This study uses the spatial features of defects on the wafers to examine the
detection and control of process variation in semiconductor fabrication. It applies
spatial stochastic process to semiconductor yield modeling and the extrinsic reliabil-
ity estimation model. New yield models of integrated circuits based on the spatial
point process are established. The defect density which varies according to location
on the wafer is modeled by the spatial nonhomogeneous Poisson process. And, in
order to capture the variations in defect patterns between wafers, a random coeff-
cient model and model-based clustering are applied. Model-based clustering is also
applied to the fabrication process control for detecting these defect clusters that are
generated by assignable causes. An extrinsic reliability model using defect data and
a statistical defect growth model are developed based on the new yield model.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/1500 |
| Date | 17 February 2005 |
| Creators | Hwang, Jung Yoon |
| Contributors | Kuo, Way |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 946204 bytes, electronic, application/pdf, born digital |
Page generated in 0.0022 seconds