Abstract: A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, the training data measured from multiple wafers and including multiple pairs of corresponding input and label datasets obtained from each respective wafer. The input dataset of each pair includes multiple scatterometric datasets, measured at multiple respective locations defined by a first map. The label dataset of each pair includes one or more critical dimension (CD) parameters of respective locations defined by a second map, the second map including at least one location not in the first map. The OCD ML model is then applied to a new set of scatterometric datasets, measured from locations of a new wafer, according to the first map, to generate predicted CD parameters of locations of the second map on the new wafer.

Abstract: Controlling semiconductor device manufacture by acquiring training scatterometric signatures collected at training locations on training semiconductor wafers and corresponding to locations within a predefined design of a training semiconductor device, the training signatures collected after predefined processing steps during manufacture of the device on the training wafers, acquiring manufacturing outcome data associated with the training locations, training a prediction model using the training signatures and the manufacturing outcome data, and applying the prediction model to a candidate scatterometric signature to predict a manufacturing outcome, the candidate signature collected at a candidate location on a candidate semiconductor wafer, the candidate location corresponding to a location within the same predefined design of a candidate semiconductor device, the candidate signature collected after any of the processing steps during manufacture of the candidate device on the candidate wafer.

Abstract: Controlling semiconductor device manufacture by acquiring training scatterometric signatures collected at training locations on training semiconductor wafers and corresponding to locations within a predefined design of a training semiconductor device, the training signatures collected after predefined processing steps during manufacture of the device on the training wafers, acquiring manufacturing outcome data associated with the training locations, training a prediction model using the training signatures and the manufacturing outcome data, and applying the prediction model to a candidate scatterometric signature to predict a manufacturing outcome, the candidate signature collected at a candidate location on a candidate semiconductor wafer, the candidate location corresponding to a location within the same predefined design of a candidate semiconductor device, the candidate signature collected after any of the processing steps during manufacture of the candidate device on the candidate wafer.