Researchers have developed a novel approach using deep learning to accelerate and optimize the design of nanoantennae, crucial components in advanced optical devices known as metasurfaces. These metasurfaces, engineered with meticulously designed nanostructures, can manipulate light in unprecedented ways.
The team generated a comprehensive dataset of 1500 aluminum nanoantenna designs, categorized into rectangles, double-arcs, rectangle-circle pairs, and rectangle-square pairs, each with varying dimensions. These designs, visualized as 2D images, were paired with simulated optical responses representing how efficiently they convert left circular polarized light to right circular polarized light across a spectrum of wavelengths (400nm to 800nm). The pixel intensity in the design images encodes the thickness of a dielectric spacer layer, a critical factor influencing performance.
To streamline the design process, two deep learning models were employed. A Simulation Neural Network (SNN), functioning as a forward design tool, was trained to predict the optical response of a given nanoantenna design. This model, structured with convolutional and fully connected layers, learns the complex relationship between the design’s geometry and its optical behavior, enabling rapid prediction of performance.
In tandem, a conditional Generative Adversarial Network (cGAN) was implemented for inverse design. This model is capable of generating nanoantenna designs based on a user-specified desired optical response. The cGAN architecture involves a generator, which creates designs from random noise and the desired optical response, and a discriminator, which distinguishes between real and generated designs. Through adversarial training, the generator learns to produce realistic and effective designs that match the requested optical performance.
To further refine the design process, the researchers introduced a cyclical generation framework. This innovative system integrates the SNN and cGAN models with a pseudo-genetic algorithm (pGA). Beginning with a batch of user-defined optical responses, the cGAN generates initial designs, which are then evaluated by the SNN to predict their optical performance. The pGA then selects the best-performing designs based on accuracy metrics like Mean Squared Error (MSE) and cosine similarity, which measures the similarity in the shape of the optical response. These top designs, along with their associated optical responses, are incorporated back into the training dataset, iteratively improving the models’ ability to generate and predict optimal designs in subsequent cycles. This cyclical refinement process effectively combines forward and inverse design, leading to highly optimized nanoantenna structures.
Evaluations of the models demonstrated high accuracy, with cosine similarity used to ensure the models captured the sharp variations and nuances in the optical responses. The training process was conducted efficiently on standard hardware, highlighting the practical applicability of this approach.
This deep learning-based framework significantly accelerates the design and optimization of nanoantennae for metasurfaces. By automating and optimizing the design process, this method paves the way for faster development and deployment of advanced optical technologies with tailored functionalities. The cyclical generation framework offers a powerful tool for creating complex nanostructures for a wide range of optical applications.
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