• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.167-171
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• 2021

The core-shell InGaN/GaN Multi Quantum Well-Nanowires (MQW-NWs) that were selectively grown on oxide templates with perfectly circular hole patterns were highly crystalline and were shaped as high-aspect-ratio pyramids with semi-polar facets, indicating hexagonal symmetry. The formation of the InGaN active layer was characterized at its various locations for two types of the substrates, one containing defect-free MQW-NWs with GQDs and the other containing MQW-NWs with defects by using HRTEM. The TEM of the defect-free NW showed a typical diode behavior, much larger than that of the NW with defects, resulting in stronger EL from the former device, which holds promise for the realization of high-performance nonpolar core-shell InGaN/GaN MQW-NW substrates. These results suggest that well-defined nonpolar InGaN/GaN MQW-NWs can be utilized for the realization of high-performance LEDs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.1-7
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• 2017

In this paper, the separation force measuring system is developed. The separation force aries due to adhesive strength between semiconductor epoxy molding compound(EMC) and the metal plate in semiconductor formed plate. In general, when removing the metal plate in semiconductor formed plate from semiconductor epoxy molding compound, excessive strength can result in a increase in semiconductor defect rates, or conversely, if too little force is exerted on the metal plate in semiconductor formed plate, the semiconductor production rates can decrease. In this study, the design criteria for the selection of the AC servo motor, the role of the ball screw, the relationship between the load cell and the ball screw, and the rate of deceleration are given. In addition, minimizing the reject rate of semiconductors and maximizing the semiconductor production rate are achieved through the standardization of the collected separation force data measured by the proposed system.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.239-244
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• 2001

Today, Ultrasonic is used as an important non-destructive test tool of semiconductor reliability evaluation and failure analysis. The semiconductor packaging trend goes to develop thin package, this trend makes difficult to inspect to defect in semiconductor package. One of the important problem in all semiconductor is moisture absorption in the atmosphere. This moisture causes crack or delamination to package when the semiconductor package is soldered on PCB. Reliability evaluation of semiconductor's object is investigating the effect of this moisture. For that reason, this study is investigating the effect of this moisture and reliability evaluation of semiconductor after preconditioning test and scanning acoustic microscope.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.05b
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• pp.222-232
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• 2000

The relationship between carrier electron concentration(n) and atmosphere oxygen partial pressure($P_{O_2}$ for pure ZnO calculated by the mass-action law, well-known as n ${\propto}P^{-1/m}_{O_2}$ where m = 4 or 6 for the single or the double ionization of the native donor defects due to its nonstoichiometry, respectively, is found in competition with the calculation result on the basis that the total defect concentration is the sum of those of unionized and ionized defects. Definitively, it is found inconsistent with the calculation result by employing the Fermi-Dirac(FD) statistics for their ionization processes. By application of the FD statistics law to the ionization while assuming the defect formation is still ruled by the mass-action law, the calculation results shows the concentration is proportional to $P^{-1/2}_{O_2}$ whenever they ionize singly and/or doubly. Conclusively we would like to propose the new theoretical relation n ${\propto}P^{-1/m}_{O_2}$ because the ionization processes of donors in ZnO should be treated with the electronoccupation probability at localized quantum states in its forbidden band created by the donor defects, i.e. the FD statistics

• Proceedings of the Korean Vacuum Society Conference
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• 2001.02a
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• pp.31-31
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• 2001

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.444-449
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• 1996

The semiconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip affect on the functions of the semiconductors. The defects of the chip surface are cracks or voids. As general inspection method requires many inspection procedure, the inspection system which searches immediately and precisely the defects of the semiconductor chip surface is required. We suggest the detection algorithm for inspecting the surface defects of the semiconductor surface. The proposed algorithm first regards the semiconductor surface as random texture and point spread function, and secondly presents the character of texture by linear estimation theorem. This paper assumes that the gray level of each pixel of an image is estimated from a weighted sum of gray levels of its neighbor pixels by linear estimation theorem. The weight coefficients are determined so that the mean square error is minimized. The obtained estimation window(two-dimensional estimation window) characterizes the surface texture of semiconductor and is used to discriminate the defects of semiconductor surface.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.12-18
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• 2020

In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.386-393
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• 2010

A defect in an image is a set of pixels forming an irregular shape. Since a defect, in most cases, is not easy to be modeled mathematically, the defect detection problem still resides in a research area. If a given image, however, composed by certain patterns, a defect can be detected by the fact that a non-defect area should be explained by another patch in terms of a rotation, translation, and noise. In this paper, therefore, the defect detection method for a repeated multi-patterned image is proposed. The proposed defect detection method is composed of three steps. First step is the interest point detection step, second step is the selection step of a appropriate patch size, and the last step is the decision step. The proposed method is illustrated using SEM images of semiconductor wafer samples.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.