A Wafer-Scale Material Removal Rate Model for Chemical Mechanical Planarization

Xu, Qinzhi; Chen, Lan; Liu, Jianyun; Cao, He

doi:10.1149/2162-8777/abadea

Cited by 9 publications

(9 citation statements)

References 54 publications

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“…Material removal rate (MRR) is one of the essential metrics to measure the quality of CMP since defect and depression generated on wafers material will increase the fault rate of CMP [2]. Traditionally, research focuses on the study of the effect of components [3] and manufacturing environment [4] in CMP and how they affect the MRR. Moreover, mathematical approaches try to fit a curve to predict the MRR [5] or establish a mathematics model to simulate the manufacturing process [6].…”

Section: Introductionmentioning

confidence: 99%

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

Xia

Zheng

Liu

2021

Volume 2: 41st Computers and Information in Engineering Conference (CIE)

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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.

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Section: Introductionmentioning

confidence: 99%

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

Xia

Zheng

Liu

2021

Volume 2: 41st Computers and Information in Engineering Conference (CIE)

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“…However, CMP is a very complicated process and the underlying removal mechanism is still not fully elucidated at present. 3,4,23 It is generally recognized that the surface removal of polished materials is largely determined by the synergistic interaction of chemical reaction and mechanical abrasion originated from the combinational effect of the slurry and polishing pad. Moreover, the design patterndependent issues and problems in the CMP process can also impact the polishing performance significantly by causing dishing and erosion defects on different pattern regions.…”

mentioning

confidence: 99%

“…Fortunately, the surface planarity and uniformity can be qualitatively predicted by physicsbased CMP simulation models which are capable of capturing the fundamental insights into the surface removal mechanism and providing optimization methods for process parameter configration. 4,[21][22][23] In order to obtain a good planarization control of the wafer surface profile, various types of CMP simulation models were proposed to describe the removal characteristic in different points of view at different length scales. Based on the abrasive wear theory, the removal rate was mainly determined by consideration of the particle concentration and size distribution in particle-scale CMP modeling.…”

mentioning

confidence: 99%

“…Based on the abrasive wear theory, the removal rate was mainly determined by consideration of the particle concentration and size distribution in particle-scale CMP modeling. [23][24][25] Wafer-scale CMP models were also constructed to reveal the intrinsic relationship between the removal mechanism and the material, equipment and process parameters on the basis of chemical reaction kinetics, fluid hydrodynamics and contact mechanics theories. 2,6,12,14,18,23,[26][27][28][29][30][31][32] The design pattern effects on the removal rate and surface planarity can be described by feature-scale CMP models through considering the feature geometry and contact behavior between the wafer surface and the polishing pad.…”

mentioning

confidence: 99%

“…[23][24][25] Wafer-scale CMP models were also constructed to reveal the intrinsic relationship between the removal mechanism and the material, equipment and process parameters on the basis of chemical reaction kinetics, fluid hydrodynamics and contact mechanics theories. 2,6,12,14,18,23,[26][27][28][29][30][31][32] The design pattern effects on the removal rate and surface planarity can be described by feature-scale CMP models through considering the feature geometry and contact behavior between the wafer surface and the polishing pad. [33][34][35][36] Furthermore, in order to optimize the functionality and performance of design chips, chip-scale CMP models were developed further to simulate the surface height variation of different pattern structures by introducing geometric and phenomenological approaches.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Physics-Based Chip-Scale Surface Profile Model for Tungsten Chemical Mechanical Planarization

Cao

Liu

2023

ECS J. Solid State Sci. Technol.

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A novel physics-based chip-scale surface profile model is proposed to focus on investigating the influence of the design pattern effects on the tungsten surface topography in the chemical mechanical planarization (CMP) process. The two-scale contact pressure computation method is constructed to obtain an accurate pressure distribution between the wafer surface and the polishing pad. The chip-scale contact mechanics-based global pressure has been first introduced to capture the long range height variation of W CMP caused by deposition and polishing processes. Then, the feature-scale pattern dependent effect is considered to accurately calculate the local contact pressure which is further integrated with the fundamental of steady-state oxidation reaction to construct a new material removal rate model used for simulation of surface height evolution. The model prediction results are consistent with the collected experimental data in predicting the dishing effect at different slurry conditions. The present CMP model can be utilized to assist in analyzing the influence of the design pattern effects on the wafer surface topography and be readily incorporated into a design for manufacturability flow to form a chip-scale planarity simulator to detect the hotspots of the entire design layout.

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A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization

et al. 2021

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The material removal rate (MRR) plays a critical role in the chemical mechanical planarization (CMP) process in the semiconductor industry. Many physics-based and data-driven approaches have been proposed to-date to predict the MRR. Nevertheless, most of them neglect the underlying equipment structure containing essential interaction mechanisms among different components. To fill the gap, this paper proposes a novel hypergraph convolution network (HGCN) based approach for predicting MRR in the CMP process. The main contributions include: 1) a generic hypergraph model to represent the interrelationships of complex equipment; and 2) a temporal-based prediction approach to learn the complex data correlation and high-order representation based on the hypergraph. To validate the effectiveness of the proposed approach, a case study is conducted by comparing with other cuttingedge models, of which it outperforms in several metrics. It is envisioned that this research can also bring insightful knowledge to similar scenarios in the manufacturing process.

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A Wafer-Scale Material Removal Rate Model for Chemical Mechanical Planarization

Cited by 9 publications

References 54 publications

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

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

A Physics-Based Chip-Scale Surface Profile Model for Tungsten Chemical Mechanical Planarization

A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization

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