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This paper presents a novel nondestructive testing system, magneto-eddy-current sensor (MECS), to enable surface profiling of dissimilar materials by combining magnetic sensing for ferromagnetic materials and eddy-current sensing for nonferromagnetic materials. The interactions between an electromagnetic field and nonferromagnetic surface and between a magnetic field and ferromagnetic surface were measured by the MECS. The MECS consists of a conic neodymium magnet and a copper coil wound around the magnet. Aluminum and steel surfaces bonded together were prepared to test nondestructive surface profiling of dissimilar materials by the MECS. The interactions between an electromagnetic field and aluminum surface were characterized by monitoring the impedance of the coil, and the interactions between a magnetic field and steel surface were characterized by using a force sensor attached to the neodymium magnet. The magnetic and electromagnetic effects were numerically analyzed by the finite element model. The developed MECS showed the following performance: measurement spot size 5 mm and 10 mm, dynamic measurement bandwidth (eddy-current sensing 1 kHz and magnetic sensing 200 Hz), measuring range 25 mm and 17 mm, polynomial fitting error 0.51% and 0.50%, and resolution 0.655 µm and 0.782 µm for nonferromagnetic and ferromagnetic surface profiling, respectively. This technique was also applied to surface profiling and inspection of the rivet joining sheet materials. The results showed that the MECS is capable of nondestructively monitoring and determining the riveting quality in a fast, large-area, low-cost, convenient manner. 

The preparation of defect-free wafers serves as a critical stage prior to fabrication of devices or chips as it is not possible to pattern any devices or chips on a defected wafer. Throughout the semiconductor process, various defects are introduced, including random particles that necessitate accurate identification and control. In order to effectively inspect particles on wafers, this study introduces a wafer particle inspection technique that utilizes computer vision based on HSV (hue-saturation-value) color space transformation models to detect and to classify different particles by types. Artificially generated particle images based on their color properties were used to verify HSV color space models of each particle and to demonstrate how the proposed method efficiently classifies particles by their types with minimum crosstalk. A high-resolution microscope consisting of an imaging system, illumination system, and spectrometer was developed for the experimental validation. Micrometer-scale particles of three different types were randomly placed on the wafers, and the images were collected under the exposed white light illumination. The obtained images were analyzed and segmented by particle types based on pre-developed HSV color space models specified for each particle type. By employing the proposed method, the presence of particles on wafers can be accurately detected and classified. It is expected to inspect and classify various wafer particles in the defect binning process. 

This paper introduces a simple three-dimensional (3D) stereoscopic method using a single unit of an imaging device consisting of a charge-coupled device (CCD) and a zoom lens. Unlike conventional stereoscopy, which requires a pair of imaging devices, 3D surface imaging is achieved by 3D image reconstruction of two images obtained from two different camera positions by scanning. The experiments were performed by obtaining two images of the measurement target in two different ways: (1) by moving the object while the imaging device is stationary, and (2) by moving the imaging device while the object is stationary. Conventional stereoscopy is limited by disparity errors in 3D image reconstruction because a pair of imaging devices is not ideally identical and alignment errors are always present in the imaging system setup. The proposed method significantly reduced the disparity error in 3D image reconstruction, and the calibration process of the imaging system became simple and convenient. The proposed imaging system showed a disparity error of 0.26 in the camera pixel.