Onto Innovation Inc.

Jingsheng Shi

Jie Li

Pedro Vagos

Yiliang Liu

Petar Žuvela

Wei Ming Chiew

Optical measurement of a sample that includes a structure-of-interest (SOI) optically coupled to an unknown structure is optically measured by extracting from the resulting spectral signal the spectral variation that is correlated to key parameters associated with the SOI and removing the spectral variation from unknown structure that is irrelevant to the key parameters. An offline process is used to generate a physics-based model from a number of calibration measurements using reconstructed spectral signals after removing the spectral variation from the spectral signals that is irrelevant to the key parameters. A machine learning model may be additionally generated using at least the spectral variations correlated to key parameters associated with the SOI. In an in-line process, a sample is measured by filtering the spectral signals from a sample to remove spectral effects from the unknown structure and using the physics-based model or using the trained machine learning model.

Mitigation of undesired spectral effects in optical metrology

Contamination of metrology data by non-target signals caused by the measurement spot being incident on target and non-target areas may be reduced or eliminated using local gradient based mixed modeling or a machine learning model. The local gradient based mixed modeling approach uses a mixed model based on a model of the target region and a term of the local gradient of measured signals, which may be determined from scan data. The machine learning approach obtains mixed metrology data from a plurality of locations with respect to the target. The mixed metrology data is a mixture of target signals from the target and non-target signals from a non-target area and is used by a trained machine learning model to determine target metrology data that includes target signals from the target without the non-target signals and is used to determine parameters of interest for the target.

Growing community of inventors

Singapore, Singapore

Jingsheng Shi