A stack fusion model for material removal rate prediction in chemical-mechanical planarization process

2018 ◽  
Vol 99 (9-12) ◽  
pp. 2407-2416 ◽  
Author(s):  
Shuai Zhao ◽  
Yixiang Huang
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Fusion Model ◽  
Rate Prediction ◽  
Planarization Process

Predicting the Material Removal Rate in Chemical Mechanical Planarization Process: A Hypergraph Neural Network-Based Approach

2021 ◽  
Author(s):  
Liqiao Xia ◽  
Pai Zheng ◽  
Chao Liu
Keyword(s):  
Neural Network ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Semiconductor Industry ◽  
Chemical Mechanical Planarization ◽  
Smart Manufacturing ◽  
Complex Data ◽  
Data Correlation ◽  
Planarization Process

Abstract Material removal rate (MRR) plays a critical role in the operation of chemical mechanical planarization (CMP) process in the semiconductor industry. To date, many physics-based and data-driven approaches have been proposed to predict the MRR. Nevertheless, most of the existing methodologies neglect the potential source of its well-organized and underlying equipment structure containing interaction mechanisms among different components. To address its limitation, this paper proposes a novel hypergraph neural network-based approach for predicting the MRR in CMP. Two main scientific contributions are presented in this work: 1) establishing a generic modeling technique to construct the complex equipment knowledge graph with a hypergraph form base on the comprehensive understanding and analysis of equipment structure and mechanism, and 2) proposing a novel prediction method by combining the Recurrent Neural Network based model and the Hypergraph Neural Network to learn the complex data correlation and high-order representation base on the Spatio-temporal equipment hypergraph. To validate the proposed approach, a case study is conducted based on an open-source dataset. The experimental results prove that the proposed model can capture the hidden data correlation effectively. It is also envisioned that the proposed approach has great potentials to be applied in other similar smart manufacturing scenarios.

scholarly journals Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Yuan Di ◽  
Xiaodong Jia ◽  
Jay Lee
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Performance Indicator ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Integrated Model ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Virtual Metrology ◽  
Planarization Process

As an essential process in semiconductor manufacturing, Chemical Mechanical Planarization has been studied in recent decades and the material removal rate has been proved to be a critical performance indicator. Comparing with after-process metrology, virtual metrology shows advantages in production time saving and quick response to the process control. This paper presents an enhanced material removal rate prediction algorithm based on an integrated model and data-driven method. The proposed approach combines the physical mechanism and the influence of nearest neighbors, and extracts relevant features. The features are then input to construct multiple regression models, which are integrated to obtain the final prognosis. This method was evaluated by the PHM 2016 Data Challenge data sets and the result obtained the best mean squared error score among competitors.

A deep learning-based approach to material removal rate prediction in polishing

CIRP Annals ◽  
2017 ◽  
Vol 66 (1) ◽  
pp. 429-432 ◽  
Author(s):  
Peng Wang ◽  
Robert X. Gao ◽  
Ruqiang Yan
Keyword(s):  
Deep Learning ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Rate Prediction

Wafer Scale Modeling and Control for Yield Improvement in Wafer Planarization

2002 ◽  
Author(s):  
Sutee Eamkajornsiri ◽  
Ranga Narayanaswami ◽  
Abhijit Chandra
Keyword(s):  
Material Removal Rate ◽  
Integrated Circuit ◽  
Material Removal ◽  
Large Scale ◽  
Removal Rate ◽  
Control Strategies ◽  
Elastic Half Space ◽  
Modeling And Control ◽  
Planarization Process ◽  
Curvature Control

Chemical mechanical polishing (CMP) is a planarization process that produces high quality surfaces both locally and globally. It is one of the key process steps during the fabrication of very large scale integrated (VLSI) chips in integrated circuit (IC) manufacturing. CMP consists of a chemical process and a mechanical process being performed together to reduce height variation across a wafer. High and reliable wafer yield, which is dependent upon uniformity of the material removal rate across the entire wafer, is of critical importance in the CMP process. In this paper, the variations in material removal rate (MRR) variation across the wafer are analytically modeled assumimg a rigid wafer and a flexible polishing pad. The wafer pad contact is modeled as the indentation of a rigid indenter on an elastic half-space. Load and curvature control strategies are investigated for improving the wafer yield. The notion of curvature control is entirely new and has not been addressed in the literature. The control strategy is based on minimizing a moment function that represents the wafer curvature and the height of the oxide layer left for material removal. Simulation results indicate that curvature control can improve wafer yield significantly, and is more effective than just the load control.

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.

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