Deep Learning-Based Optimization of Metasurface Designs for Enhanced Photonic Applications

Metasurfaces have emerged as a groundbreaking technology in the field of photonics, enabling unprecedented control over light propagation at subwavelength scales. This study leverages deep learning techniques to enhance the design and optimization of active metasurfaces, addressing critical challenges in fabrication and functional performance. We present a generalized neural network model specifically trained to recognize and classify defects in metasurfaces, using a comprehensive dataset derived from simulated and experimentally fabricated metasurface images. The model employs a convolutional neural network (CNN) to achieve high accuracy in defect detection, thereby facilitating rapid and precise quality control during the fabrication process. Our results seek to demonstrate significant improvements in the performance and reliability of metasurface-based photonic devices with the proposed methodology not only streamlining the design process, but also providing a robust framework for future advancements in metasurface technology.