Introduction of Engineering Data Analysis System for Industrial Statistics, Part II for statistical methods applied in semiconductor industrial.

1. 半導體產業常用的統計方法

8. Correlation Coefficient

9. Regerssion Residual standard error: 6.297 on 201 degrees of freedom Multiple R-Squared: 0.4439, Adjusted R-squared: 0.3111 F-statistic: 3.343 on 48 and 201 DF, p-value: 1.455e-09  Improve the model?

11. Result:

15. Fab 廠常用的統計方法 Statistical Method Purpose Distribution Basic material for statistical tests. Used to characterize a population based upon a sample . Hypothesis testing Decide whether data under investigation indicates that elements of concern are the “same” or “different.” Experimental design and analysis of variance Determine significance of factors and models; Decompose observed variation into constituent elements. Categorical modeling Use when result or response is discrete (such as “very rough,” “ rough,” or “smooth”). Understand relationships, determine process margin, and optimize process. Statistical process control Determine if system is operating as expected. Regression Yield modeling. Yield impact Duane S. Boning, Jerry Stefani and Stephanie W. Butler: Statistical Methods for Semiconductor manufacturing

17. Statistical Process Control Normal, +- 3 sigma ~ 99.7

34. Display – violin plot

35. Display -- Correlogram

40. Golden Path P1 P2 P3 E1 E2 T1 T2 3*2*2 = 12 combination for 3 steps  Whole combination? Partition method Golden wafer/golden lot tracking