Genetic Algorithm applied to the optimized project of semiconductor microcavity lasers

The application of new computational tools to design optimized devices is an important opportunity to minimize resources for its production. Moreover, these tools can ensure the correct operation of the devices, reaching its operating limit. In this paper we present the first quantitative study of parameters optimization for semiconductor microcavities synthesis under uncertainty using a genetic algorithm. These structures have been used in important studies of several areas for technological or purely scientific purposes. However, the definition of the optimal set of parameters for the fabrication of microcavities is a difficult task. Thus, the device can present different properties from those desired. Based on the reflectance spectra of a Al_xGa_1-xAs semiconductor microcavity, our goal is to find the optimal parameter set (aluminum concentrations x, thickness and the number of the layers). This set of parameters may offer increased robustness in the growth process, while providing a considerable Quality Factor and the desired position of the cavity resonance. The results indicate that the proposed algorithm is able to find satisfactory solutions by minimizing the problems caused by inaccuracy in the growth of these devices.