KEYWORDS: High dynamic range imaging, Fringe analysis, Deep learning, Cameras, 3D metrology, Projection systems, Optical spheres, Reflectivity, Neural networks, Metals
Fringe projection profilometry (FPP) technology, renowned for its stable and high-precision characteristics, is widely employed in three-dimensional surface measurements of objects. Whether utilizing deep learningbased methods or traditional multi-frequency, multi-step fringe analysis techniques, both require acquiring highquality stripe patterns modulated by the three-dimensional surface of the object. However, the limited dynamic range of cameras makes it difficult to capture effective fringe information in a single exposure, and while multiexposure methods can address this issue, they are inefficient. To address this, this study proposes an end-to-end neural network approach for generating high dynamic range (HDR) fringe patterns from projected gratings. Additionally, an end-to-end network is employed to solve the fringe phase. Experimental results demonstrate that this method significantly improves fringe pattern recovery on metallic surfaces with overexposed or underexposed regions. On a high dynamic range reflectivity dataset, the method achieved a phase error of 0.02072, successfully reconstructing 3D objects with only 8.3% of the time required by the 12-step Phase Shifting Profilometry (PSP) method. Furthermore, on standard spherical and planar objects, the method achieved a radius accuracy of 53.1 μm and flatness accuracy of 61.7 μm, demonstrating effective measurement precision without the need for additional steps. This method is effective for both high dynamic range reflective and non-high dynamic range reflective objects.
Fringe Projection Profilometry (FPP) faces challenges with objects of varying surface reflectivity, as projected light can exceed the camera’s dynamic range, hindering effective fringe capture. Current solutions using repeated projections with varying exposures increase measurement time, limiting real-time applicability. This study validates deep neural networks that transform traditional multi-frequency, multi-step, multi-exposure methods into a single-step, multi-exposure format, significantly reducing measurement time while maintaining accuracy. Experimental results demonstrate that deep learning methods can effectively extract phase information from modulated fringe images, unwrap it, and reconstruct 3D point clouds. On high-reflectivity metal datasets, the accuracy of the deep learning approach closely matches that of the traditional six-step method, while using only 16.7% of the time. For standard objects, the accuracy reaches up to 60 microns. These findings confirm that various deep learning methods can efficiently resolve phase information in modulated fringe patterns, significantly enhancing measurement speed.
Laser processing of micro-via arrays is a critical technology in the electronic packaging industry, essential for the rapid, non-destructive inspection of the geometric shape and depth of blind vias. As chip packaging processes trend towards higher density and miniaturization, the demands on blind via array detection technology are increasing. This paper proposes a fast blind via array inspection method based on dispersive spectroscopy confocal technology. By mounting the probe on a three-axis kinematic stage, auto-focusing is achieved, enabling rapid scanning imaging over a 10 mm × 10 mm area to acquire 3D point cloud data.
We have developed an effective algorithm to filter noise from the 3D point cloud data and align the line scan data, reconstructing accurate geometric profile information of the blind vias with sub-micron inspection accuracy. Tested on copper-clad board blind via arrays, this method quickly and accurately detects the geometric parameters of blind vias, providing a powerful tool for real-time monitoring of blind via processing quality and a novel solution for quality control in electronic packaging, including BGA packaging. The method offers advantages such as fast measurement speed, wide measurement range, and non-destructive, non-contact operation, with broad application prospects in the electronics manufacturing industry. Compared to existing technologies, our proposed measurement method is faster, offers higher resolution, and covers a wider measurement range, meeting the increasing requirements for blind via detection in future chip packaging processes. Furthermore, this technology can be extended to size and morphology inspection in other micro-nano processing fields, offering significant theoretical and practical value.
In modern scientific research and industrial applications, the rapid, automated, and accurate measurement of micro-liquid volumes added to reaction or detection containers is a critical need. Traditional methods for measuring micro-liquid volumes often suffer from insufficient accuracy, low stability, and are prone to interference from bubbles between microliquids and residual droplets in the transmission pipelines. To address these issues, this paper proposes an automated microliquid metering method and system based on machine vision. The system comprises optical imaging units, drive control units, image processing units, metering algorithm units, and calibration units. By optimizing the optical imaging setup, the brightness and contrast of the liquid in the metering field are enhanced, ensuring the accuracy of the volume measurement. Additionally, image processing algorithms are employed to segment the liquid section, and its length in the pixel coordinate system is extracted as a representation of the volume, effectively eliminating the interference from bubbles in the image. Finally, calibration-based measurement methods and direct measurement methods based on homography matrix scale transformation of marker points achieve metering accuracies of 98.2% and 98.3%, respectively. Compared to traditional industrial micro-liquid metering methods, this approach effectively overcomes the impact of bubbles on measurement accuracy while offering greater stability and reliability.
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