Correction of spherical surface measurements by confocal microscopy

Abstract

Refractive microlenses are nowadays widely used in optical systems. Characterizing their surface is essential to ensure their quality and to optimize their fabrication process. This is realized by optical surface profilers thanks to their vertical resolution, short measurement time and areal information. However, when measuring non-flat surfaces, errors appear caused by aberrations of the microscope objective used in such systems, which significantly limit the achievable quality of the manufactured spherical surfaces. Approaches have been proposed to tackle these errors, but none of them demonstrated its validity for measurements of high quality microlenses. In this work, we demonstrate that the surface error depends on the surface position within the field of view of the microscope objective and on the surface slope. We then explain how to record the value of this error experimentally: this can be done by measuring a reference ball placed at different positions in the field of view. We finally use a machine learning algorithm to fit the experimental data in order to correct subsequent measurements. We apply this approach to measurements performed by a 20× numerical aperture 0.6 microscope objective of a confocal microscope. The effectiveness of the proposed method is demonstrated by showing that the surface error corresponds to a RMS wavefront error of λ/7 before correction and of λ/50 after correction for glass microlenses used in the visible range. This method thus allows the use of high numerical aperture microscope objectives for an accurate characterization of microlenses. Likewise, the fabrication capability of microlenses in terms of slope and quality is greatly extended, which is especially important for aspheres or freeforms.

Three-dimensional confocal reflectance microscopy for surface metrology

“…Confocal microscopy can also be used to improve and optimize the quality and the fabrication process of micro-lenses by correcting the errors from optical systems with a machine learning algorithm. In a study to correct the measurement error, an root mean square (RMS) wavefront error of λ/50 was successfully achieved for glass micro-lenses after applying the correction method [165].…”

Commented review on refractive microlenses and microlens arrays metrology

“…However, this error can be corrected at a satisfactory level. 35,36 Consequently, the main drawback of these techniques is the limited capability to measure large and steep surfaces. This aspect is discussed in more detail in Sec.…”

“…For the confocal measurement, it has been shown that this error can be corrected. 36 This comparison is summarized in Table 1. Information about measurement time is also included.…”

A Differential Confocal Sensor for Simultaneous Position and Slope Acquisitions Based on a Zero-Crossing Prediction Algorithm

“…Mauch et al [ 13 ] explained in detail the signal formation process of the confocal system and proved that when the measured surface is curved, the defocusing wavefront may have a larger coincidence ratio than the focusing wavefront and the curvature of the measured surface, thus resulting in a large deviation in the axial position corresponding to the extreme value of the confocal signal strength. Beguelin et al [ 14 ] used machine-learning methods to compensate for errors caused by surface tilts in distance measurements and used imaging results to correct the measured data. Therefore, it is also important for the measurement of the spatial position to obtain the surface inclination while 3D spatial coordinates are concurrently obtained.…”