Generative Adversarial Networks to Design Metamaterials Based Nano-Photonics Devices

Since modern life dictates human comforts, it is not surprising that the revolution of the wireless technology dominated very quickly in industrial nations, which is unlike the practical world of yore when activities required extra physical efforts to be accomplished. Nowadays, physical tasks are avoided. Therefore, it is the time to lunch quick transformations in artificial material, based on nano-scale structures and metamaterial inclusions, design to accelerate the development of modern photonic devices that have suffered from several limitations such as availability, integrability, reconfigurability, immutability, and maintainability in which the material electromagnetic specifications play the major role. This paper presents a Python implementation of a Generative-Adversarial-Network (GAN) tailored for the design and optimization of metamaterials for nano-photonics devices. The GAN consists of a generator model that produces synthetic 2D nanostructure patterns and a discriminator model that distinguishes between real and generated patterns. The goal is to generate novel designs that exhibit desirable optical properties. The code leverages TensorFlow and Keras to build and train the GAN, utilizing the MNIST dataset as a placeholder for metamaterial data. In a JupyterLab environment, the code generates and visualizes the evolving designs during training. By refining the generator through adversarial training, this approach lays the groundwork for automating the design of metamaterial-based photonic devices, reducing the need for labor-intensive trial-and-error processes and enabling rapid exploration of the design space. The implementation can be adapted to incorporate physics-based simulations for evaluating optical performance, providing a robust framework for the automated discovery of next-generation metamaterials.