2024

Vol.31 No.3

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  • Journal of the Microelectronics and Packaging Society
  • Volume 31(2); 2024
  • Article

Review

Journal of the Microelectronics and Packaging Society 2024;31(2):28-35. Published online: Jul, 25, 2024

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DOI : doi.org/10.6117/kmeps.2024.31.2.028

Nondestructive Quantification of Corrosion in Cu Interconnects Using Smith Charts

  • Minkyu Kang* , Namgyeong Kim* , Hyunwoo Nam, and Tae Yeob Kang†
    Department of Mechanical Engineering, The University of Suwon, Hwaseong 18323, Korea
Corresponding author E-mail: tykang@suwon.ac.kr
Abstract

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.

Keywords Artificial intelligence, Cu interconnects, Corrosion, Non-destructive evaluation, Smith chart

REFERENCES
  • B. Li, T. D. Sullivan, T. C. Lee, and D. Badami, “Reliability challenges for copper interconnects”, Microelectronics Rel., 44(3), 365-380(2004).
  • B. Choe, and Tae Yeob Kang, “Reliability Assessment of Solder Joints Using Electrical Model on Cracking and S-Parameter Pattern Analysis(in Kor.)”, IEEE Transactions on Components, Packaging and Manufacturing Technology, 11(6), 990-998 (2021).
  • J. Koo, and C. Kim, “Interconnect Reliability Degradation Monitoring Circuitry”, Proceedings of the Korean Institute of Electrical and Electronic Engineers Conference, 16-17 (2010).
  • T. Y. Kang, “Reliability Assessment on Electrical Interconnects by Machine Learning With Magnitude and Phase Data of RF signals”, Korean Society of Mechanical EngineersReliability Division 2023 Spring Conference, 167 (2023).
  • D. Seo, T. Kang, J. Yoo, J. Min, and C. Park, “The defect detection circuit of an electronic circuit through impedance change detection that induces a change in S-parameter”, J. Korean Inst. Electr. Electron. Eng., 25(4), 689-696 (2021).
  • T. Y. Kang, D. Seo, and J. Min, “Cause and Severity Evaluation of Defects in Cu interconnects by Machine Learning of S-parameter patterns”, Korean Society of Mechanical Engineers-Reliability Division 2020 Spring Conference, 186 (2020).
  • N. Kim, M. Kang, H. Nam, and T. Y. Kang, “Detecting NonDestructive Corrosion of Copper Interconnects in Electronic Packages Using Smith Charts”, Proceedings of The Korean Society of Mechanical Engineers Conference, Korean Society of Mechanical Engineers-Reliability Division 2024 Spring Conference,142 (2024).
  • T. Y. Kang, D. Seo, J. Min, and T.-S. Kim, “Quantification of Performance Variation and Crack Evolution of Bond-Wire Interconnects Under Harsh Temperature Environments by SParameter Analysis”, IEEE Transactions on Components, Packaging and Manufacturing Technology, 11(6), 990-998 (2021).
  • D. Roller, and W. R. Scott, “Detecting and measuring corrosion: using electrical resistance techniques”, Anti-Corrosion Methods and Materials, 8(3), 71-76(1961).
  • M. H. Azarian, M. H., D. Kwon, and M. Pecht, “Use of the skin effect for detection of interconnect degradation”, IMAPS 42nd Int. Symposium on Microelectronics, 1-5 (2009).
  • F. Caspers, “RF engineering basic concepts: the Smith chart”, arXiv:1201.4068 (2012).
  • “MIL-STD-810G: Environmental Engineering Considerations and Laboratory Tests”, U.S. Department of Defense, (2008).
  • G.-L. Farid, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey”, Neurocomputing, 292, 1-27 (2018).
  • K. W. Liao and Y. T. Lee, “Detection of rust defects on steel bridge coatings via digital image recognition”, Automation in Construction, 71(2), 294-306 (2016).
  • V. Bondada, D. K. Pratihar, and C. S. Kumar, “Detection and quantitative assessment of corrosion on pipelines through image analysis”, Procedia Computer Science, 133, 804-811 (2018).
  • M. Khayatazad, L. D. Pue, and W. D. Waele, “Detection of corrosion on steel structures using automated image processing”, Developments in the Built Environment, 3, 100022 (2020).
  • S. H. S. Basha, S. R. Dubey, V. Pulabaigari, and S. Mukherjee, “Impact of fully connected layers on performance of convolutional neural networks for image classification”, Neurocomputing, 378, 112-119 (2020).
  • W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review”, Neural computation, 29(9), 2352-2449 (2017).
  • J. Canny, “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698(1986).
  • C. Kosemen and D. Birant, “Multi-label classification of line chart images using convolutional neural networks”, SN Appl. Sci., 2(1250), (2020).
  • F. J. P. Montalbo and D. P. Y. Barfeh, “Classification of stenography using convolutional neural networks and canny edge detection algorithm”, 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 305-310 (2019).
  • Z. Liu, H. Mao, C.Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s”, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11976-11986 (2022).
  • A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale”, arXiv:2010.11929 (2020).
  • D. Kwon, M. H. Azarian, and M. Pecht, “Early Detection of Interconnect Failure by Continuous Monitoring of RF Impedance”, IEEE Transactions on Device and Materials Reliability, 9(2), 296-304 (2009).