Item Infomation
Title: | Data Science for Delamination Prognosis and Online Batch Learning in Semiconductor Assembly Process |
Authors: | Hung, Shao-Yen |
Participants: | Lee, Chia-Yen Lin, Yung-Lun |
Issue Date: | 2020 |
Publisher: | IEEE Xplore |
Series/Report no.: | IEEE Transactions on Components, Packaging and Manufacturing Technology ( Volume: 10, Issue: 2, Feb. 2020) |
Abstract: | 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) to predict the ratio of the delamination area in a die. We also investigate the imbalance between a false positive rate and false negative rate after the quality classification with BPN and GBM models to improve the tradeoff between the two types of risks. We conducted an empirical study of a semiconductor manufacturer, and the results show that the proposed framework provides an effective delamination prediction supporting the troubleshooting. In addition, for online prediction, it is necessary to determine the batch size for the timing of retraining the model, we suggest the cost-oriented method to solve the issue |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/10469 |
Source: | https://doi.org/10.1109/TCPMT.2019.2956485 |
Appears in Collections: | Tài liệu hỗ trợ nghiên cứu khoa học |
ABSTRACTS VIEWS
43
VIEWS & DOWNLOAD
12
Files in This Item:
Bạn đọc là cán bộ, giáo viên, sinh viên của Trường Đại học Thuỷ Lợi cần đăng nhập để Xem trực tuyến/Tải về
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.