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.

Investigation of Correlation between Polishing Characteristic and Pad Roughness during Chemical Mechanical Planarization Process

2012 ◽  
Vol 488-489 ◽  
pp. 831-835
Author(s):  
Hojoong Kim ◽  
Andy Kim ◽  
Tae Sung Kim
Keyword(s):  
Integrated Circuit ◽  
Material Removal ◽  
Removal Rate ◽  
Chemical Mechanical Planarization ◽  
Life Time ◽  
Feature Size ◽  
3 Dimensional ◽  
Planarization Process ◽  
Scanning Electron

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.

Multi-Constraint Optimization for Grinding Nickel-Based Alloys

2013 ◽  
Author(s):  
Radu Pavel ◽  
Xiqun Wang ◽  
Anil K. Srivastava
Keyword(s):  
Material Removal Rate ◽  
Surface Integrity ◽  
Material Removal ◽  
Large Scale ◽  
Model Building ◽  
Removal Rate ◽  
Optimal Combination ◽  
Sensor Data ◽  
Wheel Wear ◽  
Grinding Parameters

Nickel-based alloys (Ni-based alloys) are used on a large scale in military, aerospace, missile and defense applications with the aim of improving performance, life, and fuel efficiency. Grinding is extensively used for final finishing of these components. Due to their specific material properties, such as work-hardening and low thermal conductivity, the workpieces made of Ni-based alloys are difficult to grind. The difficulty consists in finding the combination of dressing and grinding parameters that generate the prescribed dimensions, finish, and surface integrity of the finished part with high productivity. Increasing productivity is generally associated with increasing the material removal rate. This, in turn, can create detrimental effects on the ground parts such as micro-cracks, high residual stresses, white layers, and thermal damage. This paper presents a novel methodology for determining an optimal combination of dressing and grinding parameters with respect to maximizing the material removal rate, while taking into account a number of process constraints including: grinding force, power, surface roughness, wheel wear, and surface integrity. According to this methodology, predictive models for grinding behavior are determined using a reduced number of experiments based on an in-process, fast sensor data acquisition system. The models are used as inputs for the multiple criterion optimization program based on a genetic algorithm approach. A CNC surface grinding machine was instrumented to allow process monitoring and data collection. The model building and the optimization methodology have been validated using specimens made of Ni-based alloys. The workpiece materials and the range of the grinding parameters were selected according to applications from aerospace industry. The results support the use of adopted methodology for finding the optimal combination of dressing and grinding parameters.

An Investigation of the Influence of Orientation on CMP through Nanoscratch Testing

2009 ◽  
Vol 1157 ◽  
Author(s):  
Sarah Neyer ◽  
Burak Ozdoganlar ◽  
C. Fred Higgs
Keyword(s):  
Material Removal Rate ◽  
Integrated Circuit ◽  
Material Removal ◽  
Removal Rate ◽  
Orientation Imaging Microscopy ◽  
Orientation Dependence ◽  
Orientation Imaging ◽  
Ic Manufacturing ◽  
Crystallographic Planes ◽  
Scratch Tests

AbstractWith the increase in integrated circuit (IC) feature density, the quality of chemical mechanical polishing (CMP) becomes more important as the copper interconnects decrease in size. The optimization of the IC manufacturing process will be greatly enhanced if the nanoscale effects on CMP are better understood. CMP-related wear at the sub-micron scale, where a single particle affects the microstructure of individual copper features within the substrate, needs to be investigated to account for wafer-scale variations. Hardness is known to affect the material removal rate, but the grain level mechanism of the removal process is not yet well known. In this work, the orientation-dependence of wear has been investigated by performing nanoscale scratch tests on single crystal copper along different crystallographic planes, indentified using orientation imaging microscopy (OIM). An analysis of the surface forces and post-scratch topography produced during the scratch tests was conducted and the results have been interpreted from a CMP perspective. Ultimately, these results are expected to refine existing material removal rate models which do not consider the sensitivity of microstructure on the CMP 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.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

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