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

职业：利用纳米磁性阵列实现节能的仿生磁力处理

• 批准号: 2339475
• 负责人: Matthias Jungfleisch
• 金额: $ 79.88万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339475&HistoricalAwards=false
• 关键词: CAREER; Toward; energy; efficient; bio

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.

该项目由材料研究部凝聚态物理项目和刺激竞争研究既定项目（EPSCoR）共同资助。非技术描述：人工智能（AI）的蓬勃发展使得强大的工具和应用程序的创建成为可能。然而，随着人工智能和机器学习的迅速发展，相关的能源成本和温室气体排放正在爆炸式增长，这种严重不可持续的趋势可能会阻碍社会实现这一目标。因此，迫切需要低功耗计算和人工智能处理的范式转变，通过提供有关磁性纳米结构基本激发的基础知识和技术来创建变革性计算方案，以应对这一历史性挑战。当前的计算架构依赖于在单独的存储器和处理器之间不断地传输数据，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，导致以焦耳热的形式耗散。到为了规避这些问题，研究团队的目标是利用相互作用的纳米磁体网络中的动力学进行仿生处理，方法是：A）减轻处理器-内存信息传输瓶颈；B）实现基于波而不是移动电荷的数据传输和处理该项目的教育推广部分促进公众更多地参与科学研究。教育目标旨在促进波物理学的多层次学习：1）为终身学习者开发新课程，2）为在职教师开发培训计划。与一个通过创建易于理解的波移动演示来吸引不同的学生群体。 技术描述：自旋波及其量子（磁振子）是磁系统的基本集体激发，磁振子可以在不带电荷的情况下传输和处理信息，因此磁振子设备可以更少。纳米磁性阵列与神经网络类似，在同一单元中提供存储和计算能力：它们可以保留存储在磁化方向上的信息，并通过磁力激励来处理该信息。通过确定光刻定义的相互作用纳米磁体阵列中的磁振子特性来研究纳米磁振子学中的路径，其中信息通过充当“突触”的磁振子-磁振子耦合在纳米磁性“神经元”之间传递。因此，需要取得进展来理解纳米磁体网络中的动态模式耦合。该项目解决了强磁网络基本理解中相互作用的关键知识差距，四个具体目标是 1) 控制二维纳米磁体阵列中的磁振子。操纵磁振子-磁振子相互作用，3) 了解磁性纳米结构中的非线性动力学，4) 通过实验实现下一代神经形态磁振子计算概念。纳米磁性网络是通过电子束光刻、电子束蒸发和剥离制造的。并通过光学、电学和微波方法进行研究，实验研究得到微磁模型的支持。该奖项反映了 NSF 的法定使命，并通过使用评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。