TY - JOUR
T1 - Accuracy and real-time considerations for implementing various virtual metrology algorithms
AU - Su, Yu Chuan
AU - Lin, Tung Ho
AU - Cheng, Fan Tien
AU - Wu, Wei Ming
N1 - Funding Information:
Manuscript received July 23, 2007; revised April 25, 2008. Published August 6, 2008 (projected). This work was supported in part by the National Science Council of the Republic of China under Contract NSC95-2622-E-006-002 and in part by the Landmark Project of National Cheng Kung University, Taiwan, R.O.C. This paper was presented in part at the 2007 IEEE International Conference on Automation Science and Engineering, Scottsdale, AZ, September, 2007.
PY - 2008/8
Y1 - 2008/8
N2 - In the semiconductor industry, run-to-run (R2R) control is an important technique to improve process capability and further enhance the production yield. As the dimension of electronic devices shrink increasingly, wafer-to-wafer (W2W) advanced process control (APC) becomes essential for the critical stages of production processes. W2W APC requires the metrology values of each wafer; however, it will be highly time and cost consuming to obtain actual metrology values from each wafer by physical measurement. Recently, an efficient and cost-effective approach denoted "virtual metrology (VM)" was proposed to substitute the actual metrology. To implement VM in W2W APC, both conjecture-accuracy and real-time requirements need to be considered. In this paper, various VM algorithms, including back-propagation neural networks (BPNN), simple recurrent neural networks (SRNN), and multiple regression (MR), are evaluated to see whether they can meet the accuracy and real-time requirements of W2W APC or not. The fifth generation TFT-LCD chemical-vapor deposition process is used to test and verify the requirements. Test results show that both one-hidden-layered BPNN and SRNN VM algorithms achieve acceptable conjecture accuracy and meet the real-time requirements of semiconductor and TFT-LCD W2W APC applications.
AB - In the semiconductor industry, run-to-run (R2R) control is an important technique to improve process capability and further enhance the production yield. As the dimension of electronic devices shrink increasingly, wafer-to-wafer (W2W) advanced process control (APC) becomes essential for the critical stages of production processes. W2W APC requires the metrology values of each wafer; however, it will be highly time and cost consuming to obtain actual metrology values from each wafer by physical measurement. Recently, an efficient and cost-effective approach denoted "virtual metrology (VM)" was proposed to substitute the actual metrology. To implement VM in W2W APC, both conjecture-accuracy and real-time requirements need to be considered. In this paper, various VM algorithms, including back-propagation neural networks (BPNN), simple recurrent neural networks (SRNN), and multiple regression (MR), are evaluated to see whether they can meet the accuracy and real-time requirements of W2W APC or not. The fifth generation TFT-LCD chemical-vapor deposition process is used to test and verify the requirements. Test results show that both one-hidden-layered BPNN and SRNN VM algorithms achieve acceptable conjecture accuracy and meet the real-time requirements of semiconductor and TFT-LCD W2W APC applications.
UR - http://www.scopus.com/inward/record.url?scp=49249085507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49249085507&partnerID=8YFLogxK
U2 - 10.1109/TSM.2008.2001219
DO - 10.1109/TSM.2008.2001219
M3 - Article
AN - SCOPUS:49249085507
SN - 0894-6507
VL - 21
SP - 426
EP - 434
JO - IEEE Transactions on Semiconductor Manufacturing
JF - IEEE Transactions on Semiconductor Manufacturing
IS - 3
M1 - 4589025
ER -