Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches

Chiral photonic metasurfaces provide unique capabilities for tailoring light–matter interactions, which are essential for next‐generation photonic devices. Here, we report an advanced optimization framework that combines deep learning and evolutionary algorithms to significantly improve both the design and performance of chiral photonic nanostructures. Building on previous work utilizing a three‐layer perceptron, reinforced learning, and a stochastic evolutionary algorithm with decaying changes and mass extinction for chiral photonic optimization, our study introduces a refined pipeline featuring a two‐output neural network architecture to reduce the trade‐off between high chiral dichroism (CD) and reflectivity. Additionally, we use an improved fitness function and efficient data augmentation techniques. A comparative analysis between a neural network (NN)‐based approach and a genetic algorithm (GA) is presented for structures of different interface pattern depth, material combinations, and geometric complexity. We demonstrate a twice‐higher CD and the impact of both the corner number and the refractive index contrast in the example of a GaP/air and PMMA/air metasurface as a result of superior optimization performance. Additionally, a substantial increase in the number of structures explored within limited computational resources is highlighted, with tailored spectral reflectivity suggested by our electromagnetic simulations, paving the way for chiral mirrors applicable to polarization‐selective light–matter interaction studies.