Abstract: A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. In particular, a method according to embodiments of the present invention includes calculating a set of predicted output values, and obtaining a prediction model based on a set of input parameters, the set of predicted output values, and empirical output values. Each input parameter causes a change in at least two outputs. The method also includes optimizing the prediction model by minimizing differences between the set of predicted output values and the empirical output values, and adjusting the set of input parameters to obtain a set of desired output values to control the manufacturing apparatus. Obtaining the prediction model includes transforming the set of input parameters into transformed input values using a transformation function of multiple coefficient values, and calculating the predicted output values using the transformed input values.

Abstract: A method and apparatus for diagnosing faults. A fault is detected. One or more process variables that contributed to the fault are determined. A relative contribution of each of the one or more process variables is determined. A determination is made as to which fault signatures match the fault, a match occurring when the relative contributions of the one or more process variables are within relative contribution ranges of the matching fault signature. Each fault signature is associated with at least one fault class.

Abstract: A method and apparatus for detecting faults. A set of data samples is received, the set of data samples including multiple process variables. One or more multivariate statistical models are adapted, wherein adapting includes applying a change to at least one univariate statistic of the one or more multivariate statistical models if the change is greater than a threshold value. The one or more multivariate statistical models are used to analyze subsequent process data to detect faults.

Abstract: A method of film deposition in a sub-atmospheric chemical vapor deposition (CVD) process includes (a) providing a model for sub-atmospheric CVD deposition of a film that identifies one or more film properties of the film and at least one deposition model variable that correlates with the one or more film properties; (b) depositing a film onto a wafer using a first deposition recipe comprising at least one deposition recipe parameter that corresponds to the at least one deposition variable; (c) measuring a film property of at least one of said one or more film properties for the deposited film of step (b); (d) calculating an updated deposition model based upon the measured film property of step (c) and the model of step (a); and (e) calculating an updated deposition recipe based upon the updated model of step (d) to maintain a target film property. The method can be used to provide feedback to a plurality of deposition chambers or to control a film property other than film thickness.

Abstract: A method of controlling surface non-uniformity of a wafer in a polishing operation includes (a) providing a model for a wafer polishing that defines a plurality of regions on a wafer and identifies a wafer material removal rate in a polishing step of a polishing process for each of the regions, wherein the polishing process comprises a plurality of polishing steps, (b) polishing a wafer using a first polishing recipe based upon an incoming wafer thickness profile, (c) determining a wafer thickness profile for the post-polished wafer of step (b), and (d) calculating an updated polishing recipe based upon the wafer thickness profile of step (c) and the model of step (a) to maintain a target wafer thickness profile. The model can information about the tool state to improve the model quality. The method can be used to provide feedback to a plurality of platen stations.

Abstract: A method for monitoring performance of an advanced process control system for at least one process output includes calculating a variance of a prediction error for a processing performance and/or a probability for violating specification limits of the processing performance of the at least one process output. If the variance of the prediction error is calculated, the method also includes calculating a model health index. If the probability for violating specification limits is calculated, the method further includes calculating a process health index.