Paper
22 December 2003 Self-calibration for microrefractive lens measurements
Neil Gardner, Timothy Randolph, Angela Davies
Author Affiliations +
Abstract
Micro-optics are essential components for building compact optoelectronic and micro-electro-optical mechanical systems and micro-refractive lenses are an important example. Refractive lenses are continuous relief structures and details of their dimensional shape, refractive index and homogeneity strongly influence performance. Some dimensional and transmitted light properties of the refractive components can be measured with scanning white light interferometers (SWLI), profilometers, and phase-shifting micro-interferometers, however limitations exist with each method. Micro-interferometry is the most promising and can be used to measure radius of curvature, focal length, dimensional surface errors, and transmitted wavefront. However, methods have not been optimized to achieve low uncertainties. Systematic biases can be comparable to figure errors on the part, therefore a rigorous calibration method is needed. Current practice involves measuring a very high quality part and measured errors are equated to instrument biases. It is often difficult, however, to obtain such a part. The alternative is to use a self-calibration test. As an alternative, the random ball test can be applied to micro-interferometers and SWLIs for self-calibration. This paper details the implementation of this test for both types of instruments and describes the method of estimating the calibration uncertainty.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Neil Gardner, Timothy Randolph, and Angela Davies "Self-calibration for microrefractive lens measurements", Proc. SPIE 5180, Optical Manufacturing and Testing V, (22 December 2003); https://doi.org/10.1117/12.506396
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KEYWORDS
Calibration

Optical spheres

Photovoltaics

Interferometers

Monochromatic aberrations

Optics manufacturing

Surface finishing

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