Deep learning models, with a prerequisite of large databases,
are
common approaches in applying machine learning for inverse design
in photonics. For these models, less expensive, approximate methods
are usually used to generate large databases, which limit their applications.
In this study, we compare the performance of data-efficient machine
learning (ML) models for predicting the characteristics of surface
Bragg gratings in semiconductor ridge waveguides. We employ the 3D
finite-difference time-domain method which is very accurate but time-consuming
to generate a database. We analyze the performance of different ML
models including support vector regression and extreme gradient boosting
(XGBoost) on our limited data. We show that the XGBoost significantly
outperforms other models on a smaller database. Our results pave the
way for the data-efficient design of integrated photonic components
with accurate but time-demanding simulations.