Feasibility Evaluation of Virtual Metrology for the Example of a Trench Etch Process

In semiconductor manufacturing, the implementation of advanced process control systems has become essential for cost effective manufacturing at high product quality. In addition to established process control methods, new control techniques such as virtual metrology, where post-process quality parameters are predicted from process and wafer state information need to be developed and implemented for critical process steps. This requires a fab-wide approach due to the objectives of VM, which are to supplement or replace stand-alone and in-line metrology operations, to support fault detection and classification, run-to-run control, or other new control entities such as predictive maintenance. Virtual metrology is typically based on statistical learning methods, and a large variety of potentially applicable algorithms are available. A key challenge of the virtual metrology application is proving its capability to produce precise predictions even in complex semiconductor manufacturing processes. In addition, virtual metrology applications need to be implementable into the specific automation and control environment already present in the respective fab. In this paper, the approach and results of a feasibility study toward the development and implementation of virtual metrology applications in a logic fab are presented. The feasibility study was performed for the prediction of trench depth after a complex dry etch process as the specific use case studied.

[1]  Shun'ichi Kaneko,et al.  Multiparametric Virtual Metrology Model Building by Job-Shop Data Fusion Using a Markov Chain Monte Carlo Method , 2013, IEEE Transactions on Semiconductor Manufacturing.

[2]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[3]  Gian Antonio Susto,et al.  A virtual metrology system based on least angle regression and statistical clustering , 2013 .

[4]  G. Fazio,et al.  Framework for integration of virtual metrology and predictive maintenance , 2012, 2012 SEMI Advanced Semiconductor Manufacturing Conference.

[5]  John V. Ringwood,et al.  Real-time virtual metrology and control for plasma etch , 2012 .

[6]  Bernd Barak,et al.  Data Mining and Support Vector Regression Machine Learning in Semiconductor Manufacturing to Improve Virtual Metrology , 2013, 2013 46th Hawaii International Conference on System Sciences.

[7]  Gerhard Kleineidam,et al.  Implementing Virtual Metrology into semiconductor production processes - an investment assessment , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[8]  H. Gold,et al.  Predictive sampling approach to dynamically optimize defect density control operations , 2012, 2012 SEMI Advanced Semiconductor Manufacturing Conference.

[9]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[10]  Claude Yugma,et al.  A smart sampling algorithm to minimize risk dynamically , 2010, 2010 IEEE/SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[11]  Giuseppe De Nicolao,et al.  Multilevel Lasso applied to Virtual Metrology in semiconductor manufacturing , 2011, 2011 IEEE International Conference on Automation Science and Engineering.

[12]  G. Rabinowitz,et al.  In-line Inspection Impact on Cycle Time and Yield , 2009, IEEE Transactions on Semiconductor Manufacturing.

[13]  Hendrik Purwins,et al.  Regression methods for prediction of PECVD Silicon Nitride layer thickness , 2011, 2011 IEEE International Conference on Automation Science and Engineering.

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

[15]  Gian Antonio Susto,et al.  A Virtual Metrology system for predicting CVD thickness with equipment variables and qualitative clustering , 2011, ETFA2011.

[16]  Haw Ching Yang,et al.  Development of an advanced manufacturing cloud for machine tool industry based on AVM technology , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[17]  Costas J. Spanos,et al.  Optimization of blended virtual and actual metrology schemes , 2012, Advanced Lithography.

[18]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[19]  Gian Antonio Susto,et al.  Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[20]  J. Friedman Stochastic gradient boosting , 2002 .

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

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

[23]  Giuseppe De Nicolao,et al.  Multistep virtual metrology approaches for semiconductor manufacturing processes , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[24]  Satoshi Yasuda,et al.  Prediction and Control of Transistor Threshold Voltage by Virtual Metrology (Virtual PCM) Using Equipment Data , 2013, IEEE Transactions on Semiconductor Manufacturing.