TY - GEN
T1 - Method for evaluating reliance level of a virtual metrology system
AU - Cheng, Fan Tien
AU - Chen, Yen Tung
AU - Su, Yu Chuan
AU - Zeng, Deng Lin
PY - 2007
Y1 - 2007
N2 - A method for evaluating reliance level of a virtual metrology system (VMS) is proposed. This method calculates a reliance index (RI) value between 0 and 1 by analyzing the process data of production equipment to decide if the virtual metrology result is reliable. A RI threshold is also defined in this method. If a RI value is higher than the threshold, the conjecture result is reliant; otherwise, the conjecture result needs to be further examined. In addition to the RI, the method also proposes process data similarity indexes (SIs). The SIs are defined to evaluate the degree of similarity between the input set of process data and those historical sets of process data used to establish the conjecture model. Two kinds of SIs are included in the method: global similarity index (GSI) and individual similarity index (ISI). Both the GSI and ISI are applied to assist the RI in gauging the reliance level and locating the key parameter(s) that cause major deviation, hence the VMS manufacturability problem is resolved. An illustrative example with 300-mm semiconductor foundry production equipment in Taiwan is demonstrated in this work. The real experimental results show that this method is applicable to the VMS of (such as semiconductor and TFT-LCD) production equipment.
AB - A method for evaluating reliance level of a virtual metrology system (VMS) is proposed. This method calculates a reliance index (RI) value between 0 and 1 by analyzing the process data of production equipment to decide if the virtual metrology result is reliable. A RI threshold is also defined in this method. If a RI value is higher than the threshold, the conjecture result is reliant; otherwise, the conjecture result needs to be further examined. In addition to the RI, the method also proposes process data similarity indexes (SIs). The SIs are defined to evaluate the degree of similarity between the input set of process data and those historical sets of process data used to establish the conjecture model. Two kinds of SIs are included in the method: global similarity index (GSI) and individual similarity index (ISI). Both the GSI and ISI are applied to assist the RI in gauging the reliance level and locating the key parameter(s) that cause major deviation, hence the VMS manufacturability problem is resolved. An illustrative example with 300-mm semiconductor foundry production equipment in Taiwan is demonstrated in this work. The real experimental results show that this method is applicable to the VMS of (such as semiconductor and TFT-LCD) production equipment.
UR - http://www.scopus.com/inward/record.url?scp=36348945773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348945773&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2007.363551
DO - 10.1109/ROBOT.2007.363551
M3 - Conference contribution
AN - SCOPUS:36348945773
SN - 1424406021
SN - 9781424406029
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1590
EP - 1596
BT - 2007 IEEE International Conference on Robotics and Automation, ICRA'07
T2 - 2007 IEEE International Conference on Robotics and Automation, ICRA'07
Y2 - 10 April 2007 through 14 April 2007
ER -