Kla Corporation

Kla Tencor Corporation

Houssam Chouaib

Zhengquan Tan

Aaron J Rosenberg

Liequan Lee

Dawei Hu

Alexander Kuznetsov

Andrei V Shchegrov

Thaddeus Gerard Dziura

Qiang Zhao

Derrick A Shaughnessy

Stilian Pandev

John Josef Hench

Andrei Veldman

Antonio Arion Gellineau

Torsten Rudolf Kaack

Yin Xu

Ming Di

Chao Chang

Jonathan Iloreta

Manh Dang Nguyen

Tianhan Wang

John Lesoine

Kaiwen Xu

Zhaxylyk Kudyshev

Abhi Gunde

Kai-Hsiang Lin

Lu Yu

Jianou Shi

Nanchang Zhu

Paul Aoyagi

Natalia Malkova

Dimitry Sanko

Mikhail M Sushchik

Jianli Cui

Boxue Chen

Christopher Liman

Inkyo Kim

Martin Edward Murtagh

Patrick Vincent Kelly

Phillip Atkins

Hyowon Park

Daniel James Haxton

Yaolei Zheng

Jin An

Manh Nguyen

Vincent Guénebaut

HaoMiao Chang

Andrew Lagodzinski

Teng Gu

Tianrong Zhan

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

A system may include a controller including processors configured to execute program instructions causing the processors to implement a measurement recipe by: generating a transformation model for transforming full loop optical measurement data to short loop optical measurement data, wherein the short loop optical measurement data includes optical measurement data of periodic memory array structures, wherein the full loop optical measurement data includes optical measurement data of complementary metal-oxide-semiconductor (CMOS) under array (CuA) devices, the CuA devices including CMOS structures beneath duplicates of the periodic memory array structures; generating a measurement model for determining measurements of the CuA devices; receiving full loop optical measurement data for CuA devices test samples; converting the full loop optical measurement data to short loop optical measurement data using the transformation model; and determining values of the measurements of the periodic memory array structures on the test samples using the measurement model.

Metrology in the presence of CMOS under array (CuA) structures utilizing machine learning and physical modeling

An inspection system includes a controller including a memory maintaining program instructions and one or more processors configured to execute the program instructions. The program instructions cause the one or more processors to generate a geometric model of a structure of a sample, generate an optical response function model of the structure of the sample to illumination based at least in part on the geometric model, receive measured data from a detector, generate a parametric sub-structure model based on at least the optical response function model and the measured data, and extract one or more parameters of the structure based on the measured data.

Metrology of nanosheet surface roughness and profile

A system, may include a controller configured to cause the processors to implement a measurement recipe by: receiving optical measurement data for training samples after a first process step for fabricating complementary metal-oxide-semiconductor (CMOS) under array (CuA) devices, wherein the CuA devices include first structures with a non-uniform spatial distribution; classifying the first structures into spatially-continuous regions based on unsupervised clustering; receiving optical measurement data for the training samples after a second process step, wherein the CuA devices after the second process step include periodic second structures above the first structures; developing effective medium models for the first structures; developing measurement models for determining measurements of the CuA devices; receiving optical measurement data for test samples after the second process step; and generating values of the metrology measurements of the second structures based on the optical measurement data for the test samples and the measurement models.

Metrology in the presence of CMOS under array (CUA) structures utilizing an effective medium model with classification of CUA structures

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

Methods and systems for measuring optical properties of transistor channel structures and linking the optical properties to the state of strain are presented herein. Optical scatterometry measurements of strain are performed on metrology targets that closely mimic partially manufactured, real device structures. In one aspect, optical scatterometry is employed to measure uniaxial strain in a semiconductor channel based on differences in measured spectra along and across the semiconductor channel. In a further aspect, the effect of strain on measured spectra is decorrelated from other contributors, such as the geometry and material properties of structures captured in the measurement. In another aspect, measurements are performed on a metrology target pair including a strained metrology target and a corresponding unstrained metrology target to resolve the geometry of the metrology target under measurement and to provide a reference for the estimation of the absolute value of strain.

Scatterometry based methods and systems for measurement of strain in semiconductor structures

Methods and systems disclosed herein can measure thin film stacks, such as film on grating and bandgap on grating in semiconductors. For example, the thin film stack may be a 1D film stack, a 2D film on grating, or a 3D film on grating. One or more effective medium dispersion models are created for the film stack. Each effective medium dispersion model can substitute for one or more layers. A thickness of one or more layers can be determined using the effective medium dispersion based scatterometry model. In an instance, three effective medium dispersion based scatterometry models are developed and used to determine thickness of three layers in a film stack.

Measuring thin films on grating and bandgap on grating

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

Methods and systems for generating measurement models of nanowire based semiconductor structures based on re-useable, parametric models are presented herein. Metrology systems employing these models are configured to measure structural and material characteristics (e.g., material composition, dimensional characteristics of structures and films, etc.) associated with nanowire semiconductor fabrication processes. The re-useable, parametric models of nanowire based semiconductor structures enable measurement model generation that is substantially simpler, less error prone, and more accurate. As a result, time to useful measurement results is significantly reduced, particularly when modelling complex, nanowire based structures. The re-useable, parametric models of nanowire based semiconductor structures are useful for generating measurement models for both optical metrology and x-ray metrology, including soft x-ray metrology and hard x-ray metrology.

Measurement models of nanowire semiconductor structures based on re-useable sub-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 optical, model based measurements of a small sized semiconductor structure employing an anisotropic characterization of the optical dispersion properties of one or more materials comprising the structure under measurement are presented herein. This reduces correlations among geometric parameters and results in improved measurement sensitivity, improved measurement accuracy, and enhanced measurement contrast among multiple materials under measurement. In a further aspect, an element of a multidimensional tensor describing the dielectric permittivity of the materials comprising the structure is modelled differently from another element. In a further aspect, model based measurements are performed based on measurement data collected from two or more measurement subsystems combined with an anisotropic characterization of the optical dispersion of the materials under measurement. In another aspect, the characterization of the optical dispersion of one or more materials comprising the structure under measurement depends on the geometry of the structure.

Model based optical measurements of semiconductor structures with anisotropic dielectric permittivity

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Houssam Chouaib