Optical Aberration Correction for Machine Vision Z-Height
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Solution Overview
Problem
Machine vision inspection systems face challenges in achieving high accuracy and reliability for Z-height measurements due to lens aberrations like astigmatism, which cause focus errors that vary with the orientation of directional features in the field of view, leading to inconsistent and less precise measurements.
Innovation Solution
A high-accuracy optical aberration correction system and method that characterizes and corrects for astigmatism errors by using directional patterns oriented at different angles to determine Z-height measurements, and combines this with static optical error correction to provide accurate Z-height measurements independent of feature orientation and location within the field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional focus measurement methods are used, then the system is simple to operate, but Z-height measurement accuracy deteriorates due to lens aberrations like astigmatism
Solution Approach 1:
The system performs preliminary characterization of lens aberrations by measuring focus positions for directional patterns at multiple orientations before actual inspection. This pre-characterization data is stored and used to correct subsequent measurements, eliminating the need for complex real-time corrections during inspection.
Solution Approach 2:
Directional patterns serving as intermediary test objects are introduced to characterize lens aberrations. These patterns with known orientations act as mediators between the lens and the actual workpiece, allowing the system to measure and correct optical errors without requiring complex direct measurements of the workpiece itself.
2Extent of automation
If autofocus tools are used to obtain best focus position, then automation is improved, but measurement reliability deteriorates due to orientation-dependent focus errors
Solution Approach 1:
The system implements feedback by using measured focus positions from directional patterns at different orientations to generate correction values. These correction values are fed back into the autofocus algorithm to compensate for orientation-dependent errors, allowing automated measurement while maintaining reliability.
Solution Approach 2:
The system changes measurement parameters by evaluating focus metrics for directional patterns at multiple orientations (0°, 45°, 90°, 135°) rather than a single orientation. This multi-parameter approach allows the system to identify and correct astigmatism errors, improving reliability while maintaining automation.
3Productivity
If single orientation measurement is used, then measurement speed is improved, but measurement precision deteriorates due to astigmatism errors
Solution Approach 1:
The system performs preliminary measurements at multiple orientations to characterize astigmatism errors, then uses these characterization results to correct subsequent measurements. This allows rapid inspection of actual workpieces while the precision benefits of multi-orientation characterization are applied through correction algorithms.
Solution Approach 2:
The system performs more measurements than strictly necessary by characterizing lens aberrations using directional patterns at multiple orientations. This excessive characterization action provides correction data that improves precision for all subsequent measurements, including those taken at single orientations during production inspection.
Data Source
AI summary
A system and method for correcting surface height measurements for optical aberration is provided. Heights determined by an autofocus tool, which may depend on surface feature angles in a focus region of interest (ROI) and on the ROI location in the field of view, are corrected based on a novel error calibration. Error calibration data includes height corrections for different feature angles in images, and for multiple locations in a field of view. Height corrections are determined by weighting and combining the angle dependent error calibration data, e.g., based on a gradient (edge) angle distribution determined in the ROIs. When Z-heights are determined for multiple ROIs in a field of view, storage of image data from particular images of a global image stack may be efficiently controlled based on determining early in processing whether a particular image is a sufficiently focused “near-peak” focused image or not.