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.

Parametric Optimization of EN-31 Steel in Wire Cut Electro Discharge Machining by Taguchi Technique

2020 ◽  
Vol 12 (7) ◽  
pp. 881-887
Author(s):  
Sahil Sharma ◽  
Umesh Kumar Vates ◽  
Amit Bansal
Keyword(s):  
Material Removal Rate ◽  
Automobile Industry ◽  
Material Removal ◽  
Removal Rate ◽  
Dimensional Accuracy ◽  
Work Piece ◽  
Electro Discharge Machining ◽  
En 31 ◽  
The Impact ◽  
Dies And Moulds

Amongst the various methods of machining, Electro Discharge Machining is the convenient alternatives for the industries due to non-contact of work piece and tool. In the study of various EDM processes the main target is to achieve the better finish of surface, high material removal rate and good dimensional accuracy by regulating the different input parameters. There are various applications of EDM such as aerospace parts, medical equipments, dies and moulds, nuclear and automobile industry. In this experimental study, a trial has made to look the impact of input factors like pulse-on, pulse-off, peak current, tension of wire on rate of material removal, gap current and time for machining. Taguchi (L9 OA) and Analysis of Variance technique were used to optimize the outcomes for wire cut EDM of EN-31 tool steel. The outcomes revealed that Ton and Toff are the leading cogent factor for material removal rate and gap current respectively.

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.

An Investigation of Factors Affecting and Optimizing Material Removal Rate in Computer Controlled Ultra-Precision Polishing

2014 ◽  
Vol 625 ◽  
pp. 446-452
Author(s):  
Lai Ting Ho ◽  
Chi Fai Cheung ◽  
Liam Blunt ◽  
Sheng Yue Zeng
Keyword(s):  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Orthopedic Implants ◽  
Polishing Process ◽  
Factors Affecting ◽  
Ultra Precision ◽  
Computer Controlled ◽  
The Impact

There are numerous parameters and steps involved in a computer controlled ultra-precision polishing process (CCUP). The success of CCUP relies heavily on the understanding and optimization of material removal when new materials and new surfaces are polished. It is crucial to optimize the polishing parameters to enhance the effectiveness of the polishing process and to assess the impact of different process parameters on the material removal rate of particular difficult-to-machine materials such as CoCr alloys, which is commonly used in orthopedic implants. This paper aims at studying the process parameters and optimization of the parameter to enhance the material removal rate and quantify the contribution of process parameters.

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