Abstract
This paper proposes a practical methodology to quantify and compensate lateral errors for focus variation microscopy measurements without stitching. The main advantages of this new methodology are its fast and simple implementation using any uncalibrated artefact. The methodology is applied by performing measurements with multiple image fields with and without stitching on an uncalibrated artefact and using the stitched measurements as reference. To quantify the lateral errors, the determination of their geometrical components is carried out through kinematic modelling. With the quantified errors, compensation can be applied for lateral measurements without stitching. Over the entire 200 mm lateral range, the lateral errors without stitching and without compensation can reach up to 180 µm. With the proposed error compensation methodology, the lateral errors have been reduced to around 15 µm. The proposed methodology can be applied to any Cartesian-based optical measuring instrument.
Highlights
This paper proposes a practical methodology to quantify and compensate lateral errors for focus variation microscopy measurements without stitching
It is worth noting that measurement without image stitching is not the normal operating mode of commonly avail able Focus variation microscopyFocus variation microscopy (FVM) instruments
We have shown that it is possible to char acterise the xy-stage of a FVM by performing two types of measurement with an uncalibrated artefact
Summary
Texture measuring instrument [2, 3] Due to this combination, FVM is widely used for both form and surface texture mea surements in industry, research and academic institutions [3,4,5]. To improve the lateral accuracy and precision of its measurement results, commonly available FVM often stitches multiple overlapping measurement areas to compensate its lateral stage error. The main drawback of this stitching tech nique is that measurements with multiple overlapping areas (image-field measurements) are time consuming and limited by the capacity of the host computer memory to process a large number of raw datasets (a stack of images). Measurements with multiple areas are not applicable in this case; measurements without the overlapping area
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