BBAuthors: Hung, Shao-Yen; Advisor: -; Participants: Lee, Chia-Yen; Lin, Yung-Lun (2020)
The transformation of wafers into chips is a complex manufacturing process involving literally thousands of equipment parameters. Delamination, a leading cause of defective products, can occur between die and epoxy molding compound (EMC), epoxy and substrate, lead frame and EMC, etc. Troubleshooting is generally on a case-by-case basis and is both time-consuming and labor intensive. We propose a three-phase data science framework for process prognosis and prediction. The first phase is for data preprocessing. The second phase uses LASSO regression and stepwise regression to identify the key variables affecting delamination. The third phase develops backpropagation neural network (BPNN), support vector regression (SVR), partial least squares (PLS), and gradient boosting machine (GBM)... 
