Patent application number | Description | Published |
20090216348 | Dynamically updated predictive model - Data of at least one of past values and present values of a system is consolidated from a plurality of sources. Virtual data of future values of the system is generated by applying the acquired data to a predictive model. Additional acquired data is received. The virtual data is dynamically updated by applying the additional acquired data to the predictive model. | 08-27-2009 |
20090217183 | User interface with visualization of real and virtual data - First acquired data that represents past values of one or more parameters is displayed in a user interface through which a user can monitor, control and predict system operations. Second acquired data that represents present values of the one or more parameters is displayed in the user interface. Virtual data that represents predicted future values of the one or more parameters is displayed in the user interface, wherein the first acquired data, the second acquired data and the virtual data are presented with a unified visual appearance such that a relationship between the past values, present values and predicted future values is visually indicated. | 08-27-2009 |
20110166684 | YIELD PREDICTION FEEDBACK FOR CONTROLLING AN EQUIPMENT ENGINEERING SYSTEM - A yield prediction is received by a strategy engine. The strategy engine compares the end-of-line yield prediction to a plurality of rules. The strategy engine then instructs a component of an equipment engineering system to perform an action included in a rule that corresponds to the end-of-line yield prediction. | 07-07-2011 |
20110166688 | YIELD PREDICTION FEEDBACK FOR CONTROLLING AN EQUIPMENT ENGINEERING SYSTEM - A yield prediction is received by a run-to-run controller that includes an intra-process run-to-run control module that specifies process performance targets, wherein the yield prediction is associated with at least one of a manufacturing tool, a product or a process. The run-to-run control module adjusts first parameters associated with intra-process run-to-run control based on the yield prediction, wherein the first parameters include processing parameters of a process recipe. | 07-07-2011 |
20110190917 | METHOD AND APPARATUS FOR DEVELOPING, IMPROVING AND VERIFYING VIRTUAL METROLOGY MODELS IN A MANUFACTURING SYSTEM - A computing device develops a first non-adaptive virtual metrology (VM) model for a manufacturing process based on performing a non-adaptive regression using a first data set. Upon determining that an accuracy of the first non-adaptive VM model satisfies a first quality criterion, the computing device develops an adaptive VM model for the manufacturing process based on performing an adaptive regression using at least one of the first data set or a second data set. The computing device evaluates an accuracy of the adaptive VM model using a third data set that is larger than the first data set and the second data set. The computing device determines that the adaptive VM model is ready for use in production upon determining that an accuracy of the first adaptive VM model satisfies a second quality criterion that is more stringent than the first quality criterion. | 08-04-2011 |
20110202160 | METHODS AND APPARATUSES FOR UTILIZING ADAPTIVE PREDICTIVE ALGORITHMS AND DETERMINING WHEN TO USE THE ADAPTIVE PREDICTIVE ALGORITHMS FOR VIRTUAL METROLOGY - Described herein are methods, apparatuses, and systems for determining adaptive predictive algorithms for virtual metrology. In some embodiments, a computer implemented method identifies a plurality of predictive algorithms. The method determines when to use one or more of the plurality of predictive algorithms to predict one or more virtual metrology variables in a manufacturing facility. | 08-18-2011 |
20130268469 | INCREASING SIGNAL TO NOISE RATIO FOR CREATION OF GENERALIZED AND ROBUST PREDICTION MODELS - A computer system iteratively executes a decision tree-based prediction model using a set of input variables. The iterations create corresponding rankings of the input variables. The computer system generates overall variables contribution data using the rankings of the input variables and identifies key input variables based on the overall variables contribution data. | 10-10-2013 |
20140006338 | BIG DATA ANALYTICS SYSTEM | 01-02-2014 |