09:00 〜 09:30
▲ [10a-N404-1] Inverse design and forward modelling in nanophotonics using deep-learning
キーワード：Deep learning, Inverse design, Nanophotonics
Recent introduction of deep learning into nanophotonics has enabled efficient inverse design process [1]. Once the deep learning network is trained, it allows fast inverse design for multiple design tasks. In this talk, we show several inverse designing nanophotonic structures using deep learning [1-9]. We firstly discuss inverse design methods that increase the degree of freedom of design possibilities. These attempts include designing arbitrary shapes of nanophotonic structures, that are not limited to pre-defined structures [2], and designing both types of materials and structural parameters simultaneously [3]. In order to design arbitrary shapes of structures, cross-sectional design images are designed by generative model. Also, for simultaneous design of materials and structural parameters, we developed a novel objective function that combines regression and classification problems. After then, we also discuss optimizing nanophotonic structures using deep learning. We use reinforcement learning to optimize structure parameters. Using reinforcement learning, an agent learns parameter space of an environment through the exploration and exploitation of the reward. After learning, the agent can provide the optimized design parameters from its own experience. Several meta-devices including dielectric color filter [4], high efficiency hologram [5], perfect absorber [6-8], plasmonic structures [9], dielectric gratings [10] and microwave antenna [11] are designed using this method.