Modeling and Experimental Analysis of the Material Removal Rate in the Chemical Mechanical Planarization of Dielectric Films and Bare Silicon Wafers

2001 ◽  
Vol 148 (10) ◽  
pp. G581 ◽  
Author(s):  
H. Hocheng ◽  
H. Y. Tsai ◽  
Y. T. Su
Keyword(s):  
Material Removal Rate ◽  
Experimental Analysis ◽  
Material Removal ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Silicon Wafers ◽  
Dielectric Films

Study on Chemical Mechanical Polishing Removal Mechanism of Monocrystalline Silicon

2014 ◽  
Vol 538 ◽  
pp. 40-43
Author(s):  
Hong Wei Du ◽  
Yan Ni Chen
Keyword(s):  
Material Removal Rate ◽  
Chemical Mechanical Polishing ◽  
Material Removal ◽  
Removal Rate ◽  
Silicon Wafers ◽  
Monocrystalline Silicon ◽  
Mechanical Polishing ◽  
Removal Mechanism ◽  
Material Removal Mechanism ◽  
Polishing Pressure

In this paper, material removal mechanism of monocrystalline silicon by chemical etching with different solutions were studied to find effective oxidant and stabilizer. Material removal mechanism by mechanical loads was analyzed based on the measured acoustic signals in the scratching processes and the observation on the scratched surfaces of silicon wafers. The chemical mechanical polishing (CMP) processes of monocrystalline silicon wafers were analyzed in detail according to the observation and measurement of the polished surfaces with XRD. The results show that H2O2 is effective oxidant and KOH stabilizer. In a certain range, the higher concentration of oxidant, the higher material removal rate; the higher the polishing liquid PH value, the higher material removal rate. The polishing pressure is an important factor to obtain ultra-smooth surface without damage. Experimental results obtained silicon polishing pressure shall not exceed 42.5kPa.

Understanding Selectivity on Germanium/SiO2 Chemical Mechanical Planarization Through Design of Experiments

2015 ◽  
Vol 1790 ◽  
pp. 19-24
Author(s):  
Ayse Karagoz ◽  
James Mal ◽  
G. Bahar Basim
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Surface Active Agent ◽  
Chemical Mechanical Planarization ◽  
Sodium Dodecyl ◽  
Active Agent ◽  
High Mobility ◽  
High Removal Rate ◽  
The Impact

ABSTRACTThe continuous trend of achieving more complex microelectronics with smaller nodes yet larger wafer sizes in microelectronics manufacturing lead to aggressive development requirements for chemical mechanical planarization (CMP) process. Particularly, beyond the 14 nm technology the development needs made it a must to introduce high mobility channel materials such as Ge. CMP is an enabler for integration of these new materials into future devices. In this study, we implemented a design of experiment (DOE) methodology in order to understand the optimized CMP slurry parameters such as optimal concentration of surface active agent (sodium dodecyl sulfate-SDS), concentration of abrasive particles and pH from the viewpoint of high removal rate and selectivity while maintaining a defect free surface finish. The responses examined were particle size distribution (slurry stability), zeta potential, material removal rate (MRR) and the surface defectivity as a function of the selected design variables. The impact of fumed silica particle loadings, oxidizer (H2O2) concentration, SDS surfactant concentration and pH were analyzed on Ge/silica selectivity through material removal rate (MRR) surface roughness and defectivity analyses.

Prediction of Material Removal Rate for Chemical Mechanical Planarization Using Decision Tree-Based Ensemble Learning

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Zhixiong Li ◽  
Dazhong Wu ◽  
Tianyu Yu
Keyword(s):  
Decision Tree ◽  
Ensemble Learning ◽  
Material Removal Rate ◽  
Material Removal ◽  
Learning Algorithm ◽  
Removal Rate ◽  
Semiconductor Industry ◽  
Chemical Mechanical Planarization ◽  
Gradient Boosting ◽  
Ensemble Learning Algorithm

Chemical mechanical planarization (CMP) has been widely used in the semiconductor industry to create planar surfaces with a combination of chemical and mechanical forces. A CMP process is very complex because several chemical and mechanical phenomena (e.g., surface kinetics, electrochemical interfaces, contact mechanics, stress mechanics, hydrodynamics, and tribochemistry) are involved. Predicting the material removal rate (MRR) in a CMP process with sufficient accuracy is essential to achieving uniform surface finish. While physics-based methods have been introduced to predict MRRs, little research has been reported on monitoring and predictive modeling of the MRR in CMP. This paper presents a novel decision tree-based ensemble learning algorithm that can train the predictive model of the MRR. The stacking technique is used to combine three decision tree-based learning algorithms, including the random forests (RF), gradient boosting trees (GBT), and extremely randomized trees (ERT), via a meta-regressor. The proposed method is demonstrated on the data collected from a CMP tool that removes material from the surface of wafers. Experimental results have shown that the decision tree-based ensemble learning algorithm using stacking can predict the MRR in the CMP process with very high accuracy.

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