Inverse design of nanophotonic devices using generative adversarial networks

The efficient design of structures that exhibit desired properties is challenging across various engineering and scientific applications. Traditional methods employ experts in a specific domain to design new structures with desired properties. Then, simulations are performed for the designed structures to evaluate whether they show desired properties, and such a process is with until the structures exhibit desired properties. Advances in computing power and machine learning have made these simulations and optimizations faster, but challenges remain that the researchers must perform optimizations in each iteration, which generally takes time and cost. A new framework called inverse design has been studied to address the limitations. In inverse design, structures with desired properties can directly be constructed. In this work, as an inverse design framework, we introduce a controllable generative adversarial network (ControlGAN) based model to generate nanophotonic devices with user-defined properties. As a result, the proposed model outperforms other GAN-based models when the model is evaluated by producing structures with maximum transmittance at specific wavelengths. Specifically, the proposed model achieves a mean F1-score of 0.357, corresponding to a 260% improvement compared to the second-best model. The proposed model for inverse design can accelerate device designs not only in the field of nanophotonics but also in other nanostructures.