Inverse Design and Optimization of DFB Laser Spectra Using Deep Learning

We present a deep learning-based method for the inverse design and optimization of DFB laser spectra. A deep neural network is developed to predict key design parameters from spectral features such as peak wavelengths and intensities. The model is trained using data generated by the transfer matrix method, with its initial weights optimized by the Particle Swarm Optimization algorithm. Results show that the model can accurately predict design parameters for various types of DFB lasers, and effectively reduce issues like mode hopping. By combining deep learning with traditional physical models, this approach offers a faster and more accurate way to design semiconductor lasers.