A Kernel-Based Calibration Algorithm for Chromatic Confocal Line Sensors.

Area of Science:

• Optics and Photonics
• Metrology
• Sensor Technology

Background:

• Chromatic confocal line sensors rely on accurate wavelength calibration for precise measurements.
• Existing research primarily focuses on peak extraction, neglecting wavelength calibration complexities.
• Distortions from field curvature, sensor imperfections, and dispersion add challenges to calibration.

Purpose of the Study:

• To introduce a novel kernel-based algorithm for wavelength calibration in chromatic confocal line sensors.
• To address unknown distortions and the inherent complexity of dispersion in the calibration process.
• To evaluate the performance improvement offered by the kernel trick in wavelength calibration.

Main Methods:

• Developed a kernel-based algorithm using polynomial basis functions for mapping image space to physical space.
• Incorporated a kernel trick-based nonparametric function to account for sensor-specific distortions.
• Performed wavelength calibration and groove fitting on a standard groove sample (reference depth 66.14 μm).

Main Results:

• The kernel-based calibration algorithm demonstrated higher accuracy in depth calculations.
• The proposed algorithm resulted in lower uncertainty in depth measurements compared to conventional methods.
• Experimental results validated the effectiveness of the kernel trick in improving calibration performance.

Conclusions:

• The proposed kernel-based algorithm offers significant improvements for wavelength calibration in chromatic confocal line sensors.
• This approach effectively handles complex distortions and enhances measurement precision.
• The findings suggest a more robust and accurate method for metrology applications using these sensors.