Lam Research Corporation

Kla Corporation

Nanometrics Inc.

Onto Innovation Inc.

Yan Zhang

Ye Feng

Andrew D Bailey, Iii

Saravanapriyan Sriraman

Mehmet Derya Tetiker

Marcus Musselman

Julien Mailfert

Daniel T Thompson

Pedro Vagos

Barry Blasenheim

Joseph A Di Regolo

Osman Sorkhabi

Robert Press

Huy Nguyen

A metrology system may include an optical metrology tool configured to produce an optical metrology output for one or more features on a processed substrate, and a metrology machine learning model that has been trained using a training set of (i) profiles, critical dimensions, and/or contours for a plurality of features, and (ii) optical metrology outputs for the plurality of features. The metrology machine learning model may be configured to: receive the optical metrology output from the optical metrology tool; and output the profile, critical dimension, and/or contour of the one or more features on the processed substrate.

Optical metrology in machine learning to characterize features

Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Method of etch model calibration using optical scatterometry

Methods and systems for measuring the orientation of a wafer at or near an X-ray scatterometry measurement location are described herein. In one aspect, an X-ray scatterometry based metrology system includes a wafer orientation measurement system that measures wafer orientation based on a single measurement without intervening stage moves. In some embodiments, an orientation measurement spot is coincident with an X-ray measurement spot. In some embodiments, an X-ray scatterometry measurement and a wafer orientation measurement are performed simultaneously. In another aspect, signals detected by a wafer orientation measurement system are filtered temporally, spatially, or both, to improve tracking. In another aspect, a wafer orientation measurement system is calibrated to identify the orientation of the wafer with respect to an incident X-ray beam. In another aspect, a wafer under measurement is positioned based on the measured orientation in a closed loop or open loop manner.

Measurement and control of wafer tilt for x-ray based metrology

Optical metrology is used to calibrate the plane-of-incidence (POI) azimuth error by determining and correcting an azimuth angle offset. The azimuth angle offset may be determined by measuring at least a partial Mueller matrix from a calibration grating on a sample held on a stage for a plurality of POI azimuth angles. An axis of symmetry is determined for a curve describing a value of a Mueller matrix element with respect to POI azimuth angle, for each desired wavelength and each desired Mueller matrix element. The axis of symmetry may then be used to determine the azimuth angle offset, e.g., by determining a mean, median or average of all, or a filtered subset, of the axes of symmetry. If desired, an axis of symmetry may be determined for data sets other than Mueller matrix elements, such as Fourier coefficients of measured signals.

Correction of angular error of plane-of-incidence azimuth of optical metrology device

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Fremont, CA, United States of America

Yan Zhang