DocumentCode :
1101879
Title :
IC manufacturing diagnosis based on statistical analysis techniques
Author :
Kibarian, John K. ; Strojwas, Andrzej J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume :
15
Issue :
3
fYear :
1992
fDate :
6/1/1992 12:00:00 AM
Firstpage :
317
Lastpage :
321
Abstract :
During the production of integrated circuits, variations in the production environment can cause significant drops in yield. Since large amounts of data may have to be processed to diagnose the process conditions, the application of computer tools could greatly aid the engineer responsible for this task. In this paper the authors present a methodology for diagnosis, describe the algorithms, and illustrate applications with results from industrial data. More specifically, this paper presents the algorithms for the analysis of intrawafer variability. Measurements are made on many individual devices or circuits across an entire wafer. This information is used as the input to the diagnosis system. The system uses process, device, and circuit simulators to model the fabrication process. The results of the data analysis are lists of faults that may have caused the variability of measured performances. These faults are represented in terms of the inputs to the process simulator
Keywords :
circuit analysis computing; integrated circuit manufacture; process control; statistical analysis; IC manufacturing diagnosis; circuit simulators; device simulators; intrawafer variability; parametric process diagnosis system; process conditions diagnosis; process simulators; statistical analysis techniques; Algorithm design and analysis; Application software; Circuit faults; Circuit simulation; Computer applications; Data engineering; Integrated circuit yield; Manufacturing; Production; Statistical analysis;
fLanguage :
English
Journal_Title :
Components, Hybrids, and Manufacturing Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
0148-6411
Type :
jour
DOI :
10.1109/33.148497
Filename :
148497
Link To Document :
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