Abstract: A method, controller, and system for controlling a manufacturing process (batch-type or continuous-type) with a multivariate model are described. Dependent variable data and manipulated variable data are received. Dependent variable data represents values of uncontrolled process parameters from a plurality of sensors. Manipulated variable data represents controlled or setpoint values of controllable process parameters of a plurality of process tools. A predicted operational value, multivariate statistic, or both are determined based on the received data, and operating parameters of the manufacturing process are determined based on the predicted score, multivariate statistic, or both.

Abstract: Described are computer-based methods and apparatuses, including computer program products, for monitoring, detecting, and quantifying chemical compounds in a sample. A sample measurement comprising a digitized spectroscopic profile is received. A multivariate multistage background model comprising a first model that models a first time effect, a second model that models a second time effect that is different than the first time effect, or both is calculated. A background corrected sample measurement based on the sample measurement and the multivariate multistage background model is generated. A multivariate multistage library search, fault detection, and quantification algorithm is executed to identify one or more primary chemicals in the background corrected sample measurement.

Abstract: A method for creating a new model of a manufacturing process according to a multivariate analysis including selecting a set of data representative of multidimensional data measured during a step or phase of a manufacturing process. The method also includes determining a set of model generation conditions based on the set of data and generating the new model specifying intervals for the multidimensional data measured during a future manufacturing process based on the set of model generation conditions.

Abstract: A method for monitoring a manufacturing tool features acquiring metrology data (“Step a”). Data is acquired for process variables for a first process step performed by the manufacturing tool (“Step b”). A mathematical model of the first process step based on the metrology data and the acquired data is created (“Step c”). Steps b and c are repeated for at least a second process step (“Step d”). An nth mathematical model is created based on the metrology data and the data for the process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”). A multivariate metric is calculated based on the top level model of step f and data from subsequent runs of the manufacturing tool. Service is performed if the metric satisfies a condition.

Abstract: A method and system for partitioning (clustering) large amounts of data in a relatively short processing time. The method involves providing a first data matrix and a second data matrix where each of the first and second data matrices includes one or more variables, and a plurality of data points. The method also involves determining a first score from the first data matrix using a partial least squares (PLS) analysis or orthogonal PLS (OPLS) analysis and partitioning the first and second data matrices (e.g., row-wise) into a first group and a second group based on the sorted first score, the variance of the first data matrix, and a variance of the first and second groups relative to the variances of the first and second data matrices.

Abstract: Described are computer-based methods and apparatuses, including computer program products, for monitoring, detecting, and quantifying chemical compounds in a sample. A sample measurement comprising a digitized spectroscopic profile is received. A multivariate multistage background model comprising a first model that models a first time effect, a second model that models a second time effect that is different than the first time effect, or both is calculated. A background corrected sample measurement based on the sample measurement and the multivariate multistage background model is generated. A multivariate multistage library search, fault detection, and quantification algorithm is executed to identify one or more primary chemicals in the background corrected sample measurement.

Abstract: A method for monitoring a manufacturing tool features acquiring metrology data (“Step a”). Data is acquired for process variables for a first process step performed by the manufacturing tool (“Step b”). A mathematical model of the first process step based on the metrology data and the acquired data is created (“Step c”). Steps b and c are repeated for at least a second process step (“Step d”). An nth mathematical model is created based on the metrology data and the data for the process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”). A multivariate metric is calculated based on the top level model of step f and data from subsequent runs of the manufacturing tool. Service is performed if the metric satisfies a condition.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps ('Step e“). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (”Step f').

Abstract: A method, controller, and system for controlling a manufacturing process (batch-type or continuous-type) with a multivariate model are described. Dependent variable data and manipulated variable data are received. Dependent variable data represents values of uncontrolled process parameters from a plurality of sensors. Manipulated variable data represents controlled or setpoint values of controllable process parameters of a plurality of process tools. A predicted operational value, multivariate statistic, or both are determined based on the received data, and operating parameters of the manufacturing process are determined based on the predicted score, multivariate statistic, or both.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps ('Step e“). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (”Step f').

Abstract: A method for creating a new model of a manufacturing process according to a multivariate analysis including selecting a set of data representative of multidimensional data measured during a step or phase of a manufacturing process. The method also includes determining a set of model generation conditions based on the set of data and generating the new model specifying intervals for the multidimensional data measured during a future manufacturing process based on the set of model generation conditions.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”).

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”).

Abstract: A system and method is provided for computerized sorting irregular objects. The method includes receiving a representative set of irregular objects comprising at least two types of user-specified qualities. The method also includes receiving at least two types of measured data for the representative set of irregular objects. The method also includes generating at least one of a PCA model or a PLS model based on at least two user-specified qualities of the irregular objects and the at least two types of measured data for the representative set of irregular objects, and sorting a second set of irregular objects based on the at least one of the PCA model or the PLS model.

Abstract: A method and system for partitioning (clustering) large amounts of data in a relatively short processing time. The method involves providing a first data matrix and a second data matrix where each of the first and second data matrices includes one or more variables, and a plurality of data points. The method also involves determining a first score from the first data matrix using a partial least squares (PLS) analysis or orthogonal PLS (OPLS) analysis and partitioning the first and second data matrices (e.g., row-wise) into a first group and a second group based on the sorted first score, the variance of the first data matrix, and a variance of the first and second groups relative to the variances of the first and second data matrices.

Abstract: A system and method is provided for predicting consumer behavior for selected products. The method includes providing a first matrix associated with N products evaluated by a plurality of consumers, providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts and correlating the first matrix to the second or/and the third matrix to produce a relationship model.