Investigation of thin films for fabrication of Nb/AlN/NbN tunnel junctions and microstrip lines of NbTiN-SiO2-Al.

The surface of thin films of Nb, Al, NbTiN, SiO2, Al2O3 is investigated in this work. These films are necessary for the fabrication of high-sensitive devices of THz range. The fabrication processes of such devices are described briefly. All films were fabricated using a Kurt J. Lesker magnetron sputtering system. The study of the film surface roughness was carried out using a Bruker Ikon atomic force microscope. The surface quality of films is determined not only deposition mode, but plasma etching process also. The best values of the root-mean-square deviation of the surface profile Rq = 2 nm were obtained for the used NbTiN film with a thickness of 325 nm. Thin Al-layers that is used for tunnel barrier formation is studied. It is shown than Al films with a thickness of more than 6 nm are already continuous. The surface roughness of the single-layer and multilayer films has been studied.

CORRELATION-SPECTRAL ANALYSIS OF THE TOPOGRAPHY OF ENGINEERING SURFACES AT THE NANOSCALE LEVEL

Исследована нанотопография некоторых типичных технических поверхностей и экспериментально определены характеристики профиля наношероховатости как случайного процесса - автокорреляционная функция и спектральная плотность. Показано, что для исследованных поверхностей их профилограммы могут рассматриваться как реализации случайного стационарного нормального эргодического процесса. Проведена визуальная проверка нормальности процесса сравнением экспериментальных значений ординат профиля с теоретическими значениями, подчиняющимися нормальному распределению, а также сравнением полигона частот с теоретической функцией плотности вероятности нормального распределения. Количественное подтверждение нормальности процесса выполнено с применением критерия согласия Колмогорова. Показано, что на уровне значимости p = 0,05 гипотеза о нормальности случайного процесса (профиля наношероховатости поверхности) не противоречит экспериментальным результатам. Определены интервалы корреляции рассмотренных процессов. Вид автокорреляционных функций и величины интервалов корреляции говорят о случайном характере профиля поверхности: на интервале, равном одному - двум средним значениям шага неровностей профиля его ординаты становятся практически некоррелированными. Графики спектральных плотностей свидетельствуют о том, что профиль поверхности можно рассматривать как широкополосный случайный шум с преобладанием низкочастотных составляющих. The nanotopography of some typical technical surfaces is investigated and the characteristics of the nanoroughness profile as a random process are experimentally determined - the autocorrelation function and spectral density. It is shown that for the investigated surfaces, their profilograms can be considered as realizations of a random stationary normal ergodic process. A visual check of the process normality was carried out by comparing the experimental values of the profile ordinates with theoretical values obeying the normal distribution, as well as by comparing the frequency polygon with the theoretical probability density function of the normal distribution. Quantitative confirmation of the process normality was carried out using the Kolmogorov goodness-of-fit test. It is shown that at the significance level p = 0,05, the hypothesis about the normality of a random process (surface nanoroughness profile) does not contradict the experimental results. The correlation intervals of the considered processes are determined. The form of the autocorrelation functions and the values of the correlation intervals indicate the random nature of the surface profile: in the interval equal to one or two average values of the step of the irregularities of the profile, its ordinates become practically uncorrelated. Spectral density plots indicate that the surface profile can be considered as a wide-band random noise with a predominance of low-frequency components.

Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

Prediction of Upper Surface Roughness in Laser Powder Bed Fusion

In this study, a physics-based analytical method was proposed for the prediction of upper surface roughness in laser powder bed fusion (LPBF). The temperature distribution and molten pool shape in the melting process were first predicted by an analytical thermal model. The cap area of the solidified molten pool was assumed to be half-elliptical. Based on this assumption and the principle of mass conservation, the cap height and the specific profile of the cap area were obtained. The transverse overlapping pattern of adjacent molten pools of upper layer was then obtained, with given hatch space. The analytical expression of the top surface profile was obtained after putting this overlapping pattern into a 2D coordinate system. The expression of surface roughness was then derived as an explicit function of the process parameters and material properties, based on the definition of surface roughness (Ra) in the sense of an arithmetic average. The predictions of surface roughness were then compared with experimental measurements of 316L stainless steel for validation and show acceptable agreement. In addition, the proposed model does not rely on numerical iterations, which ensures its low computational cost. Thus, the proposed analytical method can help understand the causes for roughness in LPBF and guide the optimization of process conditions to fabricate products with good quality. The sensitivity of surface roughness to process conditions was also investigated in this study.

Step Surface Profile Measurement Based on Fringe Projection Phase-Shifting Using Selective Sampling

Fringe projection is a non-contact optical method that is widely used in the optical precision measurement of complex stepped surfaces. However, the accuracy of the fringe phase extraction employed has a direct impact on the measurement precision of the surface shape. Where phase-shifting measurement is used, the classical equal step phase extraction algorithm can only be used to measure simple and smooth surfaces, and leads to measurement errors on complex stepped surfaces, which affects the accuracy of the phase extraction. In addition, the iterative process lasts for a long time, resulting in a low efficiency. This paper proposes a step-by-step phase-shifting extraction algorithm based on selective sampling to measure the contour of the stepped surface. Firstly, the fringe pattern is sampled at equal intervals to reduce the iterative calculation time. Finally, the accurate measurement phase is calculated by the alternating iteration method. The phase extraction accuracy and iteration times are compared in experimental measurements between classical iterative algorithms such as four-step phase-shifting algorithms and the variable phase shift phase interpolation algorithm based on selective sampling. It is shown that the variable frequency phase-shifting extraction algorithm based on selective sampling has a shorter operation time, smaller error, and higher accuracy than the traditional iterative algorithm in fringe projection measuring complex stepped surfaces.

Prediction of Surface Profile Evolution of Workpiece and Lapping plate in Lapping Process

Lapping has a history of hundreds of years, yet it still relies on the experience of workers. To improve the automaticity and controllability of the lapping process, a modeling method of friction and wear is developed to predict the surface profile evolution of the workpiece and lapping plate in the lapping process. In the proposed method, by solving the balance equations of resultant force and moment, the inclination angles of the workpiece can be calculated, thus more accurate contact pressure distribution of the workpiece/lapping plate interface can be calculated. Combined with the material removal rate model, the continuous evolution process of the workpiece and lapping plate can be predicted in the lapping process. The modeling method was validated by a lapping test of a flat optical glass (Φ 100 mm) with a composite copper plate. The results show that the proposed method can predict the evolution of the surface profile of the workpiece and lapping plate with high accuracy. Consequently, the lapping plate can be dressed at the right time point. Benefit from this, in the validation test the PV value of the workpiece (with 5 mm edge exclusion) was reduced from 5.279 μm to 0.267 μm in 30 min. The proposed surface profile evolution modeling method not only improves the lapping efficiency but also provides an opportunity to understand the lapping process.

Ice ridge density signatures in high resolution SAR images

The statistics of ice ridging signatures was studied using a high (1.25 m) and a medium (20 m) resolution SAR image over the Baltic sea ice cover, acquired in 2016 and 2011, respectively. Ice surface profiles measured by a 2011 Baltic campaign was used as ground truth data for both. The images did not delineate well individual ridges as linear features. This was assigned to the random, intermittent occurrence of ridge rubble block arrangements with bright SAR return. Instead, the ridging signature was approached in terms of the density of bright pixels and relations with the corresponding surface profile quantity, ice ridge density, were studied. In order to apply discrete statistics, these densities were quantified by counting bright pixel numbers (BPN) in pixel blocks of side length L, and by counting ridge sail numbers (RSN) in profile segments of length L. The scale L is a variable parameter of the approach. The other variable parameter is the pixel intensity threshold defining bright pixels, equivalently bright pixel percentage (BPP), or the ridge sail height threshold used to select ridges from surface profiles, respectively. As a sliding image operation the BPN count resulted in enhanced ridging signature and better applicability of SAR in ice information production. A distribution model for BPN statistics was derived by considering how the BPN values change in BPP changes. The model was found to apply over wide range of values for BPP and L. The same distribution model was found to apply to RSN statistics. This reduces the problem of correspondence between the two density concepts to connections between the parameters of the respective distribution models. The correspondence was studied for the medium resolution image for which the 2011 surface data set has close temporal match. The comparison was done by estimating ridge rubble coverage in 1 km2 squares from surface profile data and, on the other hand, assuming that the bright pixel density can be used as a proxy for ridge rubble coverage. Apart from a scaling factor, both were found to follow the presented distribution model.

Influence of Wear Volume on Surface Quality in Grinding Process Based on Wear Prediction Model

In the grinding process, the workpiece would not only be cut by abrasive grains, but also have adhesive wear caused by high temperature and heavy load, which makes the surface quality of the workpiece worse. In this paper, a wear test method considering speed, force, wear coefficient, temperature and hardness was proposed. A new wear prediction physical model was established based on finite element method and numerical simulation technology. The wear test was carried out on a grinding machine. The comprehensive research on the relationship between force, temperature, surface morphology and wear volume of grinding process was studied. The relationship between workpiece speed, grinding depth, cooling lubrication conditions and wear volume of grinding process was studied. The results show that the wear model can achieve numerical prediction and trend prediction of grinding temperature, surface profile and wear volume, the relative errors between the theoretical and actual values of wear and grinding temperature are 9.84% and 2.07% respectively. This study provides a support for wear prediction and surface quality control of grinding process from the perspective of temperature and micro material removal form.