Genetic Algorithm Applied to the Optimized Design of Semiconductor Microcavity Lasers

Abstract

Semiconductor microcavities have been used in important studies of several areas for technological or purely scientific purposes. However, the definition of the optimal parameters for the fabrication of microcavities is a difficult task. Moreover, some uncertainties related to the growth process can change the device features. These problems cannot be experimentally controlled, hindering the development of theoretical models. In this work we present a theoretical model to simulate the microcavities and also propose an evolutionary approach to optimize the device under uncertainty in order to ensure the growth with the desired features. Thus, based on the reflectance spectra of a AlxGa1-xAs semiconductor microcavity, the aluminum concentrations, x, and the number of layers that compose the heterostructure were optimized. 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, achieving the device’s operation limits. The device was optimized considering the cavity resonance between 700nm and 2000nm, where the results indicate that the proposed algorithm is able to find satisfactory solutions, minimizing the problems caused by inaccuracy in the growth process.