Multi-Functional Optical Meta-Systems Enabled by Deep-Learning-Aided Inverse Design

非技术描述：人工智能特别是深度学习在学术界和工业界都取得了许多突破。本项目旨在建立一种基于新颖深度学习技术的生成和通用设计方法，以实现集成的多功能光子系统，并在实验中提供原理证明。与使用大量数值模拟或逆设计算法的传统方法相比，深度学习可以从数据集中揭示光子结构与其属性之间高度复杂的关系，从而大大加快新型光子器件的设计，这些器件可以根据指定的波长、偏振、入射角和其他参数同时编码不同的功能。这种多功能光子系统在光学成像、全息显示、生物医学传感、消费光子等领域有着重要的应用，具有高效率、保真度高的特点，造福于人民和国家。综合教育计划将大大加强外展活动，并教育7-12年级的学生，这得益于pi之前建立的成功经验和伙伴关系。参与该项目的研究生和本科生将学习到光子学、深度学习和先进制造等多学科领域的最新发展，并通过与东北大学著名的合作教育项目进行产业合作，获得现实世界的知识。技术描述：超表面是由亚波长设计结构的平面阵列组成的二维超材料，它创造了一种新的范例，以规定的方式定制光学特性，具有卓越的可积性、灵活性、性能和可靠性，从而推动了光子技术的发展。然而，到目前为止，几乎所有的超表面设计都依赖于耗时的数值模拟或随机搜索方法，这些方法受到小参数空间的限制。为了充分利用元表面的多功能性，人们非常希望建立一种通用的、功能驱动的方法来有效地设计在单个系统中包含明显不同光学特性和性能的元表面。该项目的目标是创建并演示一种高效的两级设计方法，该方法通过深度学习实现，以实现集成的多功能元系统。适当的深度学习方法，如条件变分自编码器和深度双向卷积网络，将被研究，创新地重新制定和定制，以应用于单元素水平和大规模系统水平，结合拓扑优化和遗传算法。这种生成式设计方法可以直接、自动地从全参数空间中识别出最优的结构和构型。设计的多功能光学元系统将被制作和表征，以实验验证其性能。该项目的成功将产生变革性的光子结构，以按需操纵光。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。

Nontechnical Description: Artificial intelligence especially deep learning has enabled many breakthroughs in both academia and industry. This project aims to create a generative and versatile design approach based on novel deep learning techniques to realize integrated, multi-functional photonic systems, and provide proof-of-principle demonstrations in experiments. Compared with traditional approaches using extensive numerical simulations or inverse design algorithms, deep learning can uncover the highly complicated relationship between a photonic structure and its properties from the dataset, and hence substantially accelerate the design of novel photonic devices that simultaneously encode distinct functionalities in response to the designated wavelength, polarization, angle of incidence and other parameters. Such multi-functional photonic systems have important applications in many areas, including optical imaging, holographic display, biomedical sensing, and consumer photonics with high efficiency and fidelity, to benefit the public and the nation. The integrated education plan will considerably enhance outreach activities and educate students in grades 7-12, empowered by the successful experience and partnership previously established by the PIs. Graduate and undergraduate students participating in the project will learn the latest developments in the multidisciplinary fields of photonics, deep learning and advanced manufacturing, and gain real-world knowledge by engaging industrial collaborators in tandem with Northeastern University's renowned cooperative education program.Technical Description: Metasurfaces, which are two-dimensional metamaterials consisting of a planar array of subwavelength designer structures, have created a new paradigm to tailor optical properties in a prescribed manner, promising superior integrability, flexibility, performance and reliability to advance photonics technologies. However, so far almost all metasurface designs rely on time-consuming numerical simulations or stochastic searching approaches that are limited in a small parameter space. To fully exploit the versatility of metasurfaces, it is highly desired to establish a general, functionality-driven methodology to efficiently design metasurfaces that encompass distinctly different optical properties and performances within a single system. The objective of the project is to create and demonstrate a high-efficiency, two-level design approach enabled by deep learning, in order to realize integrated, multi-functional meta-systems. Proper deep learning methods, such as Conditional Variational Auto-Encoder and Deep Bidirectional-Convolutional Network, will be investigated, innovatively reformulated and tailored to apply at the single-element level and the large-scale system level in combination with topology optimization and genetic algorithm. Such a generative design approach can directly and automatically identify the optimal structures and configurations out of the full parameter space. The designed multi-functional optical meta-systems will be fabricated and characterized to experimentally confirm their performances. The success of the project will produce transformative photonic architectures to manipulate light on demand.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.