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 method, system and medium is provided for enabling improved control systems. An error, or deviation from a target result, is observed for example during manufacture of semiconductor chips. The error within standard deviation is caused by two components: a white noise component and a signal component (such as systematic errors). The white noise component is, e.g., random noise and therefore is relatively non-controllable. The systematic error component, in contrast, may be controlled by changing the control parameters. A ratio between the two components is calculated autoregressively. Based on the ratio and using the observed or measured error, the actual value of the error caused by the systematic component is calculated utilizing an autoregressive stochastic sequence. The actual value of the error is then used in determining when and how to change the control parameters.

Abstract: A method, system and medium are provided for enabling improved feedback and feedforward control. An error, or deviation from target result, is observed during manufacture of semi conductor chips. The error within standard deviation is caused by two components: a white noise component and a signal component (such as systematic errors). The white noise component is random noise and therefore is relatively non-controllable. The systematic error, in contrast, may be controlled by changing the control parameters. A ratio between the two components is calculated autoregressively. Based on the ratio and using the observed or measured error, the actual value of the error caused by the signal component is calculated utilizing an autoregressive stochastic sequence. The actual value of the error is then used in determining when and how to change the control parameters.

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.