Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: Methods and systems for determining one or more parameters of a wafer inspection process are provided. One method includes acquiring metrology data for a wafer generated by a wafer metrology system. The method also includes determining one or more parameters of a wafer inspection process for the wafer or another wafer based on the metrology data.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: Methods and systems enabling ultra-high resolution topography measurements of patterned wafers are disclosed. Measurements obtained utilizing the ultra-high resolution metrology may be utilized to improve wafer metrology measurement accuracies. Additionally, measurements obtained utilizing the ultra-high resolution metrology may also be utilized to provide feedback and/or calibration control to improve fabrication and design of wafers.

Abstract: Methods and systems enabling ultra-high resolution topography measurements of patterned wafers are disclosed. Measurements obtained utilizing the ultra-high resolution metrology may be utilized to improve wafer metrology measurement accuracies. Additionally, measurements obtained utilizing the ultra-high resolution metrology may also be utilized to provide feedback and/or calibration control to improve fabrication and design of wafers.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: Methods and systems for determining one or more parameters of a wafer inspection process are provided. One method includes acquiring metrology data for a wafer generated by a wafer metrology system. The method also includes determining one or more parameters of a wafer inspection process for the wafer or another wafer based on the metrology data.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.