• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.235-238
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• 2004

본 연구는 반도체소자 제조공정에 적용되는 CMP공정 중 LPP(Landing Plug Poly) Contact간의 소자 분리를 위해 진행되는 LPP CMP의 후 세정 과정에서 유발되는 방사형 Defect 제거에 관한 내용이다. 방사형 Defect은 LPP CMP 후에 노출되는 BPSG, Poly, Nitride Film과 연마재로 사용되는SiO2 입자, 후 세정과정에서 적용되는 SC-1, DHF, $NH_4OH$ Chemical과 Brush와의 상호작용에 의해 발생되며, Cleaning시의 산성 분위기 하에서 각 물질간의 pH와 Zeta Potential의 차이에서 기인한다. 이 Defect을 제거하기 위해 LPP CMP후에 Film 표면에 노출되는 각 물질의 표면 특성과 CMP 후 오염입자의 흡착과 재 흡착에 영향을 미치는 Electrostatic force와 Van der Waals force, PVA Brush에 의한 Mechanical force의 상호작용을 고려하여 최적 후 세정 조건을 제시 하였다.

• IE interfaces
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• v.21 no.2
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• pp.151-160
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• 2008

Defect size distribution is a probability density function for the defects that occur on wafers or glasses during semiconductor/LCD fabrication. It is one of the most important information to estimate manufacturing yield using well-known statistical estimation methods. The defects are detected by automatic optical inspection (AOI) facilities. However, the data that is provided from AOI is not accurate due to resolution of AOI and its defect detection mechanism. It causes distortion of defect size distribution and results in wrong estimation of the manufacturing yield. In this paper, I suggest a size conversion method and a maximum likelihood estimator to overcome the vague defect size information of AOI. The methods are verified by the Monte Carlo simulation that is constructed as similar as real situation.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1442-1447
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• 2003

Computer is a very powerful machine which is widely using for data processing, DB construction, peripheral device control, image processing etc. Consequently, many researches and developments have progressed for high performance processing unit, and other devices. Especially, the core units such as semiconductor parts are rapidly growing so that high-integration, high-performance, microminiat turization is possible. The packaging in the semiconductor industry is very important technique to de determine the performance of the system that the semiconductor is used. In this paper, the inspection of the inner defects such as delamination, void, crack, etc. in the semiconductor packages is studied. ESPI which is a non-contact, non-destructive, and full-field inspection method is used for the inner defect inspection and its results are compared with that of C-Scan method.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.109-114
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• 2021

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.6
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• pp.82-87
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• 2006

Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator's decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.419-430
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• 2022

Defects in solids play a vital role on thermoelectric properties through the direct impacts of electronic band structure and electron/phonon transports, which can improve the electronic and thermal properties of a given thermoelectric semiconductor. Defects in semiconductors can be divided into four different types depending on their geometric dimensions, and thus understanding the effects on thermoelectric properties of each type is of a vital importance. This paper reviews the recent advances in the various thermoelectric semiconductors through defect engineering focusing on the charge carrier and phonon behaviors. First, we clarify and summarize each type of defects in thermoelectric semiconductors. Then, we review the recent achievements in thermoelectric properties by applying defect engineering when introducing defects into semiconductor lattices. This paper ends with a brief discussion on the challenges and future directions of defect engineering in the thermoelectric field.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2004.05a
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• pp.59-62
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• 2004

The defect formation energy of boron nitride (BN) nanotubes is investigated using molecular-dynamics simulation. Although the defect with tetragon-octagon pairs (4-88-4) is favored in the flat cap of BN nanotubes, BN clusters, and the growth of BN nanotubes, the formation energy of the 4-88-4 defect is significantly higher than that of the pentagon-heptagon pairs (5-77-5) defect in BN nanotubes. The 5-77-5 defect reduces the effect of the structural distortion caused by the 4-88-4 defect, in spite of homoelemental bonds. The instability of the 4-88-4 defect generates the structural transformation into BNNTs with no defect at about 1500 K.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1149-1155
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• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.