Kla Corporation

Bindi M Nagda

John Josef Hench

Antonio Arion Gellineau

Boxue Chen

Christopher Liman

Rebecca Shen

Naga Venkata Lakshmi Sandeep Inampudi

William Mcgahan

Methods and systems for performing X-ray model based scatterometry measurements of semiconductor structures with reduced computational effort are described herein. More specifically, measured detector image data is transformed to diffraction order efficiency data. The measured diffraction order efficiency data is compared with a parameter-efficiency library including simulated diffraction order efficiency data and associated sets of specimen parameter values. One or more sets of specimen parameter values are selected as seed values for regression on the measured detector image data based on the fit between the measured and simulated diffraction order efficiency data. The seed values are provided as initial values of one or more parameters of interest for the first iteration of the regression. The seed values enable the image based regression to converge to the global minimum with a dramatically reduced number of iterations. Thus, accurate X-ray scatterometry measurements of complex semiconductor structures are realized with less computational effort.

Forward library based seeding for efficient X-ray scatterometry measurements

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

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Melbourne, FL, United States of America

Bindi M Nagda