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.

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 A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

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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