| dc.description.abstract | This thesis proposes a solution to the scarcity of geometric structures and material data while designing metasurface, so that the design efficiency can be improved. We employed a conditional deep convolutional generative adversarial network (cDCGAN), training the model by learning spectral data to enable the prediction of geometric structures based on spectral characteristics. To improve computational efficiency, we utilized the rigorous coupled-wave analysis (RCWA) method combined with GPU-accelerated computing, significantly increasing the speed of optical response calculations. We constructed a database of various geometric structures and material properties, encompassing transmission spectral information across different wavelengths, including transmission spectrum under incident x-polarized light, y-polarized light, left-handed circularly polarized light, and right-handed circularly polarized light. By integrating the RCWA with the cDCGAN, we can rapidly generate and validate nanoscale structures that satisfy design requirements during the design phase, greatly reducing the computational time and resources required by traditional methods. This artificial intelligencebased inverse design approach provides an efficient and precise alternative for designing metasurface and is expected to play a significant role in the design and development of optical components. With further advancements in AI models, we anticipate that this method will drive progress in the field of optical component design, offering robust support for future scientific research and industrial applications. | en_US |