Taming complexity in computational electromagnetism: a model order reduction approach

控制计算电磁学的复杂性：模型降阶方法

• 批准号: RGPIN-2019-05060
• 负责人: Triverio, Piero
• 金额: $ 2.4万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715028
• 关键词: Taming; complexity; computational; electromagnetism; model

Electromagnetic phenomena play a critical role inside the integrated circuits (ICs) and antennas that empower many electronic systems around us, including smartphones, computers, and data centers. Behind the scenes, electromagnetism also plays a crucial role in emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). It is estimated that, by 2020, 20 billion IoT devices will be connected to the Internet. Electromagnetic waves, and significant advancements in antenna technology, will be the only way to connect the myriad of future IoT devices to the Internet, due to the prohibitive cost of terrestrial infrastructure. Metasurfaces are a new type of antennas which is ideally suited to respond to soaring IoT needs. Unfortunately, metasurfaces are extremely complex. Since no existing electromagnetic simulator can handle their complexity, engineers are currently missing one of the most critical tools for metasurface design. Similar challenges are faced by microelectronic companies such as AMD, which extensively rely on electromagnetic simulators to design new ICs, including the hardware accelerators of neural networks that are increasingly needed by advanced AI applications. The significant gap between what electromagnetic solvers can do and what designers need is a major issue for both industry and academia. The proposed research aims to fill this gap by laying down the theoretical and algorithmic foundations of an electromagnetic solver with superior scalability. We propose to investigate an innovative approach based on the concept of model order reduction. While model order reduction is well established for circuit simulation, it has received limited attention in computational electromagnetism, despite a clear need. We propose to develop the first systematic techniques to create compact, reduced-complexity models of the electromagnetic behavior of complex objects, such as the unit cell of a metasurface. The proposed models will be compatible with mainstream simulators based on the method of moments, allowing designers to analyze very large layouts in reasonable time. Leveraging the proposed developments and parallel computing, we will create an electromagnetic solver with unprecedented scalability, capable of handling an entire metasurface antenna. These developments will greatly facilitate the design of new metasurface antennas and ICs, including those required by upcoming IoT and AI applications. The proposed developments will be immediately translated to real design workflows, in collaboration with leading companies (AMD, Thales) and scientists (Profs. Eleftheriades and Hum). The proposed research will involve nine students who will receive comprehensive training of the highest quality, with access to world-class industrial partners, facilities and forefront technologies. Overall, this program is expected to result in nine graduates with a unique skillset, in high demand by industry and academia.

电磁现象在集成电路 (IC) 和天线中发挥着至关重要的作用，这些集成电路和天线为我们周围的许多电子系统（包括智能手机、计算机和数据中心）提供支持。在幕后，电磁学还在物联网 (IoT) 和人工智能 (AI) 等新兴技术中发挥着至关重要的作用。预计到 2020 年，将有 200 亿个物联网设备连接到互联网。 由于地面基础设施的成本高昂，电磁波和天线技术的重大进步将成为将无数未来物联网设备连接到互联网的唯一方法。超表面是一种新型天线，非常适合满足不断增长的物联网需求。不幸的是，超表面极其复杂。由于现有的电磁模拟器无法处理其复杂性，因此工程师目前缺少超表面设计最关键的工具之一。 AMD 等微电子公司也面临着类似的挑战，它们广泛依赖电磁模拟器来设计新的 IC，包括高级人工智能应用越来越需要的神经网络硬件加速器。 电磁求解器的功能与设计人员的需求之间的巨大差距是工业界和学术界面临的主要问题。所提出的研究旨在通过奠定具有卓越可扩展性的电磁求解器的理论和算法基础来填补这一空白。我们建议研究一种基于模型降阶概念的创新方法。虽然模型降阶在电路仿真中已经得到很好的应用，但它在计算电磁学中受到的关注有限，尽管有明确的需求。我们建议开发第一个系统技术来创建复杂物体电磁行为的紧凑、复杂性降低的模型，例如超表面的晶胞。所提出的模型将与基于矩量法的主流模拟器兼容，使设计人员能够在合理的时间内分析非常大的布局。利用所提出的开发和并行计算，我们将创建一个具有前所未有的可扩展性的电磁求解器，能够处理整个超表面天线。这些发展将极大地促进新型超表面天线和 IC 的设计，包括即将到来的物联网和人工智能应用所需的天线和 IC。 与领先公司（AMD、Thales）和科学家（Eleftheriades 和 Hum 教授）合作，拟议的开发将立即转化为实际的设计工作流程。拟议的研究将涉及九名学生，他们将接受最高质量的全面培训，并有机会接触世界一流的工业合作伙伴、设施和前沿技术。总体而言，该计划预计将培养出九名拥有独特技能的毕业生，受到工业界和学术界的高度需求。