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    스미스 차트를 이용한 구리 인터커텍트의 비파괴적 부식도 평가 = Nondestructive Quantification of Corrosion in Cu Interconnects Using Smith Charts

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    https://www.riss.kr/link?id=A109148348

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corrosion in copper interconnects using the Smith chart, a tool that integrates the magnitude and phase of complex impedance for visualization. For the experiment, specimens simulating copper transmission lines were subjected to temperature and humidity cycles according to the MIL-STD-810G standard to induce corrosion. The corrosion level of the specimen was quantitatively assessed and labeled based on color changes in the R channel. S-parameters and Smith charts with progressing corrosion stages showed unique patterns corresponding to five levels of corrosion, confirming the effectiveness of the Smith chart as a tool for corrosion assessment. Furthermore, by employing data augmentation, 4,444 Smith charts representing various corrosion levels were obtained, and artificial intelligence models were trained to output the corrosion stages of copper interconnects based on the input Smith charts. Among image classification-specialized CNN and Transformer models, the ConvNeXt model achieved the highest diagnostic performance with an accuracy of 89.4%.
    When diagnosing the corrosion using the Smith chart, it is possible to perform a non-destructive evaluation using electronic signals. Additionally, by integrating and visualizing signal magnitude and phase information, it is expected to perform an intuitive and noise-robust diagnosis.
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    Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corr...

    Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corrosion in copper interconnects using the Smith chart, a tool that integrates the magnitude and phase of complex impedance for visualization. For the experiment, specimens simulating copper transmission lines were subjected to temperature and humidity cycles according to the MIL-STD-810G standard to induce corrosion. The corrosion level of the specimen was quantitatively assessed and labeled based on color changes in the R channel. S-parameters and Smith charts with progressing corrosion stages showed unique patterns corresponding to five levels of corrosion, confirming the effectiveness of the Smith chart as a tool for corrosion assessment. Furthermore, by employing data augmentation, 4,444 Smith charts representing various corrosion levels were obtained, and artificial intelligence models were trained to output the corrosion stages of copper interconnects based on the input Smith charts. Among image classification-specialized CNN and Transformer models, the ConvNeXt model achieved the highest diagnostic performance with an accuracy of 89.4%.
    When diagnosing the corrosion using the Smith chart, it is possible to perform a non-destructive evaluation using electronic signals. Additionally, by integrating and visualizing signal magnitude and phase information, it is expected to perform an intuitive and noise-robust diagnosis.

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    참고문헌 (Reference)

    1 M. H. Azarian, "Use of the skin effect for detection of interconnect degradation" 1-5, 2009

    2 서동환 ; 강태엽 ; 유진호 ; 민준기 ; 박창근, "The defect detection circuit of an electronic circuit through impedance change detection that induces a change in S-parameter" 25 (25): 689-696, 2021

    3 G. -L. Farid, "Segmentation of images by color features : A survey" 292 : 1-27, 2018

    4 B. Li, "Reliability challenges for copper interconnects" 44 (44): 365-380, 2004

    5 T. Y. Kang, "Reliability Assessment on Electrical Interconnects by Machine Learning With Magnitude and Phase Data of RF signals" 167-, 2023

    6 B. Choe, "Reliability Assessment of Solder Joints Using Electrical Model on Cracking and S-Parameter Pattern Analysis(in Kor.)" 11 (11): 990-998, 2021

    7 F. Caspers, "RF engineering basic concepts : the Smith chart"

    8 T. Y. Kang, "Quantification of Performance Variation and Crack Evolution of Bond-Wire Interconnects Under Harsh Temperature Environments by SParameter Analysis" 11 (11): 990-998, 2021

    9 C. Kosemen, "Multi-label classification of line chart images using convolutional neural networks" 2 (2): 2020

    10 "MIL-STD-810G: Environmental Engineering Considerations and Laboratory Tests"

    1 M. H. Azarian, "Use of the skin effect for detection of interconnect degradation" 1-5, 2009

    2 서동환 ; 강태엽 ; 유진호 ; 민준기 ; 박창근, "The defect detection circuit of an electronic circuit through impedance change detection that induces a change in S-parameter" 25 (25): 689-696, 2021

    3 G. -L. Farid, "Segmentation of images by color features : A survey" 292 : 1-27, 2018

    4 B. Li, "Reliability challenges for copper interconnects" 44 (44): 365-380, 2004

    5 T. Y. Kang, "Reliability Assessment on Electrical Interconnects by Machine Learning With Magnitude and Phase Data of RF signals" 167-, 2023

    6 B. Choe, "Reliability Assessment of Solder Joints Using Electrical Model on Cracking and S-Parameter Pattern Analysis(in Kor.)" 11 (11): 990-998, 2021

    7 F. Caspers, "RF engineering basic concepts : the Smith chart"

    8 T. Y. Kang, "Quantification of Performance Variation and Crack Evolution of Bond-Wire Interconnects Under Harsh Temperature Environments by SParameter Analysis" 11 (11): 990-998, 2021

    9 C. Kosemen, "Multi-label classification of line chart images using convolutional neural networks" 2 (2): 2020

    10 "MIL-STD-810G: Environmental Engineering Considerations and Laboratory Tests"

    11 J. Koo, "Interconnect Reliability Degradation Monitoring Circuitry" 16-17, 2010

    12 S. H. S. Basha, "Impact of fully connected layers on performance of convolutional neural networks for image classification" 378 : 112-119, 2020

    13 D. Kwon, "Early Detection of Interconnect Failure by Continuous Monitoring of RF Impedance" 9 (9): 296-304, 2009

    14 K. W. Liao, "Detection of rust defects on steel bridge coatings via digital image recognition" 71 (71): 294-306, 2016

    15 M. Khayatazad, "Detection of corrosion on steel structures using automated image processing" 3 : 100022-, 2020

    16 V. Bondada, "Detection and quantitative assessment of corrosion on pipelines through image analysis" 133 : 804-811, 2018

    17 D. Roller, "Detecting and measuring corrosion : using electrical resistance techniques" 8 (8): 71-76, 1961

    18 N. Kim, "Detecting Non-Destructive Corrosion of Copper Interconnects in Electronic Packages Using Smith Charts" 142-, 2024

    19 W. Rawat, "Deep convolutional neural networks for image classification : A comprehensive review" 29 (29): 2352-2449, 2017

    20 F. J. P. Montalbo, "Classification of stenography using convolutional neural networks and canny edge detection algorithm" 305-310, 2019

    21 T. Y. Kang, "Cause and Severity Evaluation of Defects in Cu interconnects by Machine Learning of S-parameter patterns" 186-, 2020

    22 A. Dosovitskiy, "An image is worth 16x16 words : Transformers for image recognition at scale"

    23 Z. Liu, "A convnet for the 2020s" 11976-11986, 2020

    24 J. Canny, "A computational approach to edge detection" PAMI-8 (PAMI-8): 679-698, 1986

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