SYSTEMS AND METHODS FOR GENERATING SEM-QUALITY METROLOGY DATA FROM OPTICAL METROLOGY DATA USING MACHINE LEARNING

摘要

In une embodiment, one or more non transitory, machine-readable medium has instructions thereon, the instructions when executed by a processor being configured to perform operations comprising obtaining scanning electron microscopy (SEM) metrology data for first areas on a training wafer, obtaining optical metrology data for second areas on the training wafer, and training a model, by using the SEM metrology data and the optical metrology data for the training wafer, to generate parameters for features on a production wafer based on optical metrology data for areas of the production wafer.In a further embodiment, further comprising obtaining optical metrology data for areas of the production wafer and determining feature parameters of the production water, based on the optical metrology data for the production wafer and the trained model.In a further embodiment, retraining the model based on detection of a retraining trigger. In a further embodiment, wherein detecting a wafer fabrication change comprises monitoring one or more performance indicators associated with wafer fabrication.In a further embodiment, wherein the feature parameters comprise at least one of overlay, overlay error, critical dimension, critical dimension uniformity, edge placement error, focus, dose, local overlay, local overlay error, local critical dimension, focal critical dimension uniformity, one or more feature dimensions, or a combination thereof.In one embodiment, one or more non-transitory, machine-readable medium has instructions thereon, the instructions when executed by a processor being configured to perform operations comprising obtaining optical metrology data for areas of a production wafer and determining parameters for features on the production wafer based on the optical metrology data and a trained model, wherein the parameters for features on the production wafer comprise pseudo-scanning-electron-microscopy (SEM) parameters.In a further embodiment, wherein the model is generated by obtaining (SEM) metrology data for first areas on one or more training wafers, obtaining optical metrology data for second areas on the one or more training wafers, and training the model, by using the SEM metrology data and the optical metrology data, to generate parameters for features on the production wafer. In one embodiment, a processor and one or more non-transitory, machine-readable medium to as described in the previous embodiments.In one embodiment, a method comprising obtaining scanning electron microscopy (SEM) metrology data for first areas on one or more training waters, obtaining optical metrology data for second areas on the one or more training wafers, and training a model, by using the SEM metrology data and the optical metrology data, to generate parameters for features on one or more production wafers based on optical metrology data for areas of the one or more production wafers. In a further embodiment, obtaining optical metrology data for areas of the one or more production wafers, and determining parameters for features on the one or more production wafers, based on the optical metrology data (or the one or more production wafers and the trained model. Other advantages of the embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings wherein are set forth, by way of illustration and example, certain embodiments of the present invention.