• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.135-142
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• 2024

Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• 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.

• Advances in nano research
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• v.16 no.1
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• pp.91-109
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• 2024

Carbon nanotubes are drawing wide attention of research communities and several industries due to their versatile capabilities covering mechanical and other multi-physical properties. However, owing to extreme operating conditions of the synthesis process of these nanostructures, they are often imposed with certain inevitable structural deformities such as single vacancy and nanopore defects. These random irregularities limit the intended functionalities of carbon nanotubes severely. In this article, we investigate the mechanical behaviour of double-wall carbon nanotubes (DWCNT) under the influence of arbitrarily distributed single vacancy and nanopore defects in the outer wall, inner wall, and both the walls. Large-scale molecular simulations reveal that the nanopore defects have more detrimental effects on the mechanical behaviour of DWCNTs, while the defects in the inner wall of DWCNTs make the nanostructures more vulnerable to withstand high longitudinal deformation. From a different perspective, to exploit the mechanics of damage for achieving defect-induced shape modulation and region-wise deformation control, we have further explored the localized longitudinal and transverse spatial effects of DWCNT by designing the defects for their regional distribution. The comprehensive numerical results of the present study would lead to the characterization of the critical mechanical properties of DWCNTs under the presence of inevitable intrinsic defects along with the aspect of defect-induced spatial modulation of shapes for prospective applications in a range of nanoelectromechanical systems and devices.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.689-697
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• 2024

According to the South Korean Ministry of Land, Infrastructure, and Transport, instances of defect dispute resolutions, primarily between construction contractors and apartment occupants, have been occurring at an annual average of over 4,000 cases since 2014 to the present day. To address the persistent issue of disputes between contractors and occupants regarding construction defects, it is crucial to use customer sentiment analysis to improve customer rights and guide construction companies in their efforts. This study presents a methodology for effectively managing customer complaints and enhancing feedback analysis in the context of defect repair services. The study begins with collecting and preprocessing customer feedback data. Semantic network analysis is used to understand the causes of discomfort in customer feedback, revealing insights into the emotional sentiments expressed by customers and identifying causal relationships between emotions and themes. This research combines text mining, and semantic network analysis to analyze customer feedback for decision-making. By doing so, defect repair service providers can improve service quality, address customer concerns promptly, and understand the factors behind emotional responses in customer feedback. Through data-driven decision-making, these providers can enhance customer rights and identify areas for construction companies to improve service quality.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1413-1416
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• 2005

An infrared temperature sensor module developed for the detection of defects in a plate was modified to use in a cylinder. A set of optical fiber leads and a mechanism maintaining sensor-object distance constant were utilized for the modification of the IR sensor module. The detection performance was experimentally investigated, and the measured temperature was also compared with computed temperature distribution. The experimental outcome indicates that the detection of a simulated defect is readily available. The temperature distribution is better for defect detection than that with the previous device. In addition, the measured distribution is comparable to the calculated one using a heat conduction equation. The developed device of defect detection is suitable to be utilized in chemical processes where most of vessels and piping systems are in the shape of a cylinder.

• Journal of the Korean Ceramic Society
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• v.54 no.2
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• pp.75-85
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• 2017

Thermoelectric is a key technology for energy harvesting and solid-state cooling by direct thermal-to-electric energy conversion (or vice versa); however, the relatively low efficiency has limited thermoelectric systems to niche applications such as space power generation and small-scale or high-density cooling. To expand into larger scale power generation and cooling applications such as ATEG (automotive thermoelectric generators) and HVAC (heating, ventilation, and air conditioning), high-performance bulk thermoelectric materials and their low-cost processing are essential prerequisites. Recently, the performance of commercial thermoelectric materials including $Bi_2Te_3$-, PbTe-, skutterudite-, and half-Heusler-based compounds has been significantly improved through non-equilibrium processing technologies for defect engineering. This review summarizes material design approaches for the formation of multi-dimensional and multi-scale defect structures that can be used to manipulate both the electronic and thermal transport properties, and our recent progress in the synthesis of conventional thermoelectric materials with defect structures is described.

• Proceedings of the KIEE Conference
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• summer
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• pp.887-889
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• 2004

This paper presents an analysis of RPC probe signal in steam generator tube with defect using finite element method. Impedance signal is calculated according to the depth variation of defect in tube and change of frequency in same defect. As the depth of the defect and the operating frequency is increased, the magnitude of the signal is increased. From the result of this paper, we can obtain the information by the effect of defect and frequency.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1509-1511
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• 2015

Defect detection is one of the most challenging problems in industrial quality control. In this study we developed a vision-based defect detection system for wafer production. To achieve high-accuracy detection, Otsu method was improved so that it can handle both unimodal and bimodal distributions. After thresholding, detected defect regions in the wafer are classified and grouped into user-defined defect categories. The experimental result has proved the efficiency of our system.

• Smart Structures and Systems
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• v.25 no.2
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• pp.255-264
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• 2020

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.