Joe Suarez
  1. Launch Lumerical FDTD Solutions and create a new project. Define the simulation parameters, such as the size of the computational domain, material properties, source parameters, and boundary conditions. These parameters will depend on the specific problem you are solving.
  2. Use Lumerical’s layout tools to create the desired metasurface or metamaterial structure. This may involve defining the geometry, material properties, and arrangement of unit cells.
  3. Once you have set up the simulation, run the FDTD simulation to obtain the electromagnetic field distribution and other desired outputs. Lumerical will perform the necessary calculations to solve Maxwell’s equations in the defined domain.
  4. After the simulation completes, you can extract data at specific locations or surfaces of interest. This may include field profiles, transmission/reflection coefficients, near-field distributions, or any other relevant information you need for your deep-learning model.
  5. To generate a diverse dataset, you can repeat steps 3-5 for various parameter configurations, such as changing the geometrical parameters, material properties, incident angles, or polarization states.
  6. Save the extracted data in a suitable format that is compatible with your deep learning framework. Common formats include CSV, HDF5, or specialized file formats specific to deep learning libraries.
  7. Use a deep learning framework of your choice (e.g., TensorFlow, PyTorch, Keras) to develop a neural network model for metasurface or metamaterial characterization. The specifics of implementing the model will depend on the architecture and objective of your deep learning model.
  8. Before feeding the data into the deep learning model, you may need to preprocess it. This could involve normalization, resizing, or augmenting the data to enhance the model's performance.
  9. Split your dataset into training, validation, and test sets. Use the training set to train the deep learning model and adjust its weights and biases based on the data. Evaluate the model's performance using the validation set and make necessary adjustments to improve it.
  10. Finally, evaluate the trained model's performance using the test set, which contains unseen data. Analyze the model's predictions and assess its lead data enrichment accuracy and generalization capabilities.