Hierarchical Attention-Based Machine Learning Model for Radiation Prediction of WB-BGA Package

Rapid increase in operating frequency of integrated chips and intricacy of electronic packages outpaces the ability of conventional methods in coping with the growing complexity of electromagnetic interference (EMI) issues. To address it, several machine learning (ML) methods--deep neural network (DNN), convolutional neural network, support vector regression, K-nearest neighbor, and linear regression are constructed to acquire the best ML model to accurately and rapidly predict the maximum 3-m radiated electric field of a wire-bond ball grid array package. The key hyperparameters of different ML models are tuned respectively to attain the least prediction error for each model. Among the optimized ML models, the prediction accuracy of the DNN model is the highest. In this article, a hierarchical attention-based DNN model is proposed and discussed in depth to reduce the number of training datasets, and identify the structural parameters with large contributions to radiation prediction. These structural parameters with contributions can guide the packaging design. The DNN model with attention-weight input requires fewer training datasets than the original DNN model. Furthermore, the experimental measurement for EMI radiation of a test package board is implemented, and the far-field results show the effectiveness and feasibility of the DNN model.