Modeling effects of abrasive particle size and concentration on material removal at molecular scale in chemical mechanical polishing

2010 ◽  
Vol 257 (1) ◽  
pp. 249-253 ◽  
Author(s):  
Yongguang Wang ◽  
Yongwu Zhao ◽  
Wei An ◽  
Zifeng Ni ◽  
Jun Wang
1999 ◽  
Vol 566 ◽  
Author(s):  
Uday Mahajan ◽  
Marc Bielmann ◽  
Rajiv K. Singh

In this study, we have characterized the effects of abrasive properties, primarily particle size, on the Chemical Mechanical Polishing (CMP) of oxide films. Sol-gel silica particles with very narrow size distributions were used for preparing the polishing slurries. The results indicate that as particle size increases, there is a transition in the mechanism of material removal from a surface area based mechanism to an indentation-based mechanism. In addition, the surface morphology of the polished samples was characterized, with the results showing that particles larger than 0.5 μm are detrimental to the quality of the SiO2 surface.


Wear ◽  
2008 ◽  
Vol 265 (5-6) ◽  
pp. 721-728 ◽  
Author(s):  
Yongguang Wang ◽  
Yongwu Zhao ◽  
Jianzhong Jiang ◽  
Xufang Li ◽  
Jing bai

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Elon J. Terrell ◽  
C. Fred Higgs

Chemical mechanical polishing (CMP) is a manufacturing process that is commonly used to planarize integrated circuits and other small-scale devices during fabrication. Although a number of models have been formulated, which focus on specific aspects of the CMP process, these models typically do not integrate all of the predominant mechanical aspects of CMP into a single framework. Additionally, the use of empirical fitting parameters decreases the generality of existing predictive CMP models. Therefore, the focus of this study is to develop an integrated computational modeling approach that incorporates the key physics behind CMP without using empirical fitting parameters. CMP consists of the interplay of four key tribological phenomena—fluid mechanics, particle dynamics, contact mechanics, and resulting wear. When these physical phenomena are all actively engaged in a sliding contact, the authors call this particle-augmented mixed lubrication (PAML). By considering all of the PAML phenomena in modeling particle-induced wear (or material removal), this model was able to predict wear-in silico from a measured surface topography during CMP. The predicted material removal rate (MRR) was compared with experimental measurements of copper CMP. A series of parametric studies were also conducted in order to predict the effects of varying slurry properties such as solid fraction and abrasive particle size. The results from the model are promising and suggest that a tribological framework is in place for developing a generalized first-principle PAML modeling approach for predicting CMP.


2006 ◽  
Vol 129 (2) ◽  
pp. 436-437 ◽  
Author(s):  
L. Chang

Understanding of the mechanisms of material removal is of fundamental importance in chemical-mechanical planarization of semiconductor wafers. A plausible mechanism at work is that the material is removed at the molecular scale by debonding the chemistry-weakened molecules at the wafer surface. A sequence of order-of-magnitude calculations is carried out to substantiate this mechanism of chemical-mechanical polishing (CMP) materials removal. The analysis may lend further credence to the mechanism in addition to its underlying theoretical foundation.


2007 ◽  
Vol 129 (4) ◽  
pp. 933-941 ◽  
Author(s):  
Elon J. Terrell ◽  
C. Fred Higgs III

Chemical mechanical polishing (CMP) is a manufacturing process in which a wafer surface is polished by pressing it against a rotating pad that is flooded with slurry. The slurry itself is a fluid containing abrasive particles. Past experimentation has shown that the distribution of suspended particles in the slurry is significantly related to the distribution of material removal on the wafer during CMP. Therefore, this study involves the development and simulation of a model that predicts the kinematics and trajectory of the abrasive particles. The simulation results compare well to data from shear cell experiments data conducted by other researchers.


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