CDS&E: Physics-driven computational tools for photonic design

CDS

• 批准号: 2103301
• 负责人: Jonathan Fan
• 金额: $ 37.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103301&HistoricalAwards=false
• 关键词: CDS&E; Physics; driven; computational; tools

Device innovation today typically requires scientists or engineers to perform a time-consuming iterative procedure involving design and simulation. The design process is based on the application of expert knowledge to identify plausible device layouts, which are validated with a general physics-based algorithm and iteratively improved. New advances in machine learning have the potential to be disruptive in this innovation cycle, due to the ability for such algorithms to learn and process data in entirely new ways. This proposal focuses on the development of new machine learning tools that can automate and dramatically accelerate the design and simulation procedure by orders of magnitude faster speeds. These concepts will be based on a new class of algorithms that bring together conventional concepts in the data sciences with physics. Optical devices that can serve as miniaturized optical systems will be used as a testbed to benchmark the performance of these algorithms, though the concepts are ultimately general to scientific computing problems. If successful, these algorithms will serve as the foundation for a new class of computer-aided design tools that will help scientists and engineers innovate new classes of devices and systems with great expediency. The education goal of this project is to develop and disseminate new curricula that inspires high school students to consider STEM as a career pathway. The research objective of this proposal is to create an algorithmic platform for the global optimization of freeform photonic devices that can scale to large area, three-dimensional, multi-functional devices. The fundamental roadblock that is targeted is the inability of existing global freeform optimization methods to practically scale to complex three-dimensional systems, due to scaling limits in the sampling and simulation of devices within the global design space. These fundamental scaling limits will be addressed by creating data-driven and physics-driven neural network electromagnetic surrogate solvers that can couple with new global search and design space evaluation tools based on deep network training. The proposed concepts will build on a recent discovery that population-based global freeform optimization can be performed by training a generative neural network using physics-based calculations. The expected outcomes are the development of new concepts and broadly applicable algorithms that will enable the global optimization of three-dimensional dielectric electromagnetic devices.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.

今天的设备创新通常需要科学家或工程师执行一个耗时的迭代过程，包括设计和模拟。设计过程基于专家知识的应用，以确定合理的器件布局，并通过基于一般物理的算法进行验证，并进行迭代改进。机器学习的新进展有可能颠覆这个创新周期，因为这种算法能够以全新的方式学习和处理数据。该提案的重点是开发新的机器学习工具，这些工具可以将设计和仿真过程自动化，并以更快的速度显著加速。这些概念将基于一类新的算法，这些算法将数据科学中的传统概念与物理学结合在一起。可以作为小型光学系统的光学设备将被用作测试这些算法性能的基准，尽管这些概念最终适用于科学计算问题。如果成功的话，这些算法将成为一种新型计算机辅助设计工具的基础，这种工具将帮助科学家和工程师以极大的便利创造出新型的设备和系统。该项目的教育目标是开发和传播新的课程，激励高中生将STEM作为职业道路。本课题的研究目标是为自由形状光子器件的全局优化提供一个算法平台，使其能够扩展到大面积、三维、多功能的器件。主要的障碍是现有的全局自由曲面优化方法无法实际扩展到复杂的三维系统，这是由于全局设计空间内设备采样和模拟的缩放限制。这些基本的扩展限制将通过创建数据驱动和物理驱动的神经网络电磁代理求解器来解决，这些求解器可以与基于深度网络训练的新的全局搜索和设计空间评估工具相结合。提出的概念将建立在最近的一项发现之上，即可以通过使用基于物理的计算训练生成神经网络来执行基于种群的全局自由形状优化。预期结果是新概念和广泛适用的算法的发展，这将使三维介质电磁器件的全局优化成为可能。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Algorithm-Driven Paradigms for Freeform Optical Engineering

• DOI: 10.1021/acsphotonics.2c00612
• 发表时间: 2022-08-09
• 期刊: ACS PHOTONICS
• 影响因子: 7
• 作者: Fan, Jonathan A.;Chen, Mingkun;Jiang, Jiaqi
• 通讯作者: Jiang, Jiaqi

High Speed Simulation and Freeform Optimization of Nanophotonic Devices with Physics-Augmented Deep Learning

• DOI: 10.1021/acsphotonics.2c00876
• 发表时间: 2022-08
• 期刊: ACS Photonics
• 影响因子: 7
• 作者: Ming-Keh Chen;Robert Lupoiu;Chenkai Mao;Der-Han Huang;Jiaqi Jiang;P. Lalanne;Jonathan A. Fan