• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1360-1364
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• 2014

In this paper, RCS characteristics on defect pattern of crossed dipole slot FSS having a finite size have been analyzed. To analyze RCS, we applied the electric field integral equation analysis which applies BiCGSTab algorithm with iterative method and uses RWG basis function. To verify the validity of this paper, RCS of PEC sphere has been compared to the theoretical results and FSSs with defect patterns are fabricated and measured. As defect patterns in FSS, missing one column, missing some elements, and discontinuity in surfaces are simulated and compared with the measurement results. Resonant frequency shifts in pass band and changes in bandwidth are observed. From the results, precisely predicting and designing frequency characteristics over defect patterns are essential when applying FSS structures such as FSS radomes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Tribology and Lubricants
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• v.24 no.6
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• pp.332-337
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• 2008

This paper presents the surface defect analysis based on the experimental investigation of scribed glasses. The scribing process by a diamond wheel cutter is widely used as a reliable and inexpensive method for sizing of glass sheets. The wheel cutter generates a small median crack on the glass surface, which is then propagated through the glass thickness for complete separation. The surface contour patterns in which are formed during a scribing process are strongly related to wheel cutter parameters such as wheel tip surface finish, tip angle and wheel diameter, and cutting process parameters such as scribing pressure, speed and tooling technique. The scribed surface of a glass sheet provides normal Wallner lines, which represent regular median cracks and crack propagation in glass thickness, and abnormal surface roughness patterns. In this experimental study, normal and abnormal surface topographic patterns are classified based on the surface defect profiles of scribed glass sheets. A normal surface of a scribed glass sheet shows regular Wallner lines with deep median cracks. But some specimens of scribed glass sheets show that abnormal surface profiles of glass sheets in two pieces are represented by a chipping, irregular surface cracks in depth, edge cracks, and combined crack defects. These surface crack patterns are strongly related to easy breakage of the scribed glass imposed by external forces. Thus the scribed glass with abnormal crack patterns should be removed during a quality control process based on the surface defect classification method as demonstrated in this study.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.2
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• pp.130-138
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• 2006

We have investigated pattern scaling down of silicon stamps through the oxidation technique, During oxidizing the silicon stamps, silicon dioxide that has 300 nm and 500 nm thickness was grown, and critical deformations were not observed in the patterns. There was positive effect to reduce size of patterns because vertical and horizontal patterns have different orientation. We achieved pattern reduction rate of $26\%$. In addition, the formation of polymer patterns had been investigated with varied temperature and pressure conditions to improve the filling characteristics of polymers during nanoimprint lithography when pattern sizes were few micrometers. In these varied conditions, polymers had been affected by free space compensation and elastic stress relaxation for filling the cavities. Based on the results, defect control which is an important issue in the nanoimprint lithography were facilitated.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2330-2332
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• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.5
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• pp.199-204
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• 2002

In order to recognize the deterioration of insulation system by partial discharge (PD), the characteristics of PD patterns which are occurring at the interface between epoxy and rubber materials in extra high voltage cable joints, have been investigated. The artificial defects such as voids, metal particles, insulation fiber and water impregnated insulation fiber are planted between the interfaces. A high frequency partial discharge detection system was used for measuring PD signals. An analysis of the PD patterns is focused on the shape of PD pattern, phase, width and time-dependence for each artificial defect. The PD Patterns in each defect show the different behaviors and it is suggested that the precise discrimination of PD patterns could be used for the diagnosis of deterioration in the insulation systems.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1036-1039
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• 2009

We have developed an automated circuit defect repair method. We focused on the resist patterns on the circuit material layer of TFT substrates before the etching process. In this paper, we report on the repair method that utilizes the syringe system and the stability of the open defect repair process.