Comparison of three focus sensors for optical topography measurement of rough surfaces

Šarbort, Martin; Holá, Miroslava; Pavelka, Jan; Schovánek, Petr; Řeřucha, Šimon; Oulehla, Jindřich; Fořt, Tomáš; Lazar, Josef

doi:10.1364/oe.27.033459

Cited by 10 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data detection and then minimization of the influence of the measurement errors that occurred during the surface texture measurement process seems to be a compelling issue, especially when accurate assessments of surface topography parameters are reasonably required. The errors that occur while measurement proceeds are often defined as a noise [ 3 ], e.g., instrument or instrument white [ 4 ], random or random height [ 5 ], phase [ 6 ], signal-to-ratio [ 7 ], RMS height [ 8 ] or, simply, measurement noise [ 9 ]. Furthermore, the process of removal or reduction of those types of measurement uncertainty is defined as a denoising procedure [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Podulka

2021

Materials

View full text Add to dashboard Cite

The metrology of so-called “engineering surfaces” is burdened with a substantial risk of both measurement and data analysis errors. One of the most encouraging issues is the definition of frequency-defined measurement errors. This paper proposes a new method for the suppression and reduction of high-frequency measurement errors from the surface topography data. This technique is based on comparisons of alternative types of noise detection procedures with the examination of profile (2D) or surface (3D) details for both measured and modelled surface topography data. In this paper, the results of applying various spline filters used for suppressions of measurement noise were compared with regard to several kinds of surface textures. For the purpose of the article, the influence of proposed approaches on the values of surface topography parameters (from ISO 25178 for areal and ISO 4287 for profile standards) was also performed. The effect of the distribution of some features of surface texture on the results of suppressions of high-frequency measurement noise was also closely studied. Therefore, the surface topography analysis with Power Spectral Density, Autocorrelation Function, and novel approaches based on the spline modifications or studies of the shape of an Autocorrelation Function was presented.

show abstract

Section: Introductionmentioning

confidence: 99%

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Podulka

2021

Materials

View full text Add to dashboard Cite

show abstract

“…The calculated ultrathin metalens ( Figure 12 a) with a high numerical aperture and a small diameter, which alone replaces the entire optical scheme in Figure 8 a (and also replaces a focusing lens with a numerical aperture close to 1), may serve as a prototype of a compact focusing sensor for optical topography measurement of rough surfaces [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

“…i cos 2φ −i sin 2φ sin 2φ cos 2φ = e iπ/4 e iπ/4 0 0 e −iπ/4 cos 2φ − sin 2φ sin 2φ cos 2φ . (20) The calculated ultrathin metalens (Figure 12a) with a high numerical aperture and a small diameter, which alone replaces the entire optical scheme in Figure 8a (and also replaces a focusing lens with a numerical aperture close to 1), may serve as a prototype of a compact focusing sensor for optical topography measurement of rough surfaces [37]. The metalens transmission function (Figure 12a) without the Fresnel zone plate is described by the Jones matrix, which is the product of the rotation matrix of the linear polarization vector, the angle 2φ, and the matrix of a quarter-wave plate, which converts the linear polarization to elliptical or circular polarization.…”

Section: R(φ) =mentioning

confidence: 99%

Spin-Orbital Conversion of a Strongly Focused Light Wave with High-Order Cylindrical–Circular Polarization

Kotlyar

Stafeev

Козлова

et al. 2021

Sensors

View full text Add to dashboard Cite

We discuss interesting effects that occur when strongly focusing light with mth-order cylindrical–circular polarization. This type of hybrid polarization combines properties of the mth-order cylindrical polarization and circular polarization. Reluing on the Richards-Wolf formalism, we deduce analytical expressions that describe E- and H-vector components, intensity patterns, and projections of the Poynting vector and spin angular momentum (SAM) vector at the strong focus. The intensity of light in the strong focus is theoretically and numerically shown to have an even number of local maxima located along a closed contour centered at an on-axis point of zero intensity. We show that light generates 4m vortices of a transverse energy flow, with their centers located between the local intensity maxima. The transverse energy flow is also shown to change its handedness an even number of times proportional to the order of the optical vortex via a full circle around the optical axis. It is interesting that the longitudinal SAM projection changes its sign at the focus 4m times. The longitudinal SAM component is found to be positive, and the polarization vector is shown to rotate anticlockwise in the focal spot regions where the transverse energy flow rotates anticlockwise, and vice versa—the longitudinal SAM component is negative and the polarization vector rotates clockwise in the focal spot regions where the transverse energy flow rotates clockwise. This spatial separation at the focus of left and right circularly polarized light is a manifestation of the optical spin Hall effect. The results obtained in terms of controlling the intensity maxima allow the transverse mode analysis of laser beams in sensorial applications. For a demonstration of the proposed application, the metalens is calculated, which can be a prototype for an optical microsensor based on sharp focusing for measuring roughness.

show abstract

“…The errors that occur while the measurement process takes place are often defined as noise [18]. From all of the studied types of measurement noises, e.g., instrument or instrument white [19], random [20], phase [21], signal-to-ratio [22] or, simply, the measurement noise [23], the high-frequency domain (high-frequency measurement noise [24]) is most commonly studied in recent research.…”

Section: Introductionmentioning

confidence: 99%

Selection of Methods of Surface Texture Characterisation for Reduction of the Frequency-Based Errors in the Measurement and Data Analysis Processes

Podulka

2022

Sensors

View full text Add to dashboard Cite

Processes of surface texture characterisation can be roughly divided into measurement issues and analysis of the results obtained. Both actions can be fraught with various errors, some of which can be analysed with frequency performance. In this paper, various types of surface topographies were studied, e.g., cylinder liners after the plateau-honing process, plateau-honed liners with additionally burnished dimples of various sizes (width and depth), turned, milled, ground, laser-textured, ceramic, composite and some general isotropic topographies, respectively. They were measured with a stylus or via optical (white light interferometry) methods. They were analysed with frequency-based methods, proposed in often applied measuring equipment, e.g., power spectral density, autocorrelation function and spectral analysis. All of the methods were supported by regular (commonly used) algorithms, or filters with (robust) Gaussian, median, spline or Fast Fourier Transform performance, respectively. The main purpose of the paper was to use regular techniques for the improvement of detection and reduction processes regarding the influence of high-frequency noise on the results of surface texture measurements. It was found that for selected types of surface textures, profile (2D) analysis gave more confidential results than areal (3D) characterisation. It was therefore suggested to detect and remove frequency-defined errors with a multi-threaded performance application. In the end, some guidance on how to use regular methods in the analysis of selected types of surface topographies following the reduction of both measurement (high-frequency noise) and data analysis errors was required.

show abstract

Comparison of three focus sensors for optical topography measurement of rough surfaces

Cited by 10 publications

References 20 publications

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Spin-Orbital Conversion of a Strongly Focused Light Wave with High-Order Cylindrical–Circular Polarization

Selection of Methods of Surface Texture Characterisation for Reduction of the Frequency-Based Errors in the Measurement and Data Analysis Processes

Contact Info

Product

Resources

About