Machine Vision Approach for Improving Accuracy of Focus-Based Depth Measurements

摘要

Focus-based depth (Z) measurements are used extensively in industrial metrology and microscopy. Typically, a peak in the focus figure-of-merit of a region is found while moving the lens towards or away from the surface, allowing local recovery of depth. These focus-based measurements are susceptible to errors caused by: (1) Optical aberrations and characteristics of the lens (astigmatism, field curvature); (2) Optical and image sensor misalignments; (3) Image sensor shape errors. Depth measurements of the same artifact can therefore significantly vary depending on the prevailing orientation of the surface texture (due to lens astigmatism) or on the specific position in the field of view. We present a vision-based algorithm to reduce errors in focus-based depth measurements. The algorithm consists of two steps: 1. Offline calibration: We generate a calibration table for the optical system, consisting of a set of Z calibration curves for different locations in the field of view. 2. Run-time correction: During measurement, we determine the Z correction to the focus position using the stored Z calibration curves and a measurement of the local orientation of the surface texture. In our tests, the correction algorithm reduced the depth measurement errors by a factor of 2, on average, for a wide range of surfaces and conditions.