Effect of Pad Surface Micro-Texture on Removal Rate during Interlayer Dielectric Chemical Mechanical Planarization Process

2013 ◽  
Vol 52 (1R) ◽  
pp. 018001 ◽  
Author(s):  
Xiaoyan Liao ◽  
Yun Zhuang ◽  
Leonard J. Borucki ◽  
Jiang Cheng ◽  
Siannie Theng ◽  
...  
2005 ◽  
Vol 291-292 ◽  
pp. 395-400
Author(s):  
Dong Ming Guo ◽  
X.J. Li ◽  
Zhu Ji Jin ◽  
Ren Ke Kang

The slurry of Copper chemical mechanical planarization for ultra large-scale integrate circuit (ULSI) usually contains oxidizer, etchant, complexing reagent and corrosive inhibitor. In planarization process, the corrosive inhibitor has an important effect on the planarization. Only if the concave surface of the wafer is properly protected from corrosion by the inhibitor, the process can obtain perfect surface planarity. In this paper, with Fe(NO3)3 as an oxidant and several corrosive inhibitors selected, the corrosive efficiency of slurries are investigated. The static etching rate and the polishing material removal rate of wafer are obtained. The electrochemical behavior of the slurry is investigated by the potentiodynamic polarization studies. And the inhibitive efficiency of the related corrosive inhibitors is calculated from the polarization data. X-ray diffraction is applied to analyze the composition modification of the copper surface. Atom force microscopy is applied to measure the surface topography of corrosive copper wafer and the value of surface roughness is measured by ZYGO surface analysis system. The result shows that the benzotriazole (BTA) is a perfect corrosive inhibitor. With addition of 0.1wt% BTA into 1.5wt% Fe(NO3)3 solution, the inhibitive efficiency can reach 99.1%. The polishing test shows that if only 1.5wt% Fe(NO3)3 is added as an oxidizer without any other additive, the surface roughness of the polished wafer is 26.9Å, while with 0.1wt%BTA added in the meantime, 5.2 Å of surface roughness can be obtained.


2012 ◽  
Vol 488-489 ◽  
pp. 831-835
Author(s):  
Hojoong Kim ◽  
Andy Kim ◽  
Tae Sung Kim

The Chemical mechanical planarization (CMP) process has become a primary planarization technique required for the manufacture of advanced integrated circuit (IC) devices. As the feature size of IC chips shrinks down to 65 nm and below, the role of CMP as a robust planarization process becomes increasingly important. In this work, we evaluated surface roughness of CMP pad to correlate the roughness with CMP performance such as material removal rate (MRR) and pad lifetime. Pad surface was analyzed by 3-dimensional profiler and scanning electron microscope (SEM). We found that MRR could be varied with the pad life time and roughness. We also found that suitable roughness range is exist to get stable CMP performance. Finally, we introduced ‘pre-conditioning’ method to manage the roughness of CMP pad to get stable CMP performance at the initial pad life time.


2005 ◽  
Vol 867 ◽  
Author(s):  
Feng Q Liu ◽  
Liang Chen ◽  
Alain Duboust ◽  
Stan Tsai ◽  
Antoine Manens ◽  
...  

AbstractEcmpTM is a revolutionary planarization technology uniquely combining removal rate controlled by charge with superior planarization efficiency in the near no shear regime. In addition, the electrochemical removal mechanism has excellent within-wafer profile control. Multiple electrical zones configuration combined with a precise end-point control by electric charge, make it more predictable to control the remaining thickness and profile of copper film. The factors affecting the planarization such as the concentration and the efficiency of the inhibitors will be discussed in this paper. Meanwhile a planarization mechanism for Ecmp will be proposed to match the high planarization efficiency. The effects of applied voltage on removal rate and planarization efficiency will be presented in this paper. The electrical feature allows Ecmp to be a planarization process with removal rate independent of down force, enabling a wide removal rate window based on applied voltage.


2008 ◽  
Vol 600-603 ◽  
pp. 839-842 ◽  
Author(s):  
Michael L. White ◽  
Stan Reggie ◽  
Nevin Naguib ◽  
Kenneth Nicholson ◽  
Jeffrey Gilliland ◽  
...  

The influence of the chemical mechanical planarization process on the 4o off-axis 4HN SiC removal rate for silicon carbide slurry produced by Cabot Microelectronics Corporation (CMC) has been studied. A detailed kinetic analysis was applied and the linearity of an Arrhenius-like activation energy plot suggests that the primary removal occurs from particles adhered to the pad surface.


2021 ◽  
Author(s):  
Liqiao Xia ◽  
Pai Zheng ◽  
Chao Liu

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.


2005 ◽  
Vol 867 ◽  
Author(s):  
Serdar Aksu

AbstractChemical mechanical planarization (CMP), which can globally planarize both silicon dioxide (the prevalent interlayer dielectric), and copper films, has become the key process in the damascene method used for producing integrated circuit (IC) devices with multilevel copper interconnects. Cu CMP is typically carried out with slurries containing oxidizing agents, complexing agents, and corrosion inhibitors as the principal chemical components. In such slurries, complexing agents enhance the solubility of copper and increase the dissolution rate of the abraded material in Cu CMP. They also assist achieving high copper removal rates during dynamic polishing conditions. The nature of the complexing agent used, the pH and the redox potential of the slurry system are among the main factors controlling the dissolution and passivation behaviors of copper during CMP. Consequently, these factors are intimately related to the key CMP performance metrics such as removal rate and planarity. In this paper, potentialpH diagrams of copper in aqueous systems containing a number of organic complexing agents including ethylenediaminetetraacetic acid (EDTA), nitrilotriacetic acid (NTA), oxalic acid and malonic acid are presented. The predominance regions of copper complexes under different copper and ligand activities and their implications on copper removal during CMP are discussed.


Author(s):  
Yuan Di ◽  
Xiaodong Jia ◽  
Jay Lee

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.


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