A statistical approach to identify semiconductor process equipment related yield problems

Identifying and eliminating yield limiting factors is a critical requirement for achieving accelerated yield improvement rates. This paper presents a systematic methodology for identifying yield problems caused by manufacturing equipment in a semiconductor process. The method uses Multiple Die Yield Analysis methodology to decompose the probe yield into two major components: a non-random systematic yield Y/sub s/, and a random yield Y/sub r/. Systematic or random yield loss problems caused by process equipment are identified based on the analysis of the statistical significance on Y/sub s/ and Y/sub r/ between the highest yield equipment and all other equipment in the same process step. Applications of the proposed analysis method to many manufacturing lines' data have been very successful in identifying process equipment related yield problems.

[1]  Douglas A. Wolfe,et al.  Nonparametric Statistical Methods , 1973 .

[2]  B. F. Schriever,et al.  The Number of Classes in Chi-Squared Goodness-of-Fit Tests , 1985 .

[3]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[4]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[5]  M. B. Wilk,et al.  Approximations for the Null Distribution of the W Statistic , 1968 .

[6]  A. Y. Wong,et al.  Micro yield modeling for IC processes , 1995, 1995 IEEE TENCON. IEEE Region 10 International Conference on Microelectronics and VLSI. 'Asia-Pacific Microelectronics 2000'. Proceedings.

[7]  F. Mosteller,et al.  Understanding robust and exploratory data analysis , 1985 .

[8]  Allan Y. Wong.  A statistical parametric and probe yield analysis methodology [IC manufacture] , 1996, Proceedings. 1996 IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems.