Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process

2018 ◽  
Vol 31 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Ki Bum Lee ◽  
Chang Ouk Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chemical Mechanical Planarization ◽  
Virtual Metrology ◽  
Planarization Process

scholarly journals Virtual Metrology applied in Run-to-Run Control for a Chemical Mechanical Planarization process

2017 ◽  
Vol 783 ◽  
pp. 012042 ◽  
Author(s):  
M.A. Jebri ◽  
E.M. El Adel ◽  
G. Graton ◽  
M. Ouladsine ◽  
J. Pinaton
Keyword(s):  
Chemical Mechanical Planarization ◽  
Virtual Metrology ◽  
Planarization Process

Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks

2018 ◽  
Vol 62 ◽  
pp. 44-54 ◽  
Author(s):  
Xiaodong Jia ◽  
Yuan Di ◽  
Jianshe Feng ◽  
Qibo Yang ◽  
Honghao Dai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Chemical Mechanical Planarization ◽  
Virtual Metrology ◽  
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.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

No-Reference Utility Estimation with a Convolutional Neural Network

2018 ◽  
Vol 2018 (9) ◽  
pp. 202-1-202-6 ◽  
Author(s):  
Edward T. Scott ◽  
Sheila S. Hemami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Utility Estimation

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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