A team of researchers at Penn State have devised a new, streamlined approach to design metasurfaces, a class of engineered materials that can manipulate light and other forms of electromagnetic radiation with just their structures. This rapid optimization process could help manufacture advanced optical systems like camera lenses, virtual reality headsets, holographic imagers and more, the team said.
The method, which was featured on the cover of the October issue of Nanophotonics, uses large language models (LLMs) to accurately predict how a metasurface will influence light. LLMs are a type of artificial intelligence (AI) model capable of learning and improving an action over time based on provided training data and repeated behavior. This approach bypasses the traditional metasurface simulation process that required extensive domain knowledge and time, making it possible for engineers to quickly design these nanoscopic materials and predict how they will influence light solely through prompts fed to AI.
According to Doug Werner, John L. and Genevieve H. McCain Chair Professor of electrical engineering and corresponding author on the work, metasurfaces offer much more flexibility and capability than traditional materials in nanophotonic devices - systems that can manipulate light at a scale even smaller than a wavelength of visible light.
"You can only go so far using naturally occurring materials when trying to manipulate light or other types of electromagnetic waves," Werner explained. "Through the structure of the sub-wavelength unit cells that make up the materials, metasurfaces can manipulate the way light behaves at a nanoscopic level, allowing us to slim down optical systems that are traditionally very bulky."
Despite their usefulness, metasurfaces are challenging to develop, according to Haunshu Zhang, a third-year electrical engineering doctoral student and first author on the paper. Zhang said that although AI has been integrated into the development process for a few years in the form of deep-learning neural networks, which mimic the non-linear way human brains can make connections, researchers would still have to go through the time-consuming and knowledge-intensive process of simulating potential designs and constructing a custom neural network for each metasurface.
This problem inspired him to integrate LLMs into the process.
"The main limitation of current neural-network-based methods is that you must try many neural network configurations in order to find one that accurately predicts how a metasurface will interact with light," Zhang said. "By training LLMs, we can accurately predict how a metasurface will interact with light in seconds compared to the hours, days or even months it previously took, without needing specialized AI expertise or countless trials."
The team compared their LLM-generated predictions to computer-simulated metasurfaces to test their method. The LLMs would predict how light would react when exposed to a metasurface with designated "control points" that morphed the design into a desired shape. The team then trained and compared these predictions to a data set of over 45,000 randomly generated metasurface designs. The team found that their approach provided highly accurate predictions of how light would react with the metasurfaces, while effectively eliminating the time-consuming neural network design process.
The increased efficiency allows researchers to focus on developing what Lei Kang, associate research professor of electrical engineering and co-author of the paper, called "arbitrarily shaped" metasurface elements. Lei explained how, compared to standardized shapes like cylinders or cubes, using highly specialized shapes in metasurface design can significantly impact performance and efficiency - but these free-form designs come with a substantial drawback.
"Arbitrary designs allow researchers to create application-specific metasurfaces that vastly outperform designs based on traditional shapes," Lei said. "However, these designs couldn't be optimized and tested effectively because traditional simulation methods would take an impractically long time to complete. By integrating LLM predictions, we can see how the metasurfaces will influence light at unprecedented speeds."
The new method also makes engineering metasurfaces extremely approachable, according to Sawyer Campbell, associate professor of electrical engineering and co-author on the paper. The LLMs are very good at "inverse design," or starting with the desired outcome and working backward to find the exact system, material, structure or combination of factors that produces it, he said. While inverse design of metasurfaces was possible before, the simulation process meant it could sometimes take multiple weeks or months to complete, according to Campbell.
