磁振子属性，探索了纳米磁学中的几条路径，其中信息通过磁振子-磁振子耦合在纳米磁性“神经元”之间传递，充当“突触”。因此，需要进一步了解纳米磁体网络中的动态模式耦合。该项目解决了强相互作用磁网络的基本理解的关键知识差距。四个具体目标是：1）控制纳米磁体二维阵列中的磁振子，2）操纵磁振子-磁振子相互作用，3）理解磁性纳米结构中的非线性动力学，4）实验实现下一代神经形态磁振子计算概念。纳米磁性网络的制造，电子束光刻，电子束蒸发，和剥离和研究，通过光学，电学和微波方法。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project is jointly funded by the Condensed Matter Physics program of the Division of Materials Research and Established Program to Stimulate Competitive Research (EPSCoR).Nontechnical description:The surging development of artificial intelligence (AI) enables the creation of powerful tools and applications that were unimaginable just a few years ago. However, as AI and machine learning rapidly grow, the associated energy costs and greenhouse emissions are exploding. This massively unsustainable trend threatens to prevent society from achieving a net-zero future. Hence, a paradigm shift for low-power computing and AI processing is urgently needed. This project contributes to tackling this historic challenge by delivering foundational knowledge and technology concerning the fundamental excitations in magnetic nanostructures to create a transformative computing scheme taking inspiration from the brain. Current computing architectures rely on a constant shuttling of data between separate memory and processor, which is highly inefficient. Furthermore, current computing platforms are based on the flow of electronic charges, leading to dissipation in the form of Joule heating. To circumvent these problems, the research team aims to harness the dynamics in networks of interacting nanomagnets for bio-inspired processing by A) alleviating the processor-memory information transfer bottleneck and B) enabling the transport and processing of data based on waves rather than moving charges. The educational outreach component of this project fosters increased public participation in scientific research. The educational goals are designed to engage multiple levels of learning in wave physics: 1) a new course is developed for lifelong learners and 2) training programs are developed for schoolteachers working with a diverse student population by creating an accessible wave demonstration. Technical description:Spin waves, and their quanta - magnons - are the fundamental collective excitations of a magnetic system. Magnons can transport and process information without moving charges, and hence, magnonic devices can be less dissipative than their electronic counterparts. Nanomagnetic arrays are similar to neural networks, providing memory and computing abilities in the same unit: they can retain information stored in their magnetization orientation and process that information by magnonic excitations. This project explores several paths in nanomagnonics by determining the magnon properties in lithographically defined arrays of interacting nanomagnets, where information is passed between nanomagnetic ‘neurons’ via magnon-magnon coupling acting as ‘synapses’. Therefore, advances are needed to understand dynamic mode coupling in networks of nanomagnets. This project addresses critical knowledge gaps in the fundamental understanding of strongly interacting magnetic networks. The four specific aims are 1) controlling magnons in two-dimensional arrays of nanomagnets, 2) manipulating magnon-magnon interactions, and 3) understand nonlinear dynamics in magnetic nanostructures to 4) experimentally realize the next-generation of neuromorphic magnonic computing concepts. The nanomagnetic networks are fabricated by electron-beam lithography, electron-beam evaporation, and lift-off and studied by optical, electrical, and microwave methods. The experimental investigations are supported by micromagnetic modeling.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.