microStabilize: In-plane microstructure stabilization in optical microscopy via normalized correlation coefficient matching method

Abstract

In this work, an automatic vision-guided accurate positioning of a microstructure in an optical microscope is presented. Microscopes for near-infrared spectroscopy are using actuators with micrometer and nanometer precision to investigate semiconductor nanostructures. The cryostats used to cool down the structures and other mechanical elements are an inevitable source of vibrations and sample drift in the optical setup. This is one of the challenges of long-term experiments in obtaining reliable data that require excitation and detection from the same spot on the sample. Consequently, the need for setup design and software that utilizes active stabilization of a sample position has emerged. Presented Python-based software with GUI utilizes the normalized correlation coefficient matching method from the openCV library to localize microstructure and automatically compensate for any misalignment with pixel accuracy and $0.2\mu m$ precision in real-time.