Spatial stochastic processes for yield and reliability management with applications to nano electronics
| dc.contributor | Kuo, Way | |
| dc.creator | Hwang, Jung Yoon | |
| dc.date.accessioned | 2005-02-17T21:03:15Z | |
| dc.date.accessioned | 2017-04-07T19:49:37Z | |
| dc.date.available | 2005-02-17T21:03:15Z | |
| dc.date.available | 2017-04-07T19:49:37Z | |
| dc.date.created | 2004-12 | |
| dc.date.issued | 2005-02-17 | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | http://hdl.handle.net/1969.1/1500 | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.subject | yield modeling | |
| dc.subject | reliability | |
| dc.subject | integrated circuit | |
| dc.subject | spatial stochastic processes | |
| dc.subject | model-based clustering | |
| dc.title | Spatial stochastic processes for yield and reliability management with applications to nano electronics | |
| dc.type | Book | |
| dc.type | Thesis |