Title :
Ion implant data log analysis for process control and fault detection
Author :
Rendon ; Sing, D.C. ; Beard, Michael ; Hartig, M. ; Arnold, J.C.
Abstract :
Data mining techniques have been introduced to the semiconductor industry in recent years. In this paper, we report on progress in developing a network based implant data log (IDL) data analysis system which can be used to access data from multiple tools and multiple recipes. The system that is currently under development can be used to generate individual control charts from any of over 100 process variables for a user selectable process recipe or from all implants in the database. Multi-variable models are being developed to compare relationships among process variables. These models calculate predicted values of process variables, and the differences between the model and the actual variables are used as indicators of process drift, hardware malfunctions, recipe integrity, and in some cases mis-processing.
Keywords :
data mining; ion implantation; process control; semiconductor process modelling; data mining techniques; fault detection; hardware malfunctions; ion implant data log analysis; misprocessing detection; multiple recipe data; multiple tool data; network based IDL data analysis system; process control; process drift; process multi-variable models; recipe integrity; Control charts; Data analysis; Data mining; Databases; Electronics industry; Fault detection; Hardware; Implants; Predictive models; Process control;
Conference_Titel :
Ion Implantation Technology. 2002. Proceedings of the 14th International Conference on
Conference_Location :
Taos, New Mexico, USA
Print_ISBN :
0-7803-7155-0
DOI :
10.1109/IIT.2002.1258007
