Kla Corporation

Christopher Liman

Antonio Arion Gellineau

Andrei V Shchegrov

Hyowon Park

Thaddeus Gerard Dziura

Houssam Chouaib

Dimitry Sanko

Boxue Chen

Inkyo Kim

Daniel James Haxton

Bindi M Nagda

Jung Heon Song

Pavan Gurudath

Sungchul Yoo

Anderson Chou

Nakyoon Kim

Methods and systems for determining random variation in one or more structures on a specimen are provided. One method includes determining characteristic(s) of output generated by an output acquisition subsystem for structure(s) formed on a specimen and simulating the characteristic(s) of the output with initial parameter values for the structure(s). The method also includes determining parameter values of the structure(s) formed on the specimen as the initial parameter values that resulted in the simulated characteristic(s) that best match the determined characteristic(s). The determined parameter values are responsive to random variation in parameter(s) of the structure(s) on the specimen.

Optical and X-ray metrology methods for patterned semiconductor structures with randomness

Methods and systems for optimizing a semiconductor measurement recipe that is robust to variations of hardware modeling parameters and geometric modeling errors are described herein. Robust measurement recipe optimization minimizes a cost function including one or more regularization terms that constrain the process space, and thus, significantly reduces the computational effort required to optimize a measurement recipe. This reduces overall process time and improves wafer throughput. In some examples, optimization is performed based on measurement data associated with multiple instances of a semiconductor structure; each instance characterized a different value of one or more geometric parameters of interest. In some examples, the search for optimized measurement recipes is limited to the discrete set of measurement system parameter values associated with the available measurement data set. In this manner, the performance of a particular measurement recipe is validated using existing measurement data.

Methods and systems for regularizing the optimization of application specific semiconductor measurement system parameter settings

Methods and systems for monitoring the quality of a semiconductor measurement in a targeted manner are presented herein. Rather than relying on one or more general indices to determine overall measurement quality, one or more targeted measurement quality indicators are determined. Each targeted measurement quality indicator provides insight into whether a specific operational issue is adversely affecting measurement quality. In this manner, the one or more targeted measurement quality indicators not only highlight deficient measurements, but also provide insight into specific operational issues contributing to measurement deficiency. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from measurement data. In some embodiments, values of one or more targeted measurement quality indicators are determined based on features extracted from one or more indications of a comparison between measurement data and corresponding measurement data simulated by a trained measurement model.

Methods and systems for targeted monitoring of semiconductor measurement quality

Methods and systems for measuring a complex semiconductor structure based on measurement data before and after a critical process step are presented. In some embodiments, the measurement is based on x-ray scatterometry measurement data. In one aspect, a measurement is based on fitting combined measurement data to a simplified geometric model of the measured structure. In some embodiments, the combined measurement data is determined by subtraction of a measured diffraction pattern before the critical process step from a measured diffraction pattern after the critical process step. In some embodiments, the simplified geometric model includes only the features affected by the critical process step. In another aspect, a measurement is based on a combined data set and a trained signal response metrology (SRM) model. In another aspect, a measurement is based on actual measurement data after the critical process step and simulated measurement data before the critical process step.

Methods and systems for combining x-ray metrology data sets to improve parameter estimation

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Milpitas, CA, United States of America

Christopher Liman