Kla Tencor Corporation

Kla Corporation

Kla-tenor Corp.

Ming Di

Qiang Zhao

Leonid Poslavsky

Dawei Hu

Torsten Rudolf Kaack

Natalia Malkova

Andrei V Shchegrov

Stilian Pandev

Alexander Kuznetsov

Xiang Gao

Zhengquan Tan

Houssam Chouaib

Liequan Lee

Aaron J Rosenberg

Tianhan Wang

John Lesoine

Manh Dang Nguyen

Philip D Flanner, Iii

Scott Penner

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 determining band structure characteristics of high-k dielectric films deposited over a substrate based on spectral response data are presented. High throughput spectrometers are utilized to quickly measure semiconductor wafers early in the manufacturing process. Optical models of semiconductor structures capable of accurate characterization of defects in high-K dielectric layers and embedded nanostructures are presented. In one example, the optical dispersion model includes a continuous Cody-Lorentz model having continuous first derivatives that is sensitive to a band gap of a layer of the unfinished, multi-layer semiconductor wafer. These models quickly and accurately represent experimental results in a physically meaningful manner. The model parameter values can be subsequently used to gain insight and control over a manufacturing process.

Dispersion model for band gap tracking

Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.

Calculated electrical performance metrics for process monitoring and yield management

The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.

Measurement of composition for thin films

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Hayward, CA, United States of America

Ming Di