A novel machine learning method for the design optimization of diamond waveguides fabricated by femtosecond laser writing

We report on a novel machine learning method for the design optimization of femtosecond (fs) laser written dielectric waveguides. Experimental results previously obtained from the optical characterization of fs laser written depressed cladding diamond waveguides have been used to form statistically generated regression models. Design variables such as core diameter and number of written tracks were varied to both minimize the propagation loss as well as to establish a full-factorial experimental design. The regression models were used to conduct a multi-objective optimization study to optimize the competing objectives such as maximizing the refractive index contrast while minimizing the propagation loss and V-number by using a genetic algorithm. Optimization was subject to a nonlinear Rayleigh range constraint to ensure that the structure was in the waveguiding regime. Results from the optimization revealed the optimum variables to achieve low-loss and nearly single-mode guiding for a fs laser written diamond waveguide. Using the solution sets of design parameters resulting from the optimization study and their corresponding objective function values, important correlations between the design parameters and the objective functions have been revealed. With this regard, it has been shown that the number of written tracks is a much more dominant parameter, when compared to core diameter, during the design of a fs laser written circular depressed cladding diamond waveguide. The proposed method should be applicable not only to diamond waveguides but also to a wide range of dielectric waveguides fabricated by fs laser writing.