• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2004.05a
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• pp.129-131
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• 2004

극자외선 리소그래피(EUV lithography) 기술은 50nm 이하의 선폭을 가지는 차세대 소자 제작에 있어서 선도적인 기술 중 하나이다. EUVL 에서 필수적인 요소중의 하나가 mirror 로 사용되는 blank mask 이다. Blank mask 에 있어서 가장 중요한 요소는 반사도이다. 이 blank mask 는 Si substrate 위에 반사를 위한 Mo/Si pair 가 40pair 이상 적층되어있다. Blank mask 는 매우 청결해야한다. 만약 결함이 있다면 blank mask 에는 치명적이다 결함은 blank mask 에 있어서 반사도를 떨어뜨리는 주 요소이기 때문이다. 그 결함에는 amplitude defect 과 phase defect 이 있다. FIB 에서는 amplitude defect 을 수정하는 것이 가능하다. 우리는 FIB 를 이용하여 mage mode, spot mode, bar rotation mode 를 사용하여 amplitude defect을 수정하였다. 그리고, 그 결과 효과적으로 amplitude defect을 수정하였다.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.26-30
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• 2020

In this paper, we studied the problem if the experiment number occurring in order to identify defect in scaffold. We need to change each of the 5 print factor to predict defect when printing disk type scaffold using FDM 3d printer. So then the number of scaffold print will be more than 100,000 times. This experiment number is difficult to perform in the field. In order to solve this problem, we have produced a prediction model based on machine learning multiple linear regression using print conditions and defect scaffold data for print conditions. The prediction model produced was verified through experiments. The verification confirmed that the error was less than 0.5 %. We have confirmed that satisfied within the target margin of error 5 %.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.113-117
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• 2021

A composite active layer was designed based on graphene quantum dots, which is a low-dimensional structure, and a heterogeneous active layer of graphene quantum dots was applied to the interfacial defect structure to overcome the limitations. Increasing to 1.5~3.5 wt % PVP GQD, Vf changed from 2.16 ~ 2.72 V. When negative deflection is applied to the lower electrode, electrons travel through the HfOx/ITO interface. The Al + ions are reduced and the device dominates at low resistance. In addition, as the PVP GQD concentration increased, the depth of the interfacial defect decreased, and the repetition of appropriate electrical properties was confirmed through Al and HfOx/ITO. The low interfacial defects help electrophoresis of Al+ ions to the PVP GQD layer and the HfOx thin film. A local electric field increase occurred, resulting in the breakage of the conductive filament in the defect.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.81-86
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• 2023

Recently, there has been an increased demand for light-emitting diode (LED) due to the growing emphasis on environmental protection. However, the use of GaN-based sapphire in LED manufacturing leads to the generation of defects, such as dislocations caused by lattice mismatch, which ultimately reduces the luminous efficiency of LEDs. Moreover, most inspections for LED semiconductors focus on evaluating the luminous efficiency after packaging. To address these challenges, this paper aims to detect defects at the wafer stage, which could potentially improve the manufacturing process and reduce costs. To achieve this, image processing and deep learning-based defect detection techniques for Sapphire Epi-Wafer used in Green LED manufacturing were developed and compared. Through performance evaluation of each algorithm, it was found that the deep learning approach outperformed the image processing approach in terms of detection accuracy and efficiency.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.1-6
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• 2020

In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.55-61
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• 2021

In this paper, we proposed and implemented defect detection methods of polarized film with half-wave phase retardation plates. We investigated the principles of phase retardation compensation and optical principle of half-wave phase retardation plates. We analyzed of samples of polarized film with half-wave phase retardation plates. The optical defect detection methods are proposed and the performance is validated with experiments.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1510-1512
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• 2000

Due to the shrinking of the chip size and increasing of the complexity in the modern electronic devices. the defect of wafer are so important to decide the yield in the device process. The engineers has studied the wafer defects and the characteristics. They published lots of the experimental methods. I did an experiment the gettering effect of the defects due to the high temperature and the long time diffusion. Actually, As the thickness of the wafer backside polysilicon is thicker and the diffusion time is faster. the defects on the wafer are decreased. The polysilicon gram boundaries of the wafer backside played an important part as the defect gettering site.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.88-89
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• 2009

In this paper, we investigated about wet cleaning effect as deep trench formation methods for Power chip devices. Deep trench structure was classified by two methods, PSU (Poly Stick Up) and Non-PSU structure. In this paper, we could remove residue defect during wet. cleaning after deep trench etch process for non-PSU structure device as to change wet cleaning process condition. V-SEM result showed void image at the trench bottom site due to residue defect and residue component was oxide by EDS analysis. In order to find the reason of happening residue defect, we experimented about various process conditions. So, defect source was that oxide film was re-deposited at trench bottom by changed to hydrophobic property at substrate during hard mask removal process. Therefore, in order to removal residue defect, we added in-situ SCI during hard mask removal process, and defect was removed perfectly. And WLR (Wafer Level Reliability) test result was no difference between normal and optimized process condition.