A data-driven statistical approach to analyzing process variation (2007)
| Venue: | in 65 nm SOI technology,” in Proc. Int. Symp. Quality Electronic Design |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Cho07adata-driven,
author = {Choongyeun Cho and Daeik Kim and Jonghae Kim and Jean-olivier Plouchart and Daihyun Lim and Sangyeun Cho and Robert Trzcinski},
title = {A data-driven statistical approach to analyzing process variation},
booktitle = {in 65 nm SOI technology,” in Proc. Int. Symp. Quality Electronic Design},
year = {2007},
pages = {699--702}
}
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Abstract
This paper presents a simple yet effective method to analyze process variations using statistics on manufacturing in-line data without assuming any explicit underlying model for process variations. Our method is based on a variant of principal component analysis and is able to reveal systematic variation patterns existing on a die-to-die and wafer-to-wafer level individually. The separation of die variation from wafer variation can enhance the understanding of a nature of the process uncertainty. Our case study based on the proposed decomposition method shows that the dominating die-todie variation and wafer-to-wafer variation represent 31% and 25 % of the total variance of a large set of in-line parameters in 65nm SOI CMOS technology. 1.
Citations
| 71 | Modeling and analysis of manufacturing variations - Nassif - 2001 |
| 18 | Principal Component Analysis; Springer-Verlag - Jolliffe - 2002 |
| 3 | Fundamentals of Statistical Quality Control - Pond - 2004 |
| 2 | Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis - Cherry, Qin - 2006 |
| 2 | Leeuw. “Component analysis with different sets of constraints on different dimensions,” Psychometrika - Takane, Kiers, et al. - 1995 |
| 1 | A PCA-Based PCM Data Analyzing Method for Diagnosing Process Failures - Yan - 2006 |
