Machine Learning‐Based Solution for Thermomechanical Analysis of MMIC Packaging

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