Accelerating and Refining Free-form Metasurface Designs through Deep Learning
In the world of fifth-generation (5G) microwave communication, metasurfaces have emerged as a cutting-edge technology with widespread applications. Among the various types of metasurfaces, free-form metasurfaces stand out for their ability to achieve intricate spectral responses that surpass those of regular-shaped counterparts.
However, traditional numerical methods for designing free-form metasurfaces are time-consuming and require specialized expertise. Recognizing this bottleneck, recent studies have explored the potential of deep learning to expedite and enhance the metasurface design process.
In this context, researchers have introduced XGAN, an extended generative adversarial network (GAN), with a surrogate that enables the generation of high-quality free-form metasurface designs. This surrogate imposes a physical constraint on XGAN, allowing it to accurately generate metasurfaces from input spectral responses in a monolithic manner.
To assess the performance of XGAN, comparative experiments were conducted involving 20,000 free-form metasurface designs. The results were impressive, with XGAN achieving an average accuracy of 0.9734 and demonstrating a speed improvement of 500 times compared to the conventional methodology.
By enabling the rapid generation of metasurfaces with specific spectral responses, this approach facilitates the building of a metasurface library tailored to various communication needs. Moreover, the applicability of XGAN extends beyond microwave communication into other domains, such as optical metamaterials, nanophotonic devices, and even drug discovery.
The integration of deep learning techniques like XGAN into metasurface design processes marks a significant step forward in accelerating research and development efforts. By reducing both the time and expertise required for metasurface design, scientists and engineers can focus on exploring new possibilities and pushing the boundaries of what metasurfaces can achieve.