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的凝聚态物理计划共同资助，并建立了刺激竞争研究的计划（EPSCOR）。非技术描述：人工智能的迅速发展（AI）可以创建强大的工具和应用程序，而这些工具和应用程序在几年前就无法想象。但是，随着AI和机器学习的迅速增长，相关的能源成本和温室排放正在爆炸。这种极大的不可持续趋势威胁要阻止社会实现零净的未来。因此，迫切需要进行低功率计算和AI处理的范式转移。该项目通过提供有关磁性纳米结构中基本令人兴奋的基础知识和技术来创建一种变革性计算方案，从而从大脑中汲取灵感，从而有助于应对这一历史性挑战。当前的计算体系结构依赖于单独的内存和处理器之间的数据持续穿梭，这是高效效率的。此外，当前的计算平台基于电子电荷的流动，导致以焦耳加热形式耗散。为了解决这些问题，研究小组的目标是通过a）减轻处理器内存信息传输瓶颈和b）基于波浪而不是移动指控来实现数据传输和处理数据的网络来利用纳米磁体相互作用的网络进行生物启发的处理。该项目的教育外展部分促进了公众参与科学研究。教育目标旨在在波浪物理学中吸引多个学习层次的学习：1）为终身学习者开发了一门新课程，而2）培训计划是为与潜水员学生人数一起创建可访问的波浪示范的学校老师开发的。技术描述：旋转波及其量子 - 镁质 - 是磁系统的基本集体兴奋。镁可以在不移动电荷的情况下传输和处理信息，因此，磁性设备的耗散性比电子对应物少。纳米磁阵列与神经网络相似，在同一单元中提供记忆和计算能力：它们可以保留在磁化方向和过程中存储的信息，并通过磁兴奋进行信息。该项目通过确定相互作用的纳米磁体的光刻定义的阵列中的磁体特性来探索纳米磁学的几个路径，在该纳米磁性纳米磁体的相互作用阵列中，通过Magnet-Magnon耦合在纳米磁性“神经元”之间传递信息。因此，需要进步来了解纳米磁网络网络中的动态模式耦合。该项目解决了对强烈相互作用磁网络的基本理解中的关键知识差距。这四个具体目的是1）在二维纳米磁体阵列中控制磁铁，2）操纵磁杆磁通相互作用，3）3）了解磁性纳米结构中的非线性动力学4）实验实现了神经形态磁计算概念的下一代。纳米磁网络是通过电子束光刻，电子梁蒸发以及通过光学，电气和微波方法提升和研究的。实验研究得到了微磁建模的支持。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。