FET/SHF: Small: Reinforcement learning and transformer inspired smart photonics inverse design

FET/SHF：小型：强化学习和变压器启发的智能光子逆设计

• 批准号: 2309403
• 负责人: L. Jay Guo
• 金额: $ 60万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309403&HistoricalAwards=false
• 关键词: FET; SHF; Small; Reinforcement; learning

The field of optics and photonics has transformed our lives with such modern technologies as optical communications, solar energy sources, space telescopes and high-resolution photolithography. For a long time, human experts had to perform optical design manually relying on their accumulated experience and physical intuitions. This is especially true for photonic inverse design, where one must find the appropriate photonic structures with the desired optical functions. Recently, deep learning approaches have been pursued by scientific community to automatically design sophisticated structures that can satisfy the design objective, leading to substantial progress in this direction. This research project will enable photonics non-experts to use the developed Artificial Intelligence (AI) model to obtain solutions to their individual optical design problems. Furthermore, understanding the underlying operational principles will advance more generalizable photonics knowledge and enable researchers to develop new structures faster.To accomplish their goals, the research team will explore and apply two powerful AI technologies. The first tool to be studied is deep reinforcement learning, a sequential generation process that learns to design structures with trial-and-reward, in a way to mimic how human and animals learn to interact with the world. Cooperative learning between machine and human will be pursued, where human teaches machine to learn, and machine inspires human to understand. This combined input will benefit the realization of various types of optical structures. The second method utilizes the transformer method, the powerhouse behind the highly successful powerful large language models, for smart optical design. The research team will leverage the Foundation model, the large machine learning models that tackle various downstream tasks once trained on diverse, and large-scale data, to address the optical inverse design of large-scale and complicated nanostructures. The knowledge gained through the study will be applied further to a few testbeds for experimental demonstrations. With more people using the technology and increased data available for training the neural network, one can anticipate its learning and generative capabilities will advance and will the users even more effectively.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.

光学和光子学领域已经通过光通信、太阳能、空间望远镜和高分辨率光刻等现代技术改变了我们的生活。长期以来，人类专家不得不依靠他们积累的经验和物理直觉手动进行光学设计。这对于光子逆向设计尤其如此，其中必须找到具有所需光学功能的适当光子结构。最近，科学界一直在追求深度学习方法，以自动设计能够满足设计目标的复杂结构，从而在这一方向上取得了实质性进展。该研究项目将使光子学非专家能够使用开发的人工智能（AI）模型来获得其个人光学设计问题的解决方案。此外，了解基本的操作原理将推进更普遍的光子学知识，使研究人员能够更快地开发新结构。为了实现他们的目标，研究团队将探索和应用两种强大的人工智能技术。第一个要研究的工具是深度强化学习，这是一个顺序生成过程，可以学习通过尝试和奖励来设计结构，以模仿人类和动物如何学习与世界互动。机器和人类之间的合作学习将被追求，人类教机器学习，机器启发人类理解。这种组合输入将有利于实现各种类型的光学结构。第二种方法利用Transformer方法，这是非常成功的强大的大型语言模型背后的动力，用于智能光学设计。 该研究团队将利用Foundation模型，即大型机器学习模型，这些模型一旦在不同的大规模数据上进行训练，就可以解决各种下游任务，以解决大规模和复杂纳米结构的光学逆向设计。通过研究获得的知识将进一步应用于一些试验台进行实验演示。随着越来越多的人使用这项技术，越来越多的数据可用于训练神经网络，人们可以预期它的学习和生成能力将进一步提高，并将用户更有效地。这个奖项反映了NSF的法定使命，并已被认为值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。