Quantitative In-Situ Measurement of Asperity Compression Under the Wafer During Polishing

2005 ◽  
Vol 867 ◽  
Author(s):  
Caprice Gray ◽  
Daniel Apone ◽  
Chris Rogers ◽  
Vincent P. Manno ◽  
Chris Barns ◽  
...  
Keyword(s):  
Material Removal ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Static Case ◽  
Asperity Height ◽  
Dual Emission ◽  
Deep Wells ◽  
Dynamic Case ◽  
Slurry Layer

AbstractThe interaction of the wafer, slurry and pad determines the material removal rate during Chemical Mechanical Planarization (CMP). Dual emission laser induced fluorescence (DELIF) provides a means to measure the slurry layer thickness between the wafer and a Fruedenbergy FX9 pad during CMP with high spatial (4.3 μm/pixel) and temporal resolution (2 Hz). In this paper we present some preliminary measurements of pad compression using DELIF to measure the standard deviation of asperity height. Static slurry layer images were captured at high (70 kPa) and low (0 kPa) down-force applied to the wafer. In-situ, dynamic images at 10 kPa downforce applied to etched wafers were imaged. Two wafers were etched such that they contain square wells, one wafer with 27 μm and the other will 14.5 μm deep wells. In the static case, asperity compression is directly related the amount of fluid displaced. In the dynamic case, asperity compression is 35% greater under the 27 μm wells than the 14.5 μm wells.

Quantitative In-Situ Measurement of Asperity Compression During Chemical Mechanical Planarization

2005 ◽  
Author(s):  
Caprice Gray ◽  
Daniel Apone ◽  
Chris Rogers ◽  
Vincent P. Manno ◽  
Chris Barns ◽  
...  
Keyword(s):  
Film Thickness ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Chemical Mechanical Planarization ◽  
Laser Induced Fluorescence ◽  
Calibration Factor ◽  
Dual Emission ◽  
Image Intensity ◽  
Slurry Layer

Modifications to the Dual Emission Laser Induced Fluorescence (DELIF) procedure used to collect images of the slurry layer between the polishing pad and wafer during Chemical Mechanical Planarization (CMP) have provided a means to attain instantaneous, high spatial resolution images of slurry film thickness. Presented here is a technique to determine the calibration factor that correlates image intensity to slurry film thickness. This presentation will discuss how to determine slurry layer shape near wafer features, pad roughness, and pad compressibility.

Finite Element Modeling of Pad Deformation due to Diamond Disc Conditioning in Chemical Mechanical Polishing (CMP)

2012 ◽  
Author(s):  
Emmanuel A. Baisie ◽  
Z. C. Li ◽  
X. H. Zhang
Keyword(s):  
Mechanical Properties ◽  
Finite Element ◽  
Material Removal ◽  
High Performance ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Element Analysis ◽  
Fea Model ◽  
Pad Wear ◽  
Diamond Disc

Chemical mechanical planarization (CMP) is widely used to planarize and smooth the surface of semiconductor wafers. In CMP, diamond disc conditioning is traditionally employed to restore pad planarity and surface asperity. Pad deformation which occurs during conditioning affects the material removal mechanism of CMP since pad shape, stress and strain are related to cut rate during conditioning, pad wear rate and wafer material removal rate (MRR) during polishing. Available reports concerning the effect of diamond disc conditioning on pad deformation are based on simplified models of the pad and do not consider its microstructure. In this study, a two-dimensional (2-D) finite element analysis (FEA) model is proposed to analyze the interaction between the diamond disc conditioner and the polishing pad. To enhance modeling fidelity, image processing is utilized to characterize the morphological and mechanical properties of the pad. An FEA model of the characterized pad is developed and utilized to study the effects of process parameters (conditioning pressure and pad stiffness) on pad deformation. The study reveals that understanding the morphological and mechanical properties of CMP pads is important to the design of high performance pads.

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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