Statistical process adjustment problems in short-run manufacturing.

摘要

Manufacturing engineers often change or adjust the operating conditions of a production process by manipulating a set of variables, or controllable factors. The goal is usually to keep some other variables of interest, the responses, close to given target values in the presence of uncontrollable variables, the noise factors and the disturbances, that also affect the responses. The performance of a process adjustment technique, which indicates how to change the controllable factors of a process, depends on the amount of information available about the relation between the controllable factors, or inputs, the noise factors and disturbances, and the responses, or outputs. This information usually results in an input-output, or transfer function, model. This dissertation considers problems related to the identification of these models, and proposes new process adjustment techniques when the amount of information available is limited (i.e. process runs are short) and noise factors are present in a process.; A specific problem addressed in this dissertation is how to identify the input-output model of a process that is being adjusted. Such a closed-loop identification method is necessary in industrial processes that cannot be left to run without control. Traditional system identification techniques assume open-loop (no control) operation.; The first part of this dissertation presents new methodology for the identification of Box-Jenkins transfer function models under closed-loop operating conditions. It is shown how the input-output delay of the process represents crucial information and that, if known a priori, it would facilitate the identification of the rest of the model. Hence, new methods for the specific estimation of the input-output delay, while the process operates in closed-loop, are proposed. The methods are based on Time Series change-point detection techniques.; In the second part of the dissertation we study a system identification problem frequently found in semiconductor manufacturing. This is the so-called context-based model identification problem, where different process models need to be identified for different batches of products depending on the manufacturing context under which the process data was obtained (e.g. the product type, operation, chamber, tool etc.). A model identification method that uses categorical variable selection methods is developed and applied to a real semiconductor manufacturing data set.; The last part of the thesis presents new adjustment methods for processes that involve uncontrollable noise factors. In the statistical process optimization literature, Robust Parameter Design (RPD) methods have been used for designing processes that are insensitive against variation caused by noise factors. These methods, however, are largely applied off-line; that is, they are not process adjustment methods that recommend different controllable factor settings depending on the on-line noise factors measured during production. Instead, they determine the optimal process settings before production starts and they do not alter the optimal settings during production. In this research, new Bayesian process adjustment methods for on-line robust parameter design are proposed. The proposed on-line RPD controllers are feedforward multiple response controllers that utilize on-line noise factor measurements, assumed available.