White light interferometers are typically equipped with interference microscopes of various magnifications to meet the topography measurement needs for different scenarios. Low-magnification interferometric microscopes have a large field of view and a small numerical aperture (NA), enabling them to capture low-frequency information from the sample’s surface topography and measure macro-scale structures. High-magnification interference microscopes have a small field of view but a large NA, providing higher lateral resolution and a higher cutoff frequency. These are ideal for detecting microscopic topography parameters with high-frequency features in fine structures. Compared to low-magnification interference microscopes, the larger NA allows for the collection of returning light beams at steeper angles, facilitating the detection of sharper slopes and providing more accurate surface topography measurements for curved samples. To simultaneously characterize both the micro-topography features and macro-scale features in the measured topography data, the conventional method involves performing a stitching scan across the lateral range using a high-magnification interference microscope and then fusing multiple sets of measurement data. However, fine microstructures are not evenly distributed across the sample surface, and there are regions with low-frequency features between microstructures that do not require high-resolution detection. Including these low-frequency regions in the stitching and fusion process can reduce detection efficiency. To mitigate this, a more efficient approach can be adopted by considering the characteristics of various magnifications of interference microscopes in white light interferometers. This approach involves conducting targeted detection of local areas that reflect micro-topography features, while simultaneously meeting the requirement to capture macro-scale features. The fusion of macro-scale and micro-topography features into a single dataset helps improve efficiency.

The multi-magnification data fusion technology of white light interferometers integrates topography data from different magnification interference microscopes into a unified dataset, thus enhancing the comprehensiveness of topographic feature parameters in the fused data. In this paper, we introduce two techniques, surface fitting and wavelet decomposition fusion. After evaluating the advantages and disadvantages of these methods, we propose a strategy for fusing topography data from various magnification interference microscopes in white light microscopy interferometry based on frequency filtering. The process begins with normalized cross-correlation (NCC) and normalized iterative closest point (NICP) to achieve sub-pixel level registration of the data sets. During the subsequent data fusion stage, based on multi-porous wavelet decomposition, we analyze the cut-off frequency for each magnification microscope, which provides theoretical support for wavelet decomposition and fusion strategies. This ultimately results in a more comprehensive data fusion approach.

Our fusion method fully leverages the advantages of the low-magnification interference microscope’s large field of view and the high NA and resolution of the high-magnification interference microscope. We apply this method in fusion experiments for topography data from various surface structures. The first sample surface contains multiple convex arrays. By retaining the periodic information in the data measured by the low NA interference microscope, the curvature feature parameters are refined using high NA interference microscope data through fusion. As a result, the relative errors for the period and curvature radius of the subunits in the final fused data decrease by 1.34 percentage points and 6.00 percentage points, respectively. The second sample is a step-type structure with multiple cylinders arranged on the surface according to a specific pattern. The low-magnification interference microscope can measure the overall periodic arrangement of the structure. After fusing the high-resolution data from the step structure measured by the high-magnification interference microscope, various topography feature parameters can be simultaneously characterized. In the final fusion result, the relative error compared to the scanning electron microscope (SEM) measurement data is 0.419%.

In this paper, we propose a fusion method that decomposes topography data from various magnification interference microscopes, fuses the sub-data with the same frequency components, and retains the feature information from each dataset. The experiment, which includes two samples with different features, demonstrates how the proposed fusion method can extend the slope measurement range of the low-magnification interference microscope, reduce measurements, and improve the efficiency of the white light interferometer in analyzing both macro- and micro-topography feature parameters simultaneously. The broad applicability of this fusion method for handling various feature samples has been validated, further enhancing the functionality of white light interferometers. The multi-magnification fusion strategy proposed in this paper can be applied not only in white light interferometers but also to topography data obtained from various detection methods. It offers an effective data processing and characterization solution for instruments that integrate multiple measurement technologies, thus enhancing their capabilities.