Kla Tencor Corporation

Kla Corporation

Liequan Lee

Houssam Chouaib

Leonid Poslavsky

Qiang Zhao

Aaron J Rosenberg

Andrei V Shchegrov

Alexander Kuznetsov

Torsten Rudolf Kaack

Yin Xu

Dawei Hu

Jonathan Iloreta

Phillip Atkins

Stilian Pandev

Fei Li

John Josef Hench

Thaddeus Gerard Dziura

Andrei Veldman

Zhengquan Tan

Antonio Arion Gellineau

Hong Qiu

Ming Di

Meng Cao

Manh Dang Nguyen

John Lesoine

Kaiwen Xu

Abhi Gunde

Tianhan Wang

Shankar Krishnan

Sangbong Park

Paul Aoyagi

Natalia Malkova

Mikhail M Sushchik

Sungchul Yoo

In-Kyo Kim

Tianrong Zhan

David C S Wu

Manh Nguyen

Emily Chiu

Qiang Wang

Qi Dai

Raphael Jean Michel Marie Getin

A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Bandgap measurements of patterned film stacks using spectroscopic metrology

A metrology system may receive a model for measuring one or more selected attributes of a target including features distributed in a selected pattern based on regression of spectroscopic scatterometry data from a scatterometry tool for a range of wavelengths. The metrology system may further generate a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include undesired diffraction orders. The metrology system may further direct the spectroscopic scatterometry tool to generate scatterometry data of one or more measurement targets including fabricated features distributed in the selected pattern. The metrology system may further measure the selected attributes for the one or more measurement targets based on regression of the scatterometry data of the one or more measurement targets to the model weighted by the weighting function.

Scatterometry modeling in the presence of undesired diffraction orders

Machine learning techniques are used to predict values of fixed parameters when given reference values of critical parameters. For example, a neural network can be trained based on one or more critical parameters and a low-dimensional real-valued vector associated with a spectrum, such as a spectroscopic ellipsometry spectrum or a specular reflectance spectrum. Another neural network can map the low-dimensional real-valued vector. When using two neural networks, one neural network can be trained to map the spectra to the low-dimensional real-valued vector. Another neural network can be trained to predict the fixed parameter based on the critical parameters and the low-dimensional real-valued vector from the other neural network.

Automatic optimization of measurement accuracy through advanced machine learning techniques

A parameterized geometric model of a structure can be determined based on spectra from a wafer metrology tool. The structure can have geometry-induced anisotropic effects. Dispersion parameters of the structure can be determined from the parameterized geometric model. This can enable metrology techniques to measure nanostructures that have geometries and relative positions with surrounding structures that induce non-negligible anisotropic effects. These techniques can be used to characterize process steps involving metal and semiconductor targets in semiconductor manufacturing of, for example, FinFETs or and gate-all-around field-effect transistors.

Measurement methodology of advanced nanostructures

A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an augmented-reality or virtual-reality (AR/VR) image of the model that shows a 3D shape of the model and provides the AR/VR image to an AR/VR viewing device for display.

Visualization of three-dimensional semiconductor structures

A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an image of the model that shows a 3D shape of the model and provides the image to a device for display.

Methods and systems for performing spectroscopic measurements of asymmetric features of semiconductor structures are presented herein. In one aspect, measurements are performed at two or more azimuth angles to ensure sensitivity to an arbitrarily oriented asymmetric feature. Spectra associated with one or more off-diagonal Mueller matrix elements sensitive to asymmetry are integrated over wavelength to determine one or more spectral response metrics. In some embodiments, the integration is performed over one or more wavelength sub-regions selected to increase signal to noise ratio. Values of parameters characterizing an asymmetric feature are determined based on the spectral response metrics and critical dimension parameters measured by traditional spectral matching based techniques.

Detection and measurement of dimensions of asymmetric structures

Systems and methods for providing efficient modeling and measurement of critical dimensions and/or overlay registrations of wafers are disclosed. Efficiency is improved in both spectral dimension and temporal dimension. In the spectral dimension, efficiency can be improved by allowing different numerical aperture (NA) models to be used for different wavelengths in electromagnetic calculations, effectively providing a balance between computation speed and accuracy. In the temporal dimension, different NA models may be used at different iterations/stages in the process, effectively improving the computation speed without sacrificing the quality of the final result.

Adaptive numerical aperture control method and system

A library expansion system, method, and computer program product for metrology are provided. In use, processing within a first multi-dimensional library is performed by a metrology system. During the processing within the first multi-dimensional library, a second multi-dimensional library is identified. The processing is then transitioned to the second multi-dimensional library. Further, processing within the second multi-dimensional library is performed by the metrology system.

Library expansion system, method, and computer program product for metrology

A system, method, and computer program product are provided for automatically determining a parameter causing an abnormal semiconductor metrology measurement. In use, an abnormal semiconductor metrology measurement measured from a fabricated semiconductor component is received. At least one parameter of the fabricated semiconductor component causing the abnormal semiconductor metrology measurement is then automatically determined by one or more hardware processors. In particular, the one or more hardware processors determine a subset of parameters of the fabricated semiconductor component as potential sources of the abnormal semiconductor metrology measurement, rank the parameters in the determined subset of parameters, select an Nth number of the parameters in the determined subset of parameters in accordance with the ranking, and then analyze each of the selected parameters to identify one or more of the selected parameters as the at least one parameter of the fabricated semiconductor component causing the abnormal semiconductor metrology measurement.

System, method, and computer program product for automatically determining a parameter causing an abnormal semiconductor metrology measurement

A metrology system, method, and computer program product that employ automatic transitioning between utilizing a library and utilizing regression for measurement processing are provided. In use, it is determined, by the metrology system, that a predetermined condition has been met. In response to determining that the predetermined condition has been met, the metrology system automatically transitions between utilizing a library and utilizing regression for measurement processing.

Metrology system, method, and computer program product employing automatic transitioning between utilizing a library and utilizing regression for measurement processing

Disclosed are apparatus and methods for characterizing a plurality of structures of interest on a semiconductor wafer. A plurality of models having varying combinations of floating and fixed critical parameters and corresponding simulated spectra is generated. Each model is generated to determine one or more critical parameters for unknown structures based on spectra collected from such unknown structures. It is determined which one of the models best correlates with each critical parameter based on reference data that includes a plurality of known values for each of a plurality of critical parameters and corresponding known spectra. For spectra obtained from an unknown structure using a metrology tool, different ones of the models are selected and used to determine different ones of the critical parameters of the unknown structure based on determining which one of the models best correlates with each critical parameter based on the reference data.

Multi-model metrology

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Fremont, CA, United States of America

Liequan Lee