A review of surface roughness measurements based on laser speckle method

Shao, Meiqi; Xu, Dong; Li, Siyi; Zuo, Xiao-gang; Chen, Chang-ke; Peng, Gongzhuang; Zhang, Jiamin; Wang, Xiaochen; Yang, Quan

doi:10.1007/s42243-023-00930-8

Cited by 6 publications

(3 citation statements)

References 65 publications

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“…The selection of a diffuser depends on specific application requirements, such as desired spatial resolution and sensitivity to surface roughness variations. Further research 37 , 38 could focus on exploring advanced diffuser materials with controlled roughness profiles and optimizing the curvature of the probe light to enhance the performance of speckle imaging systems across various applications.…”

Section: Discussionmentioning

confidence: 99%

Lens-free reflective topography for high-resolution wafer inspection

Lee,

Sung,

Park

et al. 2024

Sci Rep

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The demand for high-resolution and large-area imaging systems for non-destructive wafer inspection has grown owing to the increasing complexity and extremely fine nature of semiconductor processes. Several studies have focused on developing high-resolution imaging systems; however, they were limited by the tradeoff between image resolution and field of view. Hence, computational imaging has arisen as an alternative method to conventional optical imaging, aimed at enhancing the aforementioned parameters. This study proposes a method for improving the resolution and field of view of an image in a lens-less reflection-type system. Our method was verified by computationally restoring the final image from diffraction images measured at various illumination positions using a visible light source. We introduced speckle illumination to expand the numerical aperture of the entire system, simultaneously improving image resolution and field of view. The image reconstruction process was accelerated by employing a convolutional neural network. Using the reconstructed phase images, we implemented high-resolution topography and demonstrated its applicability in wafer surface inspection. Furthermore, we demonstrated an ideal diffraction-limited spatial resolution of 1.7 μm over a field of view of 1.8 × 1.8 mm2 for the topographic imaging of targets with various surface roughness. The proposed approach is suitable for applications that simultaneously require high throughput and resolution, such as wafer-wide integrated metrology, owing to its compact design, cost-effectiveness, and mechanical robustness.

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Section: Discussionmentioning

confidence: 99%

Lens-free reflective topography for high-resolution wafer inspection

Lee,

Sung,

Park

et al. 2024

Sci Rep

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“…surface quality. The use of dynamic structure analysis becomes a valuable tool in the measurement of not only cracks but also the measurement of material roughness [40]. Therefore, the current limitation of measurements only in the field of cracks can be successfully extended.…”

Section: Image Resolutionmentioning

confidence: 99%

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Świłło,

Cacko

2023

Adv. Sci. Technol. Res. J.

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The sheet metal surface crack detection during manufacturing is an essential issue because of both the product quality and process productivity. Development of solutions to eliminate defective products during the metal forming process is crucial for the smooth production and for developing an appropriate tool geometry in the initial phase of the process. Currently, the methods of surface crack detection used in the industry are mostly related to visual inspection. These are methods that require operators of industrial facilities considerable attention and effort to capture emerging discontinuities on the sheet metal surface. Also, this situation results increase in the duration of the specific operations of stamping and significantly reduces productivity. Therefore, an industrial application of a non-contact laser technique that simultaneously provides the results of the speckle imaging is presented. The authors demonstrate a specially designed machine vision system along with experimental tools for the stamping operation. Proposed solution uses the phenomenon of speckle pattern that appears in the image of the investigated sheet surface produced by the laser beam emission. In this method, coherent laser light is emitted to the surface, where a speckle pattern is generated due to scatter reflection from the sheet metal surface and then, shift-and-add technique and image processing is applied. The proposed measurement technique consists, initially, of making a sequence of images of the tested object for the moving surface of the sheet. Secondly, the object's displacement quantity in each image is determined, and the position is corrected. The test object in each image is moved to the starting position, and all images are superimposed. It allows to obtain a high-quality image with visible surface defects. Finally, the dynamically changing speckle pattern intensity is evaluated using Gaussian-of-Laplacian edge detection to investigate a surface crack location due to the surface discontinues and light scattering. This process is recommended for machine vision imaging of distant objects, which works well in industrial conditions as well as online analysis. Also, from the speckle size measurement, an experimental procedure is employed to verify the best condition for vision system resolution.

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“…Surface roughness is used to represent the peaks and valleys of the tiny spacing on the processed surface, and this unevenness has a significant impact on the performance and lifespan of the parts. [1,2] The currently widely used surface roughness measurement method is stylus measurement, which measures the roughness by sliding the stylus probe on the workpiece surface. Its DOI: 10.1002/adts.202301100 shortcomings include a small measurement range, easy scratching of the workpiece surface, and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Yi,

Feng,

Shu

2024

Advcd Theory and Sims

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The surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.

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A review of surface roughness measurements based on laser speckle method

Cited by 6 publications

References 65 publications

Lens-free reflective topography for high-resolution wafer inspection

Lens-free reflective topography for high-resolution wafer inspection

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

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