Application of AI-enabled Simulation in Power Package Development

The design of power packages with both high efficiency and high-power density performance while maintaining the highest possible reliability is a challenge. During development for power package, design of experiment (DOE) by simulation can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are only part of overview of factor. Especially, as the package size continuously decrease for high-density designs, there are lots of challenge areas, like thin die pick-up process, material optimization to enhance process and reliability performance. Maching learning (ML) within the field of Artificial Intelligence (AI) simulation can be very effective tools to help address these challenges.In this paper, application of ML algorithms and enabled simulation for die pick-up process and reliability testing related to power device are discussed. For thin die pick-up process, Bayesian optimization was used to optimize the parameters to achieve the lowest die stress under a given condition. An automatic molding compound selection framework is proposed to generate the optimal material properties of epoxy molding compound (EMC) properties for solder joint reliability. Through these cases, it has been demonstrated that the AI-enabled simulation can be utilized to significantly improve parameter optimization and material selection for robust power device development.

[1]  Jingshen Wu,et al.  AI-enabled Automatic Molding Compound Selection for A Power Device with High Solder Joint Reliability , 2022, 2022 23rd International Conference on Electronic Packaging Technology (ICEPT).

[2]  Jingshen Wu,et al.  Machine Learning Enabled Optimization of Pick-up Process for Thin Die , 2021, 2021 22nd International Conference on Electronic Packaging Technology (ICEPT).

[3]  K. Chiang,et al.  An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging , 2021, Materials.

[4]  Xuejun Fan,et al.  Solder Joint Reliability Risk Estimation by AI-Assisted Simulation Framework with Genetic Algorithm to Optimize the Initial Parameters for AI Models , 2021, Materials.

[5]  Jiajie Fan,et al.  Deep machine learning of the spectral power distribution of the LED system with multiple degradation mechanisms , 2020, Journal of Mechanics.

[6]  J. Lau,et al.  Panel-Level Chip-Scale Package With Multiple Diced Wafers , 2020, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[7]  Cheng Chen,et al.  Warpage Prediction and Optimization for Embedded Silicon Fan-Out Wafer-Level Packaging Based on an Extended Theoretical Model , 2019, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[8]  Rennier Rodriguez,et al.  Pick and Place Process Optimization for Thin Semiconductor Packages , 2019, Journal of Engineering Research and Reports.

[9]  C. Ko,et al.  Warpage Measurements and Characterizations of Fan-Out Wafer-Level Packaging with Large Chips and Multiple Redistributed Layers , 2018, 2018 IEEE 68th Electronic Components and Technology Conference (ECTC).

[10]  Frank Hutter,et al.  Maximizing acquisition functions for Bayesian optimization , 2018, NeurIPS.

[11]  Yong Liu,et al.  Thin and large die assembly pick up process optimization by dynamic modeling , 2016, 2016 17th International Conference on Electronic Packaging Technology (ICEPT).

[12]  Tang Liang,et al.  Study on thin die pick-up process based on Taguchi method , 2015, 2015 16th International Conference on Electronic Packaging Technology (ICEPT).

[13]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[14]  Sheng-Jye Hwang,et al.  Static analysis of the die picking process , 2005, IEEE Transactions on Electronics Packaging Manufacturing.

[15]  H. Henning Winter,et al.  Determination of discrete relaxation and retardation time spectra from dynamic mechanical data , 1989 .

[16]  R. Landel,et al.  The Temperature Dependence of Relaxation Mechanisms in Amorphous Polymers and Other Glass-Forming Liquids , 1955 .