AN ANALYSIS AND MIMO EXTENSION OF A DOUBLE EWMA RUN-TO-RUN CONTROLLER FOR NON-SQUARED SYSTEMS

2003 ◽  
Vol 10 (04) ◽  
pp. 417-428 ◽  
Author(s):  
RAMKUMAR RAJAGOPAL ◽  
ENRIQUE DEL CASTILLO
Keyword(s):  
Semiconductor Manufacturing ◽  
Control Method ◽  
Moving Average ◽  
Multiple Input Multiple Output ◽  
Processing Step ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Average Control ◽  
Controllable Factor ◽  
Weighted Moving Average

The double EWMA (exponentially weighted moving average) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. Until recently, the dEWMA controller had been applied only for the single controllable factor (or input), single quality charcteristic (or output) case. Recently, Del Castillo and Rajagopal4 propose a multivariate double EWMA controller for squared multiple-input, multiple-output (MIMO) processes, where there is an equal number of inputs and outputs. This paper extends the MIMO dEWMA controller for non-squared systems. Two different MIMO dEWMA controllers are presented and their performance studied with application to a Chemical-Mechanical Polishing (CMP) process, a critical semiconductor manufacture processing step that exhibits non-linear dynamics.

Statistical Monitoring Techniques for Contamination Data

1997 ◽  
Vol 40 (2) ◽  
pp. 23-30
Author(s):  
Douglas Montgomery ◽  
J. Keats ◽  
John Fowler ◽  
George Runger ◽  
Geetha Rajavelu
Keyword(s):  
Control Charts ◽  
Semiconductor Manufacturing ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Monitoring Techniques ◽  
Statistical Monitoring ◽  
Statistical Assumptions ◽  
Particle Contamination ◽  
Average Control ◽  
Weighted Moving Average

Monitoring of particle contamination by statistically based methods is used extensively in semiconductor manufacturing. Often Shewhart-type control charts are used for this purpose. These charts suffer from some potential disadvantages in this application environment. Specifically, they are slow to detect shifts or changes in the level of particle contamination and, in some situations, the underlying statistical assumptions of the charts are inappropriate for contamination data. We suggest the use of cumulative sum and exponentially weighted moving average control charts for monitoring particle contamination. The advantages of these charts for the types of particle contamination data typically encountered in semiconductor manufacturing are discussed and illustrated with examples.

Some Advances in Energy Recycling Semiactive Vibration Suppression

2008 ◽  
Vol 56 ◽  
pp. 345-354 ◽  
Author(s):  
Junjiro Onoda
Keyword(s):  
Power Supply ◽  
Vibration Suppression ◽  
Control Method ◽  
Multiple Input Multiple Output ◽  
Piezoelectric Transducers ◽  
Vibrating Structures ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Self Powered ◽  
Shunt Circuit

This paper summarizes some studies performed by the author's group on energy-recycling semiactive vibration suppression using piezoelectric transducers embedded in the vibrating structures and shunted on switched inductive circuits. Basic idea of this method is to suppress the vibration by controlling the switch in the shunt circuit, which was first introduced by Richard C., et al. This idea has been upgraded by introducing (1) a multiple-input-multiple-output (MIMO) control method for the switches in the shunt circuits, (2) a self-sensing method to estimate the state of structure from the voltage across the piezoelectric transducer, so that any additional sensors can be neglected, and (3) a self-powered shunt circuit that performs the semiactive vibration suppression without any power supply. Several numerical and experimental results showed that the method works well against transient, sinusoidal, and random multi-modal vibrations and suppresses the vibrations effectively. It was also shown that the method is very robust, and, with it, the system is always stable. Studies for various applications of this method are also discussed.

Advanced Process Control Using Partial Least Squares

2012 ◽  
Vol 542-543 ◽  
pp. 124-127
Author(s):  
Shu Kai S. Fan ◽  
Yuan Jung Chang
Keyword(s):  
Process Control ◽  
Least Squares ◽  
Partial Least Squares ◽  
Model Building ◽  
Moving Average ◽  
Multiple Input Multiple Output ◽  
Advanced Process Control ◽  
Virtual Metrology ◽  
Multiple Input ◽  
Input Multiple Output

This paper applies the partial least squares (PLS) technique to the multiple-input multiple-output (MIMO) semiconductor processes under the paradigm of the Advanced Process Control (APC). First, we present a controller called the PLS-MIMO double exponentially weighted moving average (PLS-MIMO DEWMA) controller. It uses the PLS method as the model building/estimation technique to help the EWMA controller to produce more consistent and robust control outputs than purely using the conventional EWMA controller. To cope with metrology delays, the proposed controller uses the pre-process metrology data to build up a Virtual Metrology (VM) system that can provide the estimated process outputs for the PLS-MIMO DEWMA controller. Finally, a Fault Detection (FD) system is added based upon the principal components of PLS, which supplies the process state for VM and the PLS-MIMO DEWMA controller to respond to the system errors.

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