Abstract: A method for determining a spatially varying process offset for a lithographic process, the spatially varying process offset (MTD) varying over a substrate subject to the lithographic process to form one or more structures thereon. The method includes obtaining a trained model (MOD), having been trained to predict first metrology data based on second metrology data, wherein the first metrology data (OV) is spatially varying metrology data which relates to a first type of measurement of the one or more structures being a measure of yield and the second metrology data (PB) is spatially varying metrology data which relates to a second type of measurement of the one or more structures and correlates with the first metrology data; and using the model to obtain the spatially varying process offset (MTD).

Abstract: A method to infer a current sampling scheme for one or more current substrates is provided, the method including: obtaining a first model trained to infer an optimal sampling scheme based on inputting context and/or pre-exposure data associated with one or more previous substrates, wherein the first model is trained in dependency of an outcome of a second model configured to discriminate between the inferred optimal sampling scheme and a pre-determined optimal sampling scheme; and using the obtained first model to infer the current sampling scheme based on inputting context and/or pre-exposure data associated with the one or more current substrate.

Abstract: Methods and apparatus for classifying semiconductor wafers. The method can include: sorting a set of semiconductor wafers, using a model, into a plurality of sub-sets based on parameter data corresponding to one or more parameters of the set of semiconductor wafers, wherein the parameter data for semiconductor wafers in a sub-set include one or more common characteristics; identifying one or more semiconductor wafers within a sub-set based on a probability of the one or more semiconductor wafers being correctly allocated to the sub-set; comparing the parameter data of the one or more identified semiconductor wafers to reference parameter data; and reconfiguring the model based on the comparison. The comparison is undertaken by a human to provide constraints for the model. The apparatus can be configured to undertake the method.

Abstract: A method for determining an inspection strategy for at least one substrate, the method including: quantifying, using a prediction model, a compliance metric value for a compliance metric relating to a prediction of compliance with a quality requirement based on one or both of pre-processing data associated with the substrate and any available post-processing data associated with the at least one substrate; and deciding on an inspection strategy for the at least one substrate, based on the compliance metric value, an expected cost associated with the inspection strategy and at least one objective value describing an expected value of the inspection strategy in terms of at least one objective relating to the prediction model.

Abstract: Apparatus and methods of configuring an imputer model for imputing a second parameter. The method includes inputting a first data set including values of a first parameter to the imputer model, and evaluating the imputer model to obtain a second data set including imputed values of the second parameter. The method further includes obtaining a third data set including measured values of a third parameter, wherein the third parameter is correlated to the second parameter; obtaining a prediction model configured to infer values of the third parameter based on inputting values of the second parameter; inputting the second data set to the prediction model, and evaluating the prediction model to obtain inferred values of the third parameter; and configuring the imputer model based on a comparison of the inferred values and the measured values of the third parameter.

Abstract: A method for determining an inspection strategy for at least one substrate, the method including: quantifying, using a prediction model, a compliance metric value for a compliance metric relating to a prediction of compliance with a quality requirement based on one or both of pre-processing data associated with the substrate and any available post-processing data associated with the at least one substrate; and deciding on an inspection strategy for the at least one substrate, based on the compliance metric value, an expected cost associated with the inspection strategy and at least one objective value describing an expected value of the inspection strategy in terms of at least one objective relating to the prediction model.

Abstract: A method of determining a contribution of a process feature to the performance of a process of patterning substrates. The method may include obtaining a first model trained on first process data and first performance data. One or more substrates may be identified based on a quality of prediction of the first model when applied to process data associated with the one or more substrates. A second model may be trained on second process data and second performance data associated with the identified one or more substrates. The second model may be used to determine the contribution of a process feature of the second process data to the second performance data associated with the one or more substrates.