Timbre Technologies, Inc.

Tokyo Electron Limited

Kla Tencor Corporation

Joerg Bischoff

Xinhui Niu

Junwei Bao

Shifang Li

David Hetzer

Vi Vuong

Hanyou Chu

Manuel B Madriaga

Heiko Weichert

Nickhil Jakatdar

Sanjay K Yedur

Emmanuel Drege

Wen Jin

Weidong Yang

Peilin Jiang

Karl Hehl

A method for improving computation efficiency for diffraction signals in optical metrology is described. The method includes simulating a set of diffraction orders for a three-dimensional structure. The diffraction orders within the set of diffraction orders are then prioritized. The set of diffraction orders is truncated to provide a truncated set of diffraction orders based on the prioritizing. Finally, a simulated spectrum is provided based on the truncated set of diffraction orders.

Computation efficiency by diffraction order truncation

Provided are techniques for numerically integrating an intensity distribution function over a numerical aperture in a manner dependent on a determination of whether the numerical aperture spans a Rayleigh singularity. Where a singularity exists, Gaussian quadrature (cubature) is performed using a set of weights and points (nodes) that account for the effect of the Wood anomaly present within the aperture space. The numerical aperture may be divided into subregions separated by curves where the Wood anomaly condition is satisfied. Each subregion is then numerically integrated and a weighted sum of the subregion contributions is the estimate of the integral. Alternatively, generalized Gaussian quadrature (cubature) is performed where an analytical polynomial function which accounts for the effect of the Wood anomaly present within the aperture space is integrated. Points and nodes generated from a fit of the analytical polynomial function are then used for integration of the intensity distribution function.

Numerical aperture integration for optical critical dimension (OCD) metrology

Diffraction modeling of a diffracting structure employing at least two distinct differential equation solution methods. In an embodiment, a rigorous coupled wave (RCW) method and a coordinate transform (C) method are coupled with a same S-matrix algorithm to provide a model profile for a scatterometry measurement of a diffracting structure having unknown parameters. In an embodiment, a rigorous coupled wave (RCW) method and a coordinate transform (C) method generate a modeled angular spectrum of diffracted orders as a prediction for how a diffracting photolithographic mask images onto a substrate.

Hybrid diffraction modeling of diffracting structures

To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system.

Training a machine learning system to determine photoresist parameters

To generate a simulated diffraction signal, one or more values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, are obtained. One or more values of one or more profile parameters are derived using the one or more values of the one or more photoresist parameters. The one or more profile parameters characterize one or more geometric features of the structure. A simulated diffraction signal is generated using the one or more values of the one or more profile parameters. The simulated diffraction signal characterizes behavior of light diffracted from the structure. The generated simulated diffraction signal is associated with the one or more values of the one or more photoresist parameters. The generated simulated diffraction signal, the one or more values of the one or more photoresist parameters, and the association between the generated simulated diffraction signal and the one or more values of the one or more photoresist parameters are stored.

Determining photoresist parameters using optical metrology

Embodiments of an apparatus for deriving an iso-dense bias are generally described herein. Other embodiments may be described and claimed.

Apparatus for deriving an iso-dense bias

Eigensolutions for use in determining the profile of a structure formed on a semiconductor wafer can be approximated by obtaining a known set of eigenvectors associated with a first section of a hypothetical profile of the structure, where the known set of eigenvectors is used to generate a simulated diffraction signal for the hypothetical profile. A known characteristic matrix associated with a second section of a hypothetical profile is obtained, and an approximated set of eigenvalues for the second section is determined based on the known set of eigenvectors associated with the first section and the known characteristic matrix associated with the second section.

Approximating eigensolutions for use in determining the profile of a structure formed on a semiconductor wafer

An optical metrology model for a structure to be formed on a wafer is developed by characterizing a top-view profile and a cross-sectional view profile of the structure using profile parameters. The profile parameters of the top-view profile and the cross-sectional view profile are integrated together into the optical metrology model. The profile parameters of the optical metrology model are saved.

Optical metrology optimization for repetitive structures

Embodiments of controlling a fabrication process using an iso-dense bias are generally described herein. Other embodiments may be described and claimed.

Method of controlling a fabrication process using an iso-dense bias

The profile of a single feature formed on a wafer can be determined by obtaining an optical signature of the single feature using a beam of light focused on the single feature. The obtained optical signature can then be compared to a set of simulated optical signatures, where each simulated optical signature corresponds to a hypothetical profile of the single feature and is modeled based on the hypothetical profile.

Optical metrology of single features

To control a photolithography cluster using optical metrology, a structure is fabricated on a wafer using the photolithography cluster. A measured diffraction signal off the structure is obtained. The measured diffraction signal is compared to a simulated diffraction signal. The simulated diffraction signal is associated with one or more values of one or more photoresist parameters. The one or more photoresist parameters characterize behavior of photoresist when the photoresist undergoes processing steps in the photolithography cluster. The simulated diffraction signal was generated using one or more values of one or more profile parameters. The one or more values of the one or more profile parameters used to generate the simulated diffraction signal were derived from the one or more values of the one or more photoresist parameters associated with the simulated diffraction signal. If the measured diffraction signal and the simulated diffraction signal match, then one or more values of one or more photoresist parameters used in the photolithography cluster are determined to be the one or more values of the one or more photoresist parameters associated with the matching simulated diffraction signal. One or more process parameters or equipment settings of the photolithography cluster are adjusted based on the one or more values of the one or more photoresist parameters.

Automated process control using optical metrology and photoresist parameters

One or more simulated diffraction signals for use in determining the profile of a structure formed on a semiconductor wafer can be generated, where the profile varies in more than one dimension. Intermediate calculations are generated for variations in a hypothetical profile of the structure in a first dimension and a second dimension, where each intermediate calculation corresponds to a portion of the hypothetical profile of the structure. The generated intermediate calculations are then stored and used in generating one or more simulated diffraction signals for one or more hypothetical profiles of the structure.

Generating simulated diffraction signals for two-dimensional structures

A structure formed on a semiconductor wafer is examined by obtaining measurements of cross polarization components of diffraction beams, which were obtained from scanning an incident beam over a range of azimuth angles to obtain an azimuthal scan. A zero azimuth position is determined based on the azimuthal scan. The cross polarization components are zero at the zero azimuth position. A measured diffraction signal is obtained using an azimuth angle to be used in optical metrology of the structure. Misalignment of the azimuth angle is detected using the measured diffraction signal and the determined zero azimuth position.

Azimuthal scanning of a structure formed on a semiconductor wafer

The top-view profiles of repeating structures in a wafer are characterized and parameters to represent variations in the top-view profile of the repeating structures are selected. An optical metrology model is developed that includes the selected top-view profile parameters of the repeating structures. The optimized optical metrology model is used to generate simulated diffraction signals that are compared to measured diffraction signals.

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Illmenau, Germany

Joerg Bischoff