Neural Network-Driven Optimization of Photonic Crystal-Based All-Optical NOT Gate Design

This study explores the application of neural networks in optimizing design parameters for an all-optical NOT gate using photonic crystals. It focuses on the unique properties of photonic crystals, highlighting their role in controlling light flow and creating small optical devices. The research introduces a three-layer neural network architecture trained to forecast crucial parameters necessary for the effective functioning of the NOT gate. By conducting systematic simulations, the neural network accurately determines the optimal radius of the rods (R0 and R1), which is essential for the gate's operation. The results obtained, when compared with actual outputs, show an outstanding Mean Absolute Error (MAE) of 0.005 for 'R0' and 0.0006 for 'R1', confirming the model's precision in replicating the behavior of the NOT gate across various input configurations. Through methodical simulations and analyses, the paper illustrates the effectiveness of the neural network in optimizing the rod radius and predicting gate behavior. The main rationale behind employing neural networks was to achieve higher prediction accuracy. The strength of neural networks lies in their capability to handle intricate scenarios, such as dealing with parameters like the lattice constant and the radius of the rods in photonic crystals. In such situations, neural networks offer a robust solution, enabling us to effectively tackle these challenges.