AI-Assistant Thermal Hotspot Prediction and Chip Placement Optimization for Semiconductor Packages

Semiconductor packages are becoming more complex due to the increasing integration of multiple chips and chiplets within a single package. This can lead to higher heat generation and uneven temperature distribution, resulting in the formation of thermal hotspots. Excessive temperature can affect the performance and reliability of semiconductor devices. Conventional thermal analysis methods generally require repeated simulations to evaluate different chip placement arrangements, which can increase the time and computational effort. This project proposes an AI-based thermal hotspot prediction and chip placement optimization system for semiconductor packages. The proposed approach uses HotSpot thermal simulation to generate temperature data for different chip placement configurations. The generated dataset is processed using Python or MATLAB and used to train a Machine Learning model. The trained model predicts the maximum temperature and identifies possible thermal hotspot regions for different placement arrangements. Based on the predicted temperature, suitable chip placement configurations are selected and validated using HotSpot simulation. The proposed system aims to reduce the effort required for evaluating multiple placement configurations and support improved thermal-aware chip placement