Abstract: An apparatus for detecting a rare situation in a process described by a plurality of parameters, the apparatus comprising: a parameter value inputter, for inputting values of at least two interrelated parameters of the plurality of parameters, the interrelated parameters constituting at least one cluster, and a rare situation detector for detecting a rare situation according to an alert policy, the alert policy being based at least on an output value of an alert model, the alert model configured to provide the output value as a function of the input parameter values of parameters constituting the at least one cluster.

Abstract: A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. The method includes the steps of identifying one or more input parameters that cause a change in output characteristics, defining global nodes using estimated maximum and minimum values of the input parameters, and defining a mathematical equation that calculates a predicted output characteristic for each node. The method also includes the steps of receiving at least one empirical data point having one or more input parameter values and at least one empirical output value and adjusting the predicted output values at the nodes based on a difference between the at least one empirical output value and the predicted output characteristic calculated using the mathematical equation based on the one or more input parameter values.

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, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. The method includes the steps of identifying one or more input parameters that cause a change in output characteristics, defining global nodes using estimated maximum and minimum values of the input parameters, and defining a mathematical equation that calculates a predicted output characteristic for each node. The method also includes the steps of receiving at least one empirical data point having one or more input parameter values and at least one empirical output value and adjusting the predicted output values at the nodes based on a difference between the at least one empirical output value and the predicted output characteristic calculated using the mathematical equation based on the one or more input parameter values.

Abstract: A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. The method includes the steps of identifying one or more input parameters that cause a change in output characteristics, defining global nodes using estimated maximum and minimum values of the input parameters, and defining a mathematical equation that calculates a predicted output characteristic for each node. The method also includes the steps of receiving at least one empirical data point having one or more input parameter values and at least one empirical output value and adjusting the predicted output values at the nodes based on a difference between the at least one empirical output value and the predicted output characteristic calculated using the mathematical equation based on the one or more input parameter values.

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, 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 the step of identifying one or more input parameters. Each input parameter causes a change in at least two outputs. The method also includes the step of storing values of the identified inputs and corresponding empirical output values along with predicted output values. The predicted output values are calculated based on, in part, the values of the identified inputs. The method also includes the step of calculating a set of transform coefficients by minimizing a score equation that is a function of differences between one or more of the empirical output values and their corresponding predicted output values.

Abstract: A system, method and medium of controlling a semiconductor manufacturing tool using a feedback control mechanism. The feedback control mechanism includes features for receiving data points relating to an output of the tool. The data points include a current data point and at least one previous data point. The feedback control mechanism also includes features for determining whether the current data point is an erroneous outlier by comparing the current data point to a statistical representation of the at least one previous data point, and based on whether the at least one previous data point is an outlier. The feedback control mechanism further includes features for disregarding the current data point in calculating a feedback value of the feedback control mechanism if the current data point is determined as an erroneous outlier.

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 the step of identifying one or more input parameters. Each input parameter causes a change in at least two outputs. The method also includes the step of storing values of the identified inputs and corresponding empirical output values along with predicted output values. The predicted output values are calculated based on, in part, the values of the identified inputs. The method also includes the step of calculating a set of transform coefficients by minimizing a score equation that is a function of differences between one or more of the empirical output values and their corresponding predicted output values.

Abstract: A system, method and medium of controlling a semiconductor manufacturing tool using a feedback control mechanism. The feedback control mechanism includes features for receiving data points relating to an output of the tool. The data points include a current data point and at least one previous data point. The feedback control mechanism also includes features for determining whether the current data point is an erroneous outlier by comparing the current data point to a statistical representation of the at least one previous data point, and based on whether the at least one previous data point is an outlier. The feedback control mechanism further includes features for disregarding the current data point in calculating a feedback value of the feedback control mechanism if the current data point is determined as an erroneous outlier.

Abstract: A method, system, and medium of modeling and/or for controlling a manufacturing process is disclosed. The method includes the steps of identifying one or more input parameters that cause a change in output characteristics, defining global nodes using estimated maximum and minimum values of the input parameters, and defining a mathematical equation that calculates a predicted output characteristic for each node. The method also includes the steps of receiving at least one empirical data point having one or more input parameter values and at least one empirical output value and adjusting the predicted output values at the nodes based on a difference between the at least one empirical output value and the predicted output characteristic calculated using the mathematical equation based on the one or more input parameter values.

Abstract: Apparatus for control of a complex process, said process being described by a plurality of input variables, a plurality of intermediate variables and a plurality of output variables having relationships therebetween such that ones of said inputs and said intermediate variables effect respectively different output variables, each of said output variables having a target, said apparatus comprising an optimizer for finding an optimum value for respective ones of said input and intermediate variables to maximize a summed convergence of said output variables to said targets.

Abstract: Apparatus for control of a complex process, said process being described by a plurality of input variables, a plurality of intermediate variables and a plurality of output variables having relationships therebetween such that ones of said inputs and said intermediate variables effect respectively different output variables, each of said output variables having a target, said apparatus comprising an optimizer for finding an optimum value for respective ones of said input and intermediate variables to maximize a summed convergence of said output variables to said targets