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Topographical as well as microscopic imaging of nanoscale surfaces plays a pivotal role across various disciplines. Nevertheless, achieving fast, label-free, and accurate characterization of laterally expanded structures below the diffraction limit remains challenging. Recent studies highlight the use of microsphere assistance for resolution improvement. Confocal microscopy, augmented by microspheres, enables the imaging of small structures that were previously inaccessible. This is experimentally compared with microsphere-assisted microscopy (MAM) to underline the decisive role of the confocal effect.

We present how to develop virtual microcylinder- or microsphere-assisted surface topography measurement instruments. As the most critical part, the interaction between light, microcylinder and measurement object is considered based on the finite element method (FEM). Results are obtained for microcylinder-assisted conventional, interference, and confocal microscopes without necessity to repeat the time-consuming FEM simulations for each sensor.

The topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. This has proven beneficial in order to distinguish the focus region from out-of-focus areas where outliers are frequently detected. This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. Additionally, a simulation model of focus variation microscopy is introduced to validate both the measuring system and the proposed algorithm, thereby enhancing the overall performance of focus variation microscopy. Finally, comparisons of measurement results on rough metal additive manufacturing workpieces with and without self-adaptive thresholds are discussed to demonstrate the algorithm's effectiveness.The utilization of self-adaptive thresholds demonstrably reduces the uncertainty range in roughness parameter calculations. For example, in the case of an additive manufactured metal sample due to outlier elimination, the S z roughness value reduces from 543 µm to 413 µm. [ABSTRACT FROM AUTHOR]

Due to their contactless and fast measuring capabilities, laser interferometers represent an interesting alternative to tactile stylus instruments for surface profilometry. In addition to these outstanding attributes, acquisition costs play a major role in industry, limiting the frequent use of optical profilometers, which are significantly more expensive than tactile profilometers. We present a low-cost laser distance-measuring interferometer featuring axial repeatability below 1 nm at acquisition rates of 38,000 height values per second. The sensor's performance is validated on several surface standards, achieving lateral scan velocities up to 160 mm/s. Further to high scan velocities, the high acquisition rate enables improved measurement accuracy by averaging measured height values. For example, the standard deviation of 625 pm for repeated measurements can be reduced to 265 pm at the expense of the data rate. However, the sensor concept provides the potential for further improvements in data rate and measurement repeatability. [ABSTRACT FROM AUTHOR]

The depth discrimination in confocal microscopy is based on the digital analysis of depth response signals obtained by each camera pixel during measurement. Various signal‐processing algorithms are used for this purpose. The accuracy of these algorithms is inter alia restricted by the axial symmetry of the signals. However, in practice response signals are rather asymmetrical especially in case of measurement objects with critical surface structures such as edges or steep flanks. We present a novel signal‐processing algorithm based on an exponential function with a cubic argument to handle asymmetrical and also symmetrical depth response signals. Results obtained by this algorithm are compared to those of commonly used signal processing algorithms. It turns out that the novel algorithm is more robust, more accurate and exhibits a repeatability of a similar order compared to other algorithms. Research Highlights: A novel, more robust algorithm with improved accuracy in peak extraction especially for asymmetrical response signals in confocal microscopy is introduced and validated.Improved accuracy is demonstrated for height and layer thickness measurements. [ABSTRACT FROM AUTHOR]