Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

Abstract Thermomechanical analysis of monolithic microwave integrated circuit (MMIC) packaging is essential to guarantee the reliability of radio frequency/microwave applications. However, a method for fast and accurate analysis of MMIC packaging structures has not been developed. Here, a machine learning (ML)‐based solution for thermomechanical analysis of MMIC packaging is demonstrated. This ML‐based solution analyzes temperature and thermal stresses considering key design parameters, including material properties, geometric characteristics, and thermal boundary conditions. Finite element simulation with the Monte Carlo method is utilized to prepare a large dataset for supervised learning and validation of the ML solution, and a laser‐assisted thermal experiment is conducted to verify the accuracy of the simulation. After data preparation, regression tree ensemble and artificial neural network (ANN) learning models are investigated. The results show that the ANN model accurately predicts the outcomes with extremely low computing time by analyzing the high‐dimensional dataset. Finally, the developed ML solution is deployed as a web application format for facile approaches. It is believed that this study will provide a guideline for developing ML‐based solutions in chip packaging design technology.