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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. 

This paper reviews knife-edge interferometry (KEI) capable of inspection and metrology for various engineering applications, including displacement measuring sensors for dynamic system controls and edge quality of the parts, such as cutting tools, corrosive blades, and photomask patterns. This paper includes the modeling, design, and data analysis of KEI. With the expanding market of manufacturing industries, edge topography and instrumentation technology become more and more vital to industrial manufacturing-related applications such as cutting tool wear inspection, photomask edge roughness determination, and edge corrosion propagation monitoring. Due to the limitation of measurement requirements like non-contact (photomask inspection), in-situ (cutting tool inspection), and real-time (corrosion propagation monitoring), there are only a few methods available in the market above, and those methods are based on post-processing. The KEI is capable of on-machine measurements, especially for the nanopositioning systems, providing a large working range and positioning accuracy compared with the conventional displacement sensor. This review addresses the current and future KEI technology. Here, including the theoretical approaches to KEI, this review details the data analy 

Abstract We present a feature-selective segmentation and merging technique to achieve spatially resolved surface profiles of the parts by 3D stereoscopy and strobo-stereoscopy. A pair of vision cameras capture images of the parts at different angles, and 3D stereoscopic images can be reconstructed. Conventional filtering processes of the 3D images involve data loss and lower the spatial resolution of the image. In this study, the 3D reconstructed image was spatially resolved by automatically recognizing and segmenting the features on the raw images, locally and adaptively applying super-resolution algorithm to the segmented images based on the classified features, and then merging those filtered segments. Here, the features are transformed into masks that selectively separate the features and background images for segmentation. The experimental results were compared with those of conventional filtering methods by using Gaussian filters and bandpass filters in terms of spatial frequency and profile accuracy. As a result, the selective feature segmentation technique was capable of spatially resolved 3D stereoscopic imaging while preserving imaging features. 

Abstract This paper presents a novel thickness profile measuring system that measures double-sided thin pipe wall surfaces in a non-contact, continuous, cosine error-free, and fast manner. The surface metrology tool path was developed to align the displacement sensors always normal to the double-sided surfaces to remove cosine error. A pair of capacitive-type sensors that were placed on the rotary and linear axes simultaneously scans the inner and outer surfaces of thin walls. Because the rotational error of the rotary axis can severely affect the accuracy in thickness profile measurement, such error was initially characterized by a reversal method. It was compensated for along the rotational direction while measuring the measurement target. Two measurement targets (circular and elliptical metal pipe-type thin walls) were prepared to validate the developed measurement method and system. Not only inner and outer surface profiles but also thin-wall thickness profiles were measured simultaneously. Based on the output data, the circularity and wall thickness variation were calculated. The thickness profile results showed a good agreement with those obtained by a contact-type micrometer (1-µm resolution) at every 6-deg interval. The uncertainty budget for this measuring system with metrology tool path planning was estimated at approximately 1.4 µm. 

This paper presents a novel noncontact measurement and inspection method based on knife-edge diffraction theory for corrosive wear propagation monitoring at a sharp edge. The degree of corrosion on the sharp edge was quantitatively traced in process by knife-edge interferometry (KEI). The measurement system consists of a laser diode, an avalanche photodiode, and a linear stage for scanning. KEI utilizes the interferometric fringes projected on the measurement plane when the light is incident on a sharp edge. The corrosion propagation on sharp edges was characterized by analyzing the difference in the two interferometric fringes obtained from the control and measurement groups. By using the cross-correlation algorithm, the corrosion conditions on sharp edges were quantitatively quantified into two factors: lag and similarity for edge loss and edge roughness, respectively. The KEI sensor noise level was estimated at 0.03% in full scale. The computational approach to knife-edge diffraction was validated by experimental validation, and the computational error was evaluated at less than 1%. Two sets of razor blades for measurement and control groups were used. As a result, the lag will be increased at an edge loss ratio of 1.007/µm due to the corrosive wear, while the similarity will be decreased at a ratio of$5.4\times <\#comment/>{10}^{-<\#comment/>4}/\text{µ<\#comment/>}\mathrm{m}$with respect to edge roughness change. Experimental results showed a good agreement with computational results.

 

Drilling and milling operations are material removal processes involved in everyday conventional productions, especially in the high-speed metal cutting industry. The monitoring of tool information (wear, dynamic behavior, deformation, etc.) is essential to guarantee the success of product fabrication. Many methods have been applied to monitor the cutting tools from the information of cutting force, spindle motor current, vibration, as well as sound acoustic emission. However, those methods are indirect and sensitive to environmental noises. Here, the in-process imaging technique that can capture the cutting tool information while cutting the metal was studied. As machinists judge whether a tool is worn-out by the naked eye, utilizing the vision system can directly present the performance of the machine tools. We proposed a phase shifted strobo-stereoscopic method (Figure 1) for three-dimensional (3D) imaging. The stroboscopic instrument is usually applied for the measurement of fast-moving objects. The operation principle is as follows: when synchronizing the frequency of the light source illumination and the motion of object, the object appears to be stationary. The motion frequency of the target is transferring from the count information of the encoder signals from the working rotary spindle. If small differences are added to the frequency, the object appears to be slowly moving or rotating. This effect can be working as the source for the phase-shifting; with this phase information, the target can be whole-view 3D reconstructed by 360 degrees. The stereoscopic technique is embedded with two CCD cameras capturing images that are located bilateral symmetrically in regard to the target. The 3D scene is reconstructed by the location information of the same object points from both the left and right images. In the proposed system, an air spindle was used to secure the motion accuracy and drilling/milling speed. As shown in Figure 2, two CCDs with 10X objective lenses were installed on a linear rail with rotary stages to capture the machine tool bit raw picture for further 3D reconstruction. The overall measurement process was summarized in the flow chart (Figure 3). As the count number of encoder signals is related to the rotary speed, the input speed (unit of RPM) was set as the reference signal to control the frequency (f0) of the illumination of the LED. When the frequency was matched with the reference signal, both CCDs started to gather the pictures. With the mismatched frequency (Δf) information, a sequence of images was gathered under the phase-shifted process for a whole-view 3D reconstruction. The study in this paper was based on a 3/8’’ drilling tool performance monitoring. This paper presents the principle of the phase-shifted strobe-stereoscopic 3D imaging process. A hardware set-up is introduced, , as well as the 3D imaging algorithm. The reconstructed image analysis under different working speeds is discussed, the reconstruction resolution included. The uncertainty of the imaging process and the built-up system are also analyzed. As the input signal is the working speed, no other information from other sources is required. This proposed method can be applied as an on-machine or even in-process metrology. With the direct method of the 3D imaging machine vision system, it can directly offer the machine tool surface and fatigue information. This presented method can supplement the blank for determining the performance status of the machine tools, which further guarantees the fabrication process.