DIFFERENTIAL METHODS AND APPARATUS FOR METROLOGY OF SEMICONDUCTOR TARGETS

• US 20150046118A1
• Filed: 08/06/2014
• Published: 02/12/2015
• Est. Priority Date: 08/11/2013
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Pandev, Stilian Ivanov, Shchegrov, Andrei V.
• Granted Patent: US 10,935,893 B2
• Time in Patent Office: Days
• US Class Current: 702/155
• CPC Class Codes: G01N 21/956 Inspecting patterns on the surface of objects contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G03F 7/70625 Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness
• CPC Class Codes: G03F 7/70641 Focus
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/30 Structural arrangements specially adapted for testing or measuring during manufacture or treatment, or specially adapted for reliability measurements

Abstract

Disclosed are apparatus and methods for determining process or structure parameters for semiconductor structures. A plurality of optical signals is acquired from one or more targets located in a plurality of fields on a semiconductor wafer. The fields are associated with different process parameters for fabricating the one or more targets, and the acquired optical signals contain information regarding a parameter of interest (POI) for a top structure and information regarding one or more underlayer parameters for one or more underlayers formed below such top structure. A feature extraction model is generated to extract a plurality of feature signals from such acquired optical signals so that the feature signals contain information for the POI and exclude information for the underlayer parameters. A POI value for each top structure of each field is determined based on the feature signals extracted by the feature extraction model.

Claims

1. A method of determining process or structure parameters for semiconductor structures, the method comprising:

acquiring a plurality of optical signals from one or more targets located in a plurality of fields on a semiconductor wafer, wherein the fields are associated with different process parameters for fabricating the one or more targets, wherein the acquired optical signals contain information regarding a parameter of interest (POI) for a top structure and information regarding one or more underlayer parameters for one or more underlayers formed below such top structure;

generating a feature extraction model to extract a plurality of feature signals from such acquired optical signals so that the feature signals contain information for the POI and exclude information for the underlayer parameters; and

determining a POI value for each top structure of each field based on the feature signals extracted by the feature extraction model.

2. The method of claim 1, wherein generating the feature extraction model includes performing a dataset reduction technique on the acquired optical signals to produce transformed optical signals data and generating the feature extraction model to extract the feature signals from the transformed optical signals data.

3. The method of claim 2, wherein the transformed optical signals data is a linear combination of the acquired optical signals.

4. The method of claim 2, wherein the dataset reduction technique is accomplished using a principal component analysis (PCA), kernel PCA (kPCA), non-lineary PCA (NLPCA), Independent Component Analysis (ICA), or Local Linear Embedding (LLE) algorithm.

5. The method of claim 2, wherein the dataset reduction technique is accomplished using a principal component analysis (PCA) technique and the transformed optical signals represent the acquired optical signals with respect to a first principal component resulting from the PCA technique.

6. The method of claim 1, wherein determining the POI is accomplished by training a parameter model to determine the POI value for each top structure based on the feature signals, the method further comprising:

acquiring a plurality of optical signals from a plurality of unknown structures on one or more subsequent wafers; and

using the feature extraction model and the parameter model to determine a plurality of POI values for the unknown structures.

7. The method of claim 1, wherein the acquired optical signals include a first set of acquired signals from one or more first targets in each field and a second set of acquired signals from one or more second target in each field, wherein the one or more first targets have a top layer structure and an underlayer structure and the one or more second targets have the same underlayer structure as the one or more first targets but excludes the top layer structure of the one or more first targets, wherein the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the residual signals determined for the first acquired signals being defined as the feature signals.

8. The method of claim 1, wherein the acquired optical signals include a first set of acquired signals from one or more first targets in each field and a second set of acquired signals from one or more second target in each field, wherein the one or more first targets have a top layer structure and an underlayer structure and the one or more second targets have the same top layer structure as the one or more first targets but a different underlayer than the one or more first targets, the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the function of the second acquired signals determined for the first acquired signals being defined as the feature signals.

9. The method of claim 1, wherein the acquired optical signals include a first set of acquired signals at a first azimuth angle from a particular target in each field and a second set of acquired signals at a second azimuth angle from the particular target in each field, wherein the particular target has an unpatterned underlayer portion over which the top structure is formed, wherein the first azimuth angle differs from the second azimuth angle, wherein the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the residual signals determined for the first acquired signals being defined as the feature signals.

10. The method of claim 9, wherein the first azimuth angle is zero and the second azimuth angle is 90°

.

11. The method of claim 1, wherein the acquired optical signals include a plurality of two-dimensional beam profile reflectometry (2DBPR) images from a particular target in each field, wherein the particular target has an unpatterned underlayer over which the top structure is formed, wherein the feature extraction model is a radial symmetric function that fits each 2DBPR image with a residual signal, the residual signals determined for the images being defined as the feature signals.

12. The method of claim 1, wherein the optical signals are acquired using one or more of the following:

spectroscopic ellipsometry, Mueller matrix spectroscopic ellipsometry, spectroscopic reflectometry, spectroscopic scatterometry, beam profile reflectometry, beam profile ellipsometry, a single wavelength, a single discrete wavelength range, or multiple discrete wavelength ranges.

13. A system for inspecting or measuring a specimen, comprising:

an illuminator for generating illumination;

illumination optics for directing the illumination towards one or more targets located in a plurality of fields on a semiconductor wafer, wherein the fields are associated with different process parameters for fabricating the one or more targets, wherein the acquired optical signals contain information regarding a parameter of interest (POI) for a top structure and information regarding one or more underlayer parameters for one or more underlayers formed below such top structure;

collection optics for directing a plurality of optical signals from the one or more targets located in the plurality of fields in response to the illumination to a detector system;

the detector system for acquiring the plurality of optical signals from the one or more targets from the plurality of fields in response to the illumination; and

a processor and memory configured for performing the following operations;

generating a feature extraction model to extract a plurality of feature signals from such acquired optical signals so that the feature signals contain information for the POI and exclude information for the underlayer parameters; and

determining a POI value for each top structure of each field based on the feature signals extracted by the feature extraction model.

14. The system of claim 13, wherein generating the feature extraction model includes performing a dataset reduction technique on the acquired optical signals to produce transformed optical signals data and generating the feature extraction model to extract the feature signals from the transformed optical signals data.

15. The system of claim 14, wherein the transformed optical signals data is a linear combination of the acquired optical signals.

16. The system of claim 14, wherein the dataset reduction technique is accomplished using a principal component analysis (PCA), kernel PCA (kPCA), non-lineary PCA (NLPCA), Independent Component Analysis (ICA), or Local Linear Embedding (LLE) algorithm.

17. The system of claim 14, wherein the dataset reduction technique is accomplished using a principal component analysis (PCA) technique and the transformed optical signals represent the acquired optical signals with respect to a first principal component resulting from the PCA technique.

18. The system of claim 13, wherein determining the POI is accomplished by training a parameter model to determine the POI value for each top structure based on the feature signals, wherein processor and memory are further configured for:

acquiring a plurality of optical signals from a plurality of unknown structures on one or more subsequent wafers; and

using the feature extraction model and the parameter model to determine a plurality of POI values for the unknown structures.

19. The system of claim 13, wherein the acquired optical signals include a first set of acquired signals from one or more first targets in each field and a second set of acquired signals from one or more second target in each field, wherein the one or more first targets have a top layer structure and an underlayer structure and the one or more second targets have the same underlayer structure as the one or more first targets but excludes the top layer structure of the one or more first targets, wherein the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the residual signals determined for the first acquired signals being defined as the feature signals.

20. The system of claim 13, wherein the acquired optical signals include a first set of acquired signals from one or more first targets in each field and a second set of acquired signals from one or more second target in each field, wherein the one or more first targets have a top layer structure and an underlayer structure and the one or more second targets have the same top layer structure as the one or more first targets but a different underlayer than the one or more first targets, the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the function of the second acquired signals determined for the first acquired signals being defined as the feature signals.

21. The system of claim 13, wherein the acquired optical signals include a first set of acquired signals at a first azimuth angle from a particular target in each field and a second set of acquired signals at a second azimuth angle from the particular target in each field, wherein the particular target has an unpatterned underlayer portion over which the top structure is formed, wherein the first azimuth angle differs from the second azimuth angle, wherein the feature extraction model predicts each of the first acquired signals as a function of the second acquired signals with a residual signal, the residual signals determined for the first acquired signals being defined as the feature signals.

22. The system of claim 21, wherein the first azimuth angle is zero and the second azimuth angle is 90°

.

23. The system of claim 13, wherein the acquired optical signals include a plurality of two-dimensional beam profile reflectometry (2DBPR) images from a particular target in each field, wherein the particular target has an unpatterned underlayer over which the top structure is formed, wherein the feature extraction model is a radial symmetric function that fits each 2DBPR image with a residual signal, the residual signals determined for the images being defined as the feature signals.

24. The system of claim 13, wherein the optical signals are acquired using one or more of the following:

spectroscopic ellipsometry, Mueller matrix spectroscopic ellipsometry, spectroscopic reflectometry, spectroscopic scatterometry, beam profile reflectometry, beam profile ellipsometry, a single wavelength, a single discrete wavelength range, or multiple discrete wavelength ranges.