2024

Vol.31 No.3

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

Review

Journal of the Microelectronics and Packaging Society 2024;31(3):50-57. Published online: Oct, 30, 2024

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DOI : doi.org/10.6117/kmeps.2024.31.3.050

Time Reduction for Package Warpage Optimization based on Deep Neural Network and Bayesian Optimization

  • Jungeon Lee and Daeil Kwon†
    Departement of Industrial Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si, Gyeonggi-do, 16419, Korea
Corresponding author E-mail: dikwon@skku.edu
Abstract

Recently, applying a machine learning to surrogate modeling for rapid optimization of complex designs have been widely researched. Once trained, the machine learning surrogate model can predict similar outputs to Finite Element Analysis (FEA) simulations but require significantly less computing resources. In addition, combined with optimization methodologies, it can identify optimal design variable with less time requirement compared to iterative simulation. This study proposes a Deep Neural Network (DNN) model with Bayesian Optimization (BO) approach for efficiently searching the optimal design variables to minimize the warpage of electronic package. The DNN model was trained by using design variable-warpage dataset from FEA simulation, and the Bayesian optimization was applied to find the optimal design variables which minimizing the warpage. The suggested DNN + BO model shows over 99% consistency compared to actual simulation results, while only require 15 second to identify optimal design variable, which reducing the optimization time by more than 57% compared to FEA simulation.

Keywords Deep Neural Network, Bayesian Optimization, Electronics package, Finite Element Analysis

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