主讲嘉宾

L. Jay Guo，the Emmett Leith Collegiate Professor of Electrical and Computer Engineering at the University of Michigan and holds courtesy appointment in Mechanical Engineering and Applied Physics、the director of the Macromolecular Science and Engineering program、a fellow of IEEE and a fellow of Optica

主讲人简介

L. Jay Guo is the Emmett Leith Collegiate Professor of Electrical and Computer Engineering at the University of Michigan. He currently serves as the director of the Macromolecular Science and Engineering program, and holds courtesy appointment in Mechanical Engineering, and Applied Physics. He is a fellow of IEEE and a fellow of Optica. Professor Guo’s lab is involved in interdisciplinary research, with activities ranging from polymer-based photonic devices and sensor applications, flexible transparent conductors, nanophotonics, structural colors and AI assisted design, hybrid photovoltaics and photodetectors, to nanomanufacturing technologies. Professor Guo has 295 journal publications; with citation more than 35,000 times, and an H-index of 92 (by google scholar). He is co-Editor-in-Chief of Micro and Nano Manufacturing, and associate Editor for the IEEE Journal of Photovoltaics. He serves on the Advisory Board of Advanced Optical Materials and the Editorial Boards of Opto-Electronic Science and Opto-Electronic Technology, and regularly contributes as a chair and program committee member for leading nanofabrication and photonics conferences.

讲座摘要

Light interacting with metallic and dielectric structures can produce various interesting optical effects. Structural color is one of them, where structure-property relationship determines the optical response, more so than the constituent materials. Structural colors can be produced by exploiting optical resonances in 1D, 2D and 3D structures, offering advantages over conventional colored pigments such as high brightness, durability, and environmental safety and sustainability. For sub-micron structures, scalable nanopatterning technique is the key to enable large area manufacturing. Simpler yet is multilayer structure that can be realized by vapor deposition, with a wide range of applications. Analysis shows that color chromaticity can be controlled by adjusting the absorption and radiation loss in an optical cavity. When it comes to the design of the structures, in the past, human experts perform the optical design manually based on the accumulated experience and physical intuitions. This is especially true for photonic inverse design, which is to find the appropriate photonic structures with the desired color performance. We show that deep learning approach (reinforcement learning and GPT) can automatically design sophisticated structures to satisfy the design objective, and can facilitate researchers to gain insight on the working mechanism. For cost-effective fabrication, we explored solution process (e.g. electrochemical deposition, dip-coating, and nanocrystal growth) to form optical thin film structures, which can yield both primary and subtractive colors depending on the morphology of the thin metal layer.