An innovative 3-D surface imaging methodology for reconstructing micro surface profiles with a long depth measuring range and a nano-scale resolution was developed using the newly developed Mirau-based lateral scanning interferometry (LSI). The current measuring field of view (FOV) of conventional white light interferometers is limited by microscopic views of the existing interferometric objectives, such as those in Michelson, Mirau or Linnik designs. Moreover, the vertical scanning operation required for acquiring volumetric interferometric data is extremely time-consuming and makes white light vertical scanning interferometry (VSI) infeasible for automatic optical inspection (AOI) of micro 3-D structures. To resolve this, a newly developed white light LSI method based on Mirau’s optical configuration was developed by controlling the tilting angle of the reference mirror in the Mirau interferometric objective. With the proposed optical configuration, the surface is inspected at a tilting angle with respect to the maximum coherence plane of the interferometric system along its lateral scanning direction when the objective lies perpendicular to the tested surface. In addition, a system calibration method was developed to establish an accurate mathematical mapping model between the object depth and the lateral axis. To evaluate the feasibility of the methodology, a calibrated step height was measured for evaluating the accuracy and repeatability. Some industrial samples, such as photon spacers and other microstructures fabricated by nano-imprinting processes, were measured to verify the actual performance on real components. It was found that the measurement repeatability was controlled less than 60 nm within one standard deviation for a maximum measurable depth of 27.21 µm.
The article presents the development of in-situ integrated circuit (IC) chip defect detection techniques for automated clipping detection by proposing infrared imaging and full-field volumetric topography. IC chip inspection, especially held during or post IC packaging, has become an extremely critical procedure in IC fabrication to assure manufacturing quality and reduce production costs. To address this, in the article, microscopic infrared imaging using an electromagnetic light spectrum that ranges from 0.9 to 1.7 µm is developed to perform volumetric inspection of IC chips, in order to identify important defects such as silicon clipping, cracking or peeling. The main difficulty of infrared (IR) volumetric imaging lies in its poor image contrast, which makes it incapable of achieving reliable inspection, as infrared imaging is sensitive to temperature difference but insensitive to geometric variance of materials, resulting in difficulty detecting and quantifying defects precisely. To overcome this, 3D volumetric topography based on 3D infrared confocal measurement with active structured light, as well as light refractive matching principles, is developed to detect defects the size, shape and position of defects in ICs. The experimental results show that the algorithm is effective and suitable for in-situ defect detection of IC semiconductor packaging. The quality of defect detection, such as measurement repeatability and accuracy, is addressed. Confirmed by the experimental results, the depth measurement resolution can reach up to 0.3 µm, and the depth measurement uncertainty with one standard deviation was verified to be less than 1.0% of the full-scale depth-measuring range.
This paper proposes a novel method employing a developed 3-D optical imaging and processing algorithm for accurate classification of an object’s surface characteristics in robot pick and place manipulation. In the method, 3-D geometry of industrial parts can be rapidly acquired by the developed one-shot imaging optical probe based on Fourier Transform Profilometry (FTP) by using digital-fringe projection at a camera’s maximum sensing speed. Following this, the acquired range image can be effectively segmented into three surface types by classifying point clouds based on the statistical distribution of the normal surface vector of each detected 3-D point, and then the scene ground is reconstructed by applying least squares fitting and classification algorithms. Also, a recursive search process incorporating the region-growing algorithm for registering homogeneous surface regions has been developed. When the detected parts are randomly overlapped on a workbench, a group of defined 3-D surface features, such as surface areas, statistical values of the surface normal distribution and geometric distances of defined features, can be uniquely recognized for detection of the part’s orientation. Experimental testing was performed to validate the feasibility of the developed method for real robotic manipulation
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