TY - GEN
T1 - Manufacturability of multivariate applications in the semiconductor industry
AU - Jonathan, Chang Yung Cheng
AU - Cheng, Fan Tien
PY - 2006/1/1
Y1 - 2006/1/1
N2 - When advanced automation technologies are applied in the semiconductor industry, advanced equipment control has become increasingly feasible; hence it is now easier to improve overall manufacturing performance, including cycle time, productivity, and product quality. Currently, Fault Detection and Classification (FDC) is a newly deployed system that supports tool control in the semiconductor industry. Based on comprehensive and real time tool sensor data of an FDC infrastructure, numerous novel multivariate applications have been developed to further enhance tool and manufacturing productivity. However, when most multivariate applications were designed and pilot run in production environment, they all encountered a similar challenge - manufacturability. Because of insufficiency of manufacturing know-how and manufacturability, most multivariate applications cannot be widely deployed to an entire FAB for all types of tools and processes. This work investigates various multivariate applications and presents a novel methodology for improving manufacturability design of multivariate applications in the semiconductor industry.
AB - When advanced automation technologies are applied in the semiconductor industry, advanced equipment control has become increasingly feasible; hence it is now easier to improve overall manufacturing performance, including cycle time, productivity, and product quality. Currently, Fault Detection and Classification (FDC) is a newly deployed system that supports tool control in the semiconductor industry. Based on comprehensive and real time tool sensor data of an FDC infrastructure, numerous novel multivariate applications have been developed to further enhance tool and manufacturing productivity. However, when most multivariate applications were designed and pilot run in production environment, they all encountered a similar challenge - manufacturability. Because of insufficiency of manufacturing know-how and manufacturability, most multivariate applications cannot be widely deployed to an entire FAB for all types of tools and processes. This work investigates various multivariate applications and presents a novel methodology for improving manufacturability design of multivariate applications in the semiconductor industry.
UR - http://www.scopus.com/inward/record.url?scp=45149090006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=45149090006&partnerID=8YFLogxK
U2 - 10.1109/COASE.2006.326885
DO - 10.1109/COASE.2006.326885
M3 - Conference contribution
SN - 1424403103
SN - 9781424403103
T3 - 2006 IEEE International Conference on Automation Science and Engineering, CASE
SP - 230
EP - 235
BT - 2006 IEEE International Conference on Automation Science and Engineering, CASE
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2006 IEEE International Conference on Automation Science and Engineering, CASE
Y2 - 8 October 2006 through 10 October 2006
ER -