Data-driven automated prediction of the thermal property map of semiconductor packaging
Автор: MSC LAB, Sungkyunkwan University
Загружено: 2025-08-12
Просмотров: 134
References: J. H. Park, J. Kim, S. Jang, S. Mun, E. H. Lee (2025). “Image-based accelerated prediction of thermal properties of package substrates using combined deep-learning and an enhanced thermal network model”. IEEE ACCESS, 13, 107926 - 107935.
J. H. Park, H. Park, T. Kim, J. Kim, E. H. Lee. (2024). “Numerical analysis of thermal and mechanical characteristics with property maps in complex semiconductor package designs”. Applied Mathematical Modelling, 130, 140-159.
T. H. Kim, J. H. Park, K. W. Jung, J. C. Kim, E. H. Lee (2022). “Application of convolutional neural network to predict anisotropic effective thermal conductivity of semiconductor package”. IEEE Access, 10, pp. 51995-52007.
https://swb.skku.edu/MSC/Internationaljour...
Developed at the MSC Lab of Sungkyunkwan University, this technology is a platform that rapidly predicts the effective thermal properties of Cu wiring patterns in advanced semiconductor packaging based on image data.
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