GOALI: Processing, System Modeling and Process Control for Complicated Functional Data

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

This Grant Opportunity for Academic Liaison with Industry (GOALI) award provides funding for the development of methodology and algorithm for processing large-size functional data with sharp changes and nonstationary patterns from manufacturing systems. The developed methods will integrate wavelet-based signal-processing techniques and neural-networks oriented system-modeling procedures to develop an in-situ process control tool for manufacturing processes such as ultra-thin semiconductor-film deposition. The research will optimize a generic data-reduction objective function balancing data-compression goals and modeling accuracy requirements for various types of data analysis and decision-making use. The developed statistical process control algorithm aiming to study process deviates at selective wavelet parameters will be more effective in detecting local changes in monitoring complicated functional data. Experimenting the developed procedures on various testing data curves obtained from the literature by simulating different amount of data noises and signal-change patterns will provide insights of the strength and weakness of the proposed methods against extensions of existing work for data-reduction purposes. Our industry partner from PDF Solutions, Inc. will work with us in providing insights for tuning the researched methods in solving real-life problems and in testing the developed methods and tools in practical applications. If successful, the results of this research will lead to improvement in algorithms for in-situ process control and a locally focused neural network, and new development in analysis and modeling of large-size complicated functional data. The primary goal of this work is to provide a generic data-reduction tool with a wide range of applications in mining knowledge from massive and complex functional data. Getting the relevant knowledge will help practitioners identify process problems more efficiently and improve the quality of manufacturing systems more effectively. The experience learned in this interdisciplinary project will nourish our integrated education and research program for training the next generation of manufacturing enterprise system engineers.