2024

Vol.31 No.3

Current Issue Archives
Search
Advanced Search
Editorial Office

Review

  • Journal of the Microelectronics and Packaging Society
  • Volume 31(3); 2024
  • Article

Review

Journal of the Microelectronics and Packaging Society 2024;31(3):24-37. Published online: Oct, 30, 2024

PDF Full Text XML

DOI : doi.org/10.6117/kmeps.2024.31.3.024

Cost-effective Machine Learning Method for Predicting Package Warpage during Mold Curing

  • Seong-Hwan Park1 , Tae-Hyun Kim2 , and Eun-Ho Lee1,2,3†
    1 School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro 2066, Suwon-si, Gyeonggi-do, 16419, Republic of Korea, 2 Department of Smart Fab. Technology, Sungkyunkwan University, Seobu-ro 2066, Suwon-si, Gyeonggi-do, 16419, Republic of Korea, 3 Department of Intelligent Robotics, Sungkyunkwan University, Seobu-ro 2066, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
Corresponding author E-mail: e.h.lee@skku.edu
Abstract

Due to the thin nature of semiconductor packages, even minor thermal loads can cause significant warpage, impacting product reliability through issues like delamination or cracking. The mold curing process, which encloses the package to protect the semiconductor chip, is particularly challenging to predict due to the complex thermal, chemical, and mechanical interactions. This study proposes a cost-effective machine learning model to predict warpage in the mold curing process. We developed methods to characterize the curing degree based on time and temperature and quantify the material's mechanical properties accordingly. A Finite Element Method (FEM) simulation model was created by integrating these properties into ABAQUS UMAT to predict warpage for various design factors. Additionally, a Warpage formula was developed to estimate local warpage based on the package's stacking structure. This formula combines bending theory with thermo-chemical-mechanical properties and was validated through FEM simulation results. The study presents a method to construct a machine learning model for warpage prediction using this formula and proposes a cost-effective approach for building a training dataset by analyzing input variables and design factors. This methodology achieves over 98% prediction accuracy and reduces simulation time by 96.5%.

Keywords Warpage, Curing process, FEM simulation, Cost-effective, Physics informed machine Learning

REFERENCES
  • C. Kim, H. Choi, M. Kim, T.-S. Kim, Packaging Substrate Bending Prediction due to Residual Stress, J. Microelectron. Packag. Soc., 20 (2013)
  • J. Zhao, Y. Luo, Z. Huang, R. Ma, , Effects of package design on top PoP package warpage, (2008)
  • M. Joshi, R. Pendse, V. Pandey, T. Lee, I. Yoon, J. Yun, Y. Kim, H. Lee, , Molded underfill (MUF) technology for flip chip packages in mobile applications, (2010)
  • W. K. Loh, R. Kulterman, T. Purdie, H. Fu, M. Tsuriya, , Recent trend of package warpage characteristic, (2015)
  • W. K. Loh, R. Kulterman, H. Fu, M. Tsuriya, , Recent trends of package warpage and measurement metrologies, (2016)
  • S.-H. Chae, J.-H. Zhao, D. R. Edwards, P. S. Ho, , Characterization of viscoelasticity of molding compounds in time domain, (2009)
  • D.-H. Lee, J.-H. Lim, E.-H. Lee, A Numerical Study on the Effect of Initial Shape on Inelastic Deformation of Solder Balls under Various Mechanical Loading Conditions, J. Microelectron. Packag. Soc., 30 (2023)
  • C.-C. Lee, C.-C. Lee, C.-P. Chang, Simulation methodology development of warpage estimation for epoxy molding compound under considerations of stress relaxation characteristics and curing conditions applied in semiconductor packaging, Materials Science in Semiconductor Processing, 145 (2022)
  • T.-C. Chiu, C.-L. Gung, H.-W. Huang, Y.-S. Lai, Effects of curing and chemical aging on warpage—Characterization and simulation, IEEE Transactions on Device and Materials Reliability, 11 (2011)
  • D. Yang, K. Jansen, L. Ernst, G. Zhang, W. Van Driel, H. J. L. Bressers, , Modeling of cure-induced warpage of plastic IC packages, (2004)
  • M.-Y. Lin, Y.-J. Zeng, S.-J. Hwang, M.-H. Wang, H.-P. Liu, C.-L. Fang, Warpage and residual stress analyses of post-mold cure process of IC packages, The International Journal of Advanced Manufacturing Technology, 124 (2023)
  • P.-Y. Lin, S. Lee, Warpage modeling of ultra-thin packages based on chemical shrinkage and cure-dependent viscoelasticity of molded underfill, IEEE Transactions on Device and Materials Reliability, 20 (2020)
  • Y.-J. Lin, S.-J. Hwang, H.-H. Lee, D.-Y. Huang, Modeling of viscoelastic behavior of an epoxy molding compound during and after curing, IEEE Transactions on Components, Packaging and Manufacturing Technology, 1 (2011)
  • C. Yang, C.-Y. Gu, M. Ju, J. Kim, D. Jang, J. Jang, J. Jang, J. Kim, T.-S. Kim, Time-Dependent Warpage Analysis for PCB Considering Viscoelastic Properties of Prepreg, J. Microelectron. Packag. Soc., 31 (2024)
  • S.-S. Yeh, P. Lin, K. Lee, M. Yew, C. Yang, J. Wang, C. Hsu, P. Lai, D. Tseng, S. Cheng, S.-P. Jeng, , Viscoelastic Modeling for Heterogeneous Fan-out Wafer Molding Process, (2020)
  • P.-Y. Lin, S. Lee, Modeling and characterization of curedependent viscoelasticity of molded underfill in ultrathin packages, IEEE Transactions on Components, Packaging and Manufacturing Technology, 10 (2020)
  • S.-S. Yeh, P.-Y. Lin, K.-C. Lee, J.-H. Wang, W.-Y. Lin, M.-C. Yew, P.-C. Lai, C.-K. Hsu, C.-C. Yang, S.-P. Jeng, , An integrated warpage prediction model based on chemical shrinkage and viscoelasticity for molded underfill, (2018)
  • J. Kim, J. Lee, Y. Han, Fault detection prediction using a deep belief network-based multi-classifier in the semiconductor manufacturing process, International Journal of Software Engineering and Knowledge Engineering, 29 (2019)
  • C.-F. Chien, W.-C. Wang, J.-C. Cheng, Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Expert Systems with Applications, 33 (2007)
  • K. Nakata, R. Orihara, Y. Mizuoka, K. Takagi, A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing, IEEE Transactions on Semiconductor Manufacturing, 30 (2017)
  • G. Köksal, I. Batmaz, M. C. Testik, A review of data mining applications for quality improvement in manufacturing industry, Expert Systems with Applications, 38 (2011)
  • S. Cheon, H. Lee, C. O. Kim, S. H. Lee, Convolutional neural network for wafer surface defect classification and the detection of unknown defect class, IEEE Transactions on Semiconductor Manufacturing, 32 (2019)
  • D.-Y. Liu, L.-M. Xu, X.-M. Lin, X. Wei, W.-J. Yu, Y. Wang, Z.-M. Wei, Machine learning for semiconductors, Chip, 1 (2022)
  • T.-H. Kim, J.-H. Park, K. W. Jung, J. Kim, E.-H. Lee, Application of convolutional neural network to predict anisotropic effective thermal conductivity of semiconductor package, IEEE Access, 10 (2022)
  • T. Y. Kang, T.-S. Kim, Signal-Based Fault Detection and Diagnosis on Electronic Packaging and Applications of Artificial Intelligence Techniques, J. Microelectron. Packag. Soc., 30 (2023)
  • G. E. Batista, R. C. Prati, M. C. Monard, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD explorations newsletter, 6 (2004)
  • H. Chen, , Novel machine learning approaches for modeling variations in semiconductor manufacturing, (2017)
  • S. Lynn, J. Ringwood, E. Ragnoli, S. McLoone, N. Mac-Gearailty, , Virtual metrology for plasma etch using tool variables, (2009)
  • T.-H. Lin, F.-T. Cheng, W.-M. Wu, C.-A. Kao, A.-J. Ye, F.-C. Chang, NN-based key-variable selection method for enhancing virtual metrology accuracy, IEEE Transactions on Semiconductor Manufacturing, 22 (2009)
  • J. R. Beattie, F. W. L. Esmonde-White, Exploration of principal component analysis: deriving principal component analysis visually using spectra, Applied Spectroscopy, 75 (2021)
  • D. Jiang, W. Lin, N. Raghavan, A novel framework for semiconductor manufacturing final test yield classification using machine learning techniques, IEEE Access, 8 (2020)
  • G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, L. Yang, Physics-informed machine learning, Nature Reviews Physics, 3 (2021)
  • T. Ahmed, P. Sharma, C. L. Karmaker, S. Nasir, Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm, Materialstoday: Proceedings, 50 (2022)
  • Z. Shu, B. S. Wang, K. N. Chiang, , Using Extra Trees Machine Learning Algorithm to Predict the Asymmetric Warpage Geometry of Panel Level Packaging, (2022)
  • S. W. Liu, S. K. Panigrahy, K. N. Chiang, , Prediction of fan-out panel level warpage using neural network model with edge detection enhancement, (2020)
  • S. K. Panigrahy, F. X. Che, Y. C. Ong, P. N. Nune, H. W. Ng, , Integration of Artificial Neural Network and Finite Element Simulation for Package Warpage Prediction, (2023)
  • E.-H. Lee, A model for irreversible deformation phenomena driven by hydrostatic stress, deviatoric stress and an externally applied field, International Journal of Engineering Science, 169 (2021)
  • M. R. Kamal, Thermoset characterization for moldability analysis, Polymer Engineering & Science, 14 (1974)
  • A. Armillotta, M. Bellotti, M. Cavallaro, Warpage of FDM parts: Experimental tests and analytic model, Robotics and Computer-Integrated Manufacturing, 50 (2018)
  • F. X. Che, D. Ho, M. Z. Ding, D. R. MinWoo, , Study on process induced wafer level warpage of fan-out wafer level packaging, (2016)
  • C. Chen, D. Yu, T. Wang, Z. Xiao, L. Wan, Warpage prediction and optimization for embedded silicon fan-out wafer-level packaging based on an extended theoretical model, IEEE Transactions on Components, Packaging and Manufacturing Technology, 9 (2019)
  • M. A. Eltaher, A. E. Alshorbagy, F. Mahmoud, Determination of neutral axis position and its effect on natural frequencies of functionally graded macro/nanobeams, Composite Structures, 99 (2013)
  • S. Timoshenko, Analysis of bi-metal thermostats, Journal of the Optical Society of America, 11 (1925)
  • M.-Y. Tsai, Y.-W. Wang, A theoretical solution for thermal warpage of flip-chip packages, IEEE Transactions on Components, Packaging and Manufacturing Technology, 10 (2020)
  • G. Yang, Z. Kuang, H. Lai, Y. Liu, R. Cui, J. Cao, Y. Zhang, C. Cui, A quantitative model to understand the effect of gravity on the warpage of fan-out panel-level packaging, IEEE Transactions on Components, Packaging and Manufacturing Technology, 11 (2021)