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

今天的设备创新通常需要科学家或工程师执行涉及设计和仿真的耗时的迭代过程。 设计过程基于应用专家知识来识别合理的设备布局，这些布局使用基于物理的通用算法进行验证并迭代改进。 机器学习的新进展有可能在这个创新周期中具有破坏性，因为这些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，这些工具可以自动化并大大加速设计和仿真过程，速度快了几个数量级。 这些概念将基于一类新的算法，将数据科学中的传统概念与物理学结合在一起。 可以作为小型化光学系统的光学设备将被用作测试平台，以衡量这些算法的性能，尽管这些概念最终适用于科学计算问题。 如果成功，这些算法将成为一类新的计算机辅助设计工具的基础，帮助科学家和工程师以极大的便利性创新新的设备和系统。该项目的教育目标是开发和传播新课程，激励高中生将STEM视为职业途径。本论文的研究目标是建立一个算法平台，用于自由形态光子器件的全局优化，该算法平台可以扩展到大面积，三维，多功能器件。 所针对的基本障碍是现有的全局自由形式优化方法实际上无法扩展到复杂的三维系统，这是由于在全局设计空间内的设备的采样和模拟的缩放限制。 这些基本的缩放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解算器来解决，这些解算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。 所提出的概念将建立在最近的发现基础上，即基于群体的全局自由形式优化可以通过使用基于物理的计算训练生成神经网络来执行。 预期的成果是开发新的概念和广泛适用的算法，使三维电介质电磁设备的全球优化成为可能。该奖项反映了NSF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。

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.