Barry M. Wise

The process chemometrics approach to process monitoring and fault detection

Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. This article reviews the chemometrics approach to chemical process monitoring and fault detection. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data. New process data can then be compared with models of normal operation in order to detect a change in the system. Typical modelling approaches rely on principal components analysis, partial least squares and a variety of other chemometric methods. Applications where the ordered nature of the data is taken into account explicitly are also beginning to see use. This article reviews the state-of-the-art of process chemometrics and current trends in research and applications.

Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness through Model Updating

Abstract Multivariate Statistical Process Control tools have been developed for monitoring and fault detection on a Lam 9600 Metal Etcher. Application of these methods is complicated because the process data exhibits large amounts of normal variation that is continuous on some time scales and discontinuous on others. Variations due to faults can be minor in comparison. Several models based on principal components analysis and variants which incorporate methods for model updating have been tested for long term robustness and sensitivity to known faults. Model performance was assessed with about six month’s worth of process data and a set of benchmark fault detection problems.

Hydrogen Bond Acidic Polymers for Surface Acoustic Wave Vapor Sensors and Arrays

Four hydrogen bond acidic polymers are examined as sorbent layers on acoustic wave devices for the detection of basic vapors. A polysiloxane polymer with pendant hexafluoro-2-propanol groups and polymers with hexafluorobisphenol groups linked by oligosiloxane spacers yield sensors that respond more rapidly and with greater sensitivity than fluoropolyol, a material used in previous SAW sensor studies. Sensors coated with the new materials all reach 90% of full response within 6 s of the first indication of a response. Unsupervised learning techniques applied to pattern-normalized sensor array data were used to examine the spread of vapor data in feature space when the array does or does not contain hydrogen bond acidic polymers. The radial distance in degrees between pattern-normalized data points was utilized to obtain quantifiable distances that could be compared as the number and chemical diversity of the polymers in the array were varied. The hydrogen bond acidic polymers significantly increase the distances between basic vapors and nonpolar vapors when included in the array.

Application of multi-way principal components analysis to nuclear waste storage tank monitoring

Multivariate statistical process control (MSPC) techniques for batch processes have been extended to monitoring a semi-batch process by focussing on periodic process set point changes. This procedure can be extended to continuous processes that have repeated upsets or perturbations. The MSPC technique was demonstrated for a nuclear waste storage tank that undergoes periodic agitation from a mixing pump. The procedure described here used multi-way principal components analysis to develop a statistical model of the process based on historical data. The model can be used to determine if changes have occurred in the system. At present this procedure is used off-line for monitoring but it could be implemented on-line.

The Process Chemometrics Approach to Process Monitoring and Fault Detection

Abstract Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. This article reviews the chemometrics approach to chemical process monitoring and fault detection. These approaches rely on the formation of a mathematical/statistical model that is based on historical process data. Process data can then be compared with models of normal operation in order to detect a change in the system. Typical modeling approaches rely on principal components analysis, partial least squares and a variety of other chemometric methods. Applications where the ordered nature of the data is taken into account explicitly are also beginning to see use. This article reviews the state-of-the art of process chemometrics and current trends in research and applications.

A comparison of neural networks, non-linear biased regression and a genetic algorithm for dynamic model identification

Abstract A variety of non-linear modeling techniques were applied to a single input/single output dynamic model identification problem. Results of the tests show that the prediction error of an artificial neural network with direct linear feed through terms is nearly as good or better than the other methods when tested on new data. However, non-linear models with nearly equal and occasionally better performance can be developed (including the selection of the model form and order) with a genetic algorithm (GA) in far less computer time. The GA derived models have the additional advantage of being more parsimonious and can be reparameterized, if need be, extremely rapidly. The non-linear biased regression techniques tested typically had larger, though possibly acceptable, prediction errors. These model structures offer the advantage of low computational requirements and reproducibility, i.e. the same model is produced each time for a given data set.

Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor ETCH Process: Impact of Measurement Selection and Data Treatment on Sensitivity

Abstract Multivariate Statistical Process Control (MSPC) tools have been developed for monitoring a Lam 9600 TCP Metal Etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibits a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved Optical Emission Spectroscopy (OES) of the plasma and a Radio Frequency Monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems will be presented and the strengths and weaknesses of the methods will be discussed, along with the relative advantages of each of the sensor systems.

A non-linear dynamic model identification challenge problem

Abstract A non-linear dynamic model identification challenge problem is proposed. The problem is to identify a dynamic model from single input single output data from a laboratory surge tank system that was built expressly for generating non-linear input output data. The problem is divided into six separate tests that challenge the ability of the modeling method to produce a model that interpolates well, degrades gracefully when extrapolating and is robust to noise in the identification data. Instructions are given for the development and testing of the models and for reporting the results. It is hoped that this problem will lead to a more constructive comparison of non-linear modeling methods than has been seen to date.