Predicting Thermal Resistance of Packaging Design by Machine Learning Models

Area of Science:

• Semiconductor packaging thermal management
• Computational modeling and simulation
• Machine learning applications in electronics

Background:

• Effective thermal management is crucial for integrated circuit (IC) package performance and reliability.
• High operating temperatures can lead to degraded performance and component failure.
• Accurate prediction of thermal resistance is essential for robust electronic component design.

Purpose of the Study:

• To evaluate machine learning models for predicting thermal resistance in semiconductor packages.
• To compare the forecasting accuracy of five distinct machine learning algorithms.
• To identify the most effective model for thermal resistance prediction in QFN and TFBGA packages.

Main Methods:

• Utilized finite element analysis (FEA) data for training machine learning models.
• Applied five regression models: Light Gradient Boosting Machine (LGBM), Random Forest (RF), XGBoost (XGB), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLP).
• Predicted key thermal resistance parameters (θJA, θJB, θJC, ΨJT, ΨJB) for QFN and TFBGA packages.

Main Results:

• The XGBoost model exhibited the highest forecasting accuracy across most tested cases.
• The predictive performance of the XGBoost model was found to be highly satisfactory.
• FEA-derived data proved effective for training machine learning models for thermal resistance prediction.

Conclusions:

• The XGBoost model is a promising and reliable tool for predicting thermal resistance in semiconductor packaging design.
• Machine learning techniques can significantly enhance the efficiency and reliability of IC packaging development.
• Accurate thermal resistance prediction facilitates improved performance and longevity of electronic components.