Author
Kincal, S., Başım, Gül Bahar
Publication Date
2013-01
Publication Place
-
Springer Science+Business Media
Subject
Chemical mechanical planarization (CMP), Conditioning, Pad profile modeling, Defectivity
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1543-186X
Record ID
47823b4c-f513-4e78-a1e9-c8d398686f27
Library Location
Mechanical Engineering
Date
2013-01
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
Chemical mechanical planarization (CMP) has been proven to be the best method to achieve within-wafer and within-die uniformity for multilevel metallization. Decreasing device dimensions and increasing wafer sizes continuously demand better planarization, which necessitates better understanding of all the variables of the CMP process. A recently highlighted critical factor, pad conditioning, affects the pad surface profile and consequently the wafer profile; in addition, it reduces defects by refreshing the pad surface during polishing. This work demonstrates the changes in the postpolish wafer profile as a function of pad wear. It also introduces a wafer material removal rate profile model based on the locally relevant Preston equation by estimating the pad thickness profile as a function of polishing time. The result is a dynamic predictor of how the wafer removal rate profile shifts as the pad ages. The model helps fine-tune the pad conditioner operating characteristics without the requirement for costly and lengthy experiments. The accuracy of the model is demonstrated by experiments as well as data from a real production line. Both experimental data and simulations indicate that the smaller conditioning disk size and extended conditioning sweep range help improve the post-CMP wafer planarization. However, the defectivity tends to increase when the conditioning disk sweeps out of the pad radius; hence, the pad conditioning needs to be designed by considering the specific requirements of the CMP process conducted. The presented model predicts the process outcomes without requiring detailed experimentation.
DOI
10.1007/s11664-012-2250-z
Cilt
42
