Enhancing the Performance of Photonic Crystal AND Gates with Machine Learning Optimization

Recent advances in photonic crystals have opened up new possibilities for developing high-speed, low-power optical devices. One promising application is using photonic crystals to realize AND gates, fundamental building blocks in digital logic circuitry. This paper presents a machine learning-based approach to optimizing the performance of AND gates. We utilize the Extra Trees Regressor model to train on a dataset of simulation results and predict the output power for different input and parameter configurations. Our results show that the model can accurately predict the output power with an RMSE (Root Mean Square Error) of 0.18. We then use the model to identify optimal parameter settings for the radius of the rods and the lattice constant. We find that the optimal parameters are R= 0.05 um and x=0.12 um. The paper further scrutinizes the optimization process for optical gate outputs, leveraging predictions from the Extra Trees Regressor for conditional calculations. It meticulously examines the impact of parameters such as rod radius and lattice constant on gate functionality, emphasizing their role in achieving desired output states. Simulation results elucidate the efficacy of optimized parameters in realizing the behavior of an AND gate within the photonic crystal framework.