• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.61-66
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• 2015

Recently, the major trends of NIL are high throughput and large area patterning. For UV NIL, if it can be proceeded in the non-vacuum environment, which greatly simplifies tool construction and greatly shorten process times. However, one key issue in non-vacuum environment is air bubble formation problem. In this paper, numerical analysis of bubble defect of UV NIL is performed. Fluent, flow analysis focused program was utilized and VOF (Volume of Fluid) skill was applied. For various resist-substrate and resist-mold angles, effects of velocity inlet and residual layer thickness of resist on bubble defect formation were investigated. The numerical analyses show that the increases of velocity inlet and residual layer thickness can cause the bubble defect formation, however the decreases of velocity inlet and residual layer thickness take no difference in the bubble defect formation.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.371-385
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• 1995

The yield of semiconductor chips is dependent not only on the average defect density but also on the distribution of defects over a wafer. The distribution of defects leads to consider a cluster index. This paper briefly reviews the existing yield prediction models ad proposes a new cluster index, which utilizes the information about the defect location on a wafer in terms of the coefficient of variation. An extensive simulation is performed under a variety of defect distributions and a yield prediction model is derived through the regression analysis to relate the yield with the proposed cluster index and the average number of defects per chip. The performance of the proposed simulation-based yield prediction model is compared with that of the well-known negative binomial model.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.2
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• pp.46-52
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• 1993

The estimation of yield for semiconductor devices requires not only establishment of critical area but also a new parameter of process defect density that contains inspection mean defect density related cleanness of manufacure process line, minimum feature size and the total number of mask process. We estimate the repaired yield of memory devide, leads the semiconductor technique, repaired by redundancy scheme in relation with defect density distribution function, and we confirm the repaired yield for different devices as this model. This shows the possibility of the yield estimation as statistical analysis for the condition of device related cleanness of manufacture process line, design and manufacture process.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.129-131
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• 2004

전 세계적으로 반도체 연마제용으로 silica를 많이 사용하고 있으며, 주로 fumed silica 및 colloidal silica로 구분되어진다. 반도체 연마제로서의 가장 중요한 요소는 연마율, defect 및 uniformity 등이 있으며, 현재 defect 및 uniformity는 많은 연구개발을 통하여 증진되었지만 반도체 생산량과 직접 관련된 연마율을 증가시키는 기술은 화학약품 및 slurry의 농도 증가로만 가능하다. 이에 연마제의 전반적인 기능을 상승시켜 기존보다 연마율은 높이고, 결함율을 낮추며, 120nm 이상의 입자크기를 제조하여도 근일한 입도 분포도를 나타내어주고, 장기간 안정하게 사용가능하고, 친환경적인 반도체 연마제를 개발하였다.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.59-64
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• 2020

In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.54-58
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• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Journal of Korean Vacuum Science & Technology
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• v.3 no.1
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• pp.1-9
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• 1999

This paper presents a newly enhanced damage model in Monte Carlo (MC) simulation for the accurate prediction of 3-Dimensional (3D) as-implanted impurity and point defect profiles induced by ion implantation in (100) crystal silicon. An empirical electronic energy loss model for B, BF2, As, P and Si self implant over the wide energy range has been proposed for the ULSI device technology and development. Our model shows very good agreement with the SIMS data over the wide energy range. In the damage accumulation, we considered the self-annealing effects by introducing our proposed non-linear recomvination probability function of each point defect for the computational efficiency. For the damage profiles, we compared the published RBS/channeling data with our results of phosphorus implants. Our damage model shows very reasonable agreement with the experiments for phosphorus implants.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.125-126
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• 2005

Recently semiconductor devices are required more smaller scale and more powerful performance. For smaller scale of device, multilayer structure is proposed. And, for the higher performance, interconnection material is change to copper, because copper has high EM(Electro-migration)and low resistivity. Then copper CMP process is a great role in a multilayer formation of semiconductor. Copper process is different from aluminum process. ECP process is one of the copper processes. In this paper, we focused on the defects tendency by copper thickness which filled using ECP process. we observed hump high and dishing. Conclusively, hump hight reduced at copper thickness increased Also dishing reduced.