Design of Nanophotonic Devices using Multi Objective Optimization Method

The performance of nanophotonic devices was very sensitive and nonlinear to the structural design parameters. In this manuscript, two examples of multi-objective optimizations using the response surface method and Kriging surrogate model with the disability function for the designing of nanophotonic devices were introduced. Although reasonable optimum design parameters could be obtained using performance expectation models after the proper selection of key design factors and ranges of design factors, a machine learning method with big data could be a powerful solution for the extensive parametric analysis and optimization in the design of nanophotonic devices.