Tutorial on Applying the VM Technology for TFT-LCD Manufacturing

In the high-tech industries, on-line quality monitoring on each workpiece under processing is required to ensure process stability and improve yield rate. However, conducting workpiece-by-workpiece actual metrology is very expensive and time-consuming. In this case, a novel idea is to use “virtual metrology” (VM) that conjectures workpiece quality based on process data collected from production equipment with a slight supplement of actual metrology data. The purpose of this tutorial paper is to select the thin film transistor-liquid crystal display (TFT-LCD) manufacturing processes as the illustrative examples for demonstrating the methodology of fab-wide implementation of the VM technology systematically. To begin with, a survey of VM-related literature is performed. Then, the features of an effective and refined VM system are presented with the automatic VM (AVM) system developed by the authors as a case study, followed by introduction of the TFT-LCD production tools and manufacturing processes. After that, the generic deployment schemes of the VM technology for the TFT-LCD tools are proposed. Finally, illustrative examples with the AVM system as a case study are presented to show how the VM technology applies to TFT-LCD manufacturing.

[1]  Yuan Kang,et al.  Virtual Metrology Technique for Semiconductor Manufacturing , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[2]  Shane A. Lynn,et al.  Global and Local Virtual Metrology Models for a Plasma Etch Process , 2012, IEEE Transactions on Semiconductor Manufacturing.

[3]  Fan-Tien Cheng,et al.  Accuracy and Real-Time Considerations for Implementing Various Virtual Metrology Algorithms , 2008, IEEE Transactions on Semiconductor Manufacturing.

[4]  C.H. Yu,et al.  Virtual metrology: a solution for wafer to wafer advanced process control , 2005, ISSM 2005, IEEE International Symposium on Semiconductor Manufacturing, 2005..

[5]  C. Spanos,et al.  Virtual metrology modeling for plasma etch operations , 2008, 2008 International Symposium on Semiconductor Manufacturing (ISSM).

[6]  D.M. Tilbury,et al.  An Approach for Factory-Wide Control Utilizing Virtual Metrology , 2007, IEEE Transactions on Semiconductor Manufacturing.

[7]  Fan-Tien Cheng,et al.  Developing a dual-stage indirect virtual metrology architecture , 2010, 2010 IEEE International Conference on Robotics and Automation.

[8]  K.M. Monahan Enabling DFM and APC strategies at the 32 nm technology node , 2005, ISSM 2005, IEEE International Symposium on Semiconductor Manufacturing, 2005..

[9]  Fan-Tien Cheng,et al.  A virtual metrology scheme for predicting CVD thickness in semiconductor manufacturing , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[10]  Fan-Tien Cheng,et al.  A Novel Virtual Metrology Scheme for Predicting CVD Thickness in Semiconductor Manufacturing , 2007, IEEE/ASME Transactions on Mechatronics.

[11]  Shi-Shang Jang,et al.  Performance Analysis of EWMA Controllers Subject to Metrology Delay , 2008, IEEE Transactions on Semiconductor Manufacturing.

[12]  Taho Yang,et al.  A quality prognostics scheme for semiconductor and TFT-LCD manufacturing processes , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[13]  Beibei Ma,et al.  Estimation and Control in Semiconductor Etch: Practice and Possibilities , 2010, IEEE Transactions on Semiconductor Manufacturing.

[14]  Fan-Tien Cheng,et al.  Developing an Automatic Virtual Metrology System , 2012, IEEE Transactions on Automation Science and Engineering.

[15]  David Shan-Hill Wong,et al.  Comparison of Embedded System Design for Industrial Applications , 2011, IEEE Transactions on Industrial Informatics.

[16]  Fan-Tien Cheng,et al.  NN-Based Key-Variable Selection Method for Enhancing Virtual Metrology Accuracy , 2009 .

[17]  James Moyne,et al.  Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares , 2008 .

[18]  Yu-Chuan Su,et al.  Intelligent prognostics system design and implementation , 2006, IEEE Transactions on Semiconductor Manufacturing.

[19]  Yi-Ting Huang,et al.  Automatic Data Quality Evaluation for the AVM System , 2011, IEEE Transactions on Semiconductor Manufacturing.

[20]  Fan-Tien Cheng,et al.  Benefit Model of Virtual Metrology and Integrating AVM Into MES , 2011, IEEE Transactions on Semiconductor Manufacturing.

[21]  M. Kitabata,et al.  Prevention of Copper Interconnection Failure in System on Chip Using Virtual Metrology , 2009, IEEE Transactions on Semiconductor Manufacturing.

[22]  Li-Ren Lin,et al.  Run-to-run control utilizing the AVM system in the solar industry , 2011, 2011 e-Manufacturing & Design Collaboration Symposium & International Symposium on Semiconductor Manufacturing (eMDC & ISSM).

[23]  Yu-Chuan Su,et al.  A processing quality prognostics scheme for plasma sputtering in TFT-LCD manufacturing , 2006, IEEE Transactions on Semiconductor Manufacturing.

[24]  Fan-Tien Cheng,et al.  Dual-Phase Virtual Metrology Scheme , 2007, IEEE Transactions on Semiconductor Manufacturing.

[25]  Fan-Tien Cheng,et al.  Applying the Automatic Virtual Metrology system to obtain tube-to-tube control in a PECVD tool , 2013 .

[26]  Fan-Tien Cheng,et al.  Evaluating Reliance Level of a Virtual Metrology System , 2008, IEEE Transactions on Semiconductor Manufacturing.

[27]  Fan-Tien Cheng,et al.  Selection Schemes of Dual Virtual-Metrology Outputs for Enhancing Prediction Accuracy , 2011, IEEE Transactions on Automation Science and Engineering.

[28]  Fan-Tien Cheng,et al.  Dynamic-Moving-Window Scheme for Virtual-Metrology Model Refreshing , 2012, IEEE Transactions on Semiconductor Manufacturing.