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

如今的设备创新通常需要科学家或工程师执行耗时的迭代过程，包括设计和模拟。设计过程是基于应用专家知识来识别合理的器件布局，并使用基于物理的通用算法进行验证和迭代改进。机器学习的新进展有可能在这一创新周期中产生颠覆性影响，因为这样的算法能够以全新的方式学习和处理数据。这项提议的重点是开发新的机器学习工具，这些工具可以使设计和模拟过程自动化并显著加快速度，速度提高了数量级。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起来。可以作为微型光学系统的光学设备将被用作测试平台，以对这些算法的性能进行基准测试，尽管这些概念最终是科学计算问题的通用概念。如果成功，这些算法将成为新一类计算机辅助设计工具的基础，这些工具将帮助科学家和工程师以极大的便利创新新类型的设备和系统。该项目的教育目标是开发和传播新的课程，鼓励高中生将STEM视为一条职业道路。该方案的研究目标是为自由形状光子器件的全局优化创建一个算法平台，使其能够规模化到大面积、三维、多功能器件。针对的根本障碍是现有的全局自由形式优化方法无法实际扩展到复杂的三维系统，这是由于在全局设计空间内对设备的采样和模拟的缩放限制。这些基本的可伸缩性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，这些求解器可以与新的全局搜索和设计基于深度网络训练的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，即基于种群的全局自由形式优化可以通过使用基于物理的计算来训练生成性神经网络来执行。预期的结果是开发新的概念和广泛适用的算法，使三维介质电磁设备能够进行全球优化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.