Developing a selection scheme for dual virtual-metrology outputs

• Published in: 2008 IEEE International Conference on Automation Science and Engineering pp. 230 - 235
• Main Authors: Wei-Ming Wu, Fan-Tien Cheng, Deng-Lin Zeng, Tung-Ho Lin, Jyun-fang Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2008
• Subjects: Accuracy; Algorithm design and analysis; Artificial neural networks; Data models; Mathematical model; Metrology; multiple regression (MR); neural network (NN); Prediction algorithms; selection scheme (S-scheme); Virtual metrology (VM)
• ISBN: 9781424420223; 1424420229
• ISSN: 2161-8070
• DOI: 10.1109/COASE.2008.4626525

This paper proposes a selection scheme (S-scheme) between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS). Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift or shift occurs. To take advantage of the merits of both MR and NN algorithms, the S-scheme is proposed to enhance virtual-metrology (VM) conjecture accuracy. Two illustrative examples in the CVD process of fifth generation TFT-LCD are used to test and compare the conjecture accuracy among solo NN, solo MR, and S-scheme. One-hidden-layered back-propagation neural network (BPNN-I) is adopted for establishing the NN conjecture model. Test results show that the conjecture accuracy of S-scheme can achieve superior accuracy than solo NN and solo MR algorithms.