DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities:
[0008] FIG. 2D is a schematic view of schematic view of generating OCD based on updated optical transformation model, in accordance with some embodiments of the present disclosure. – Consider removing repeated words.
[00014] FIG. 4D is a schematic view of schematic view of generating OCD based on updated optical transformation model, in accordance with some embodiments of the present disclosure. – Consider removing repeated words.
[00018] FIG. 5D is a schematic view of schematic view of generating OCD based on updated optical transformation model, in accordance with some embodiments of the present disclosure. – Consider removing repeated words.
[00036] In some embodiments, the at least one physical parameter associated with the first semiconductor structure includes a wafer radius, a wavelength or a process recipe associated with the first semiconductor structure, and the transformer F20 receives the first structure parameters 212 and outputted(s) the updated first structure parameters 212 based on the at least one physical parameter. – Tenses are mixed; consider replacing the bold letters with the one in parenthesis.
[00036] … For example, when the physical parameter is a wafer radius of a wafer where the first semiconductor structure is in, whether the location of the first semiconductor structure is near the center of the wafer affects a width (i.e., the first structure parameters 212) of an opening of the first semiconductor structure. – This sentence is not clear as to what it is trying to communicate! Consider revising.
[00044] …In some embodiments, the transformer F40 is a pre-determined function for receiving the first structure parameter sets 412 and outputted(ing) the updated first structure parameter sets 412. – Tenses are mixed; consider replacing the bold letters with the ones in parenthesis.
[00049] In some embodiments, the at least one physical parameter associated with the first semiconductor structure includes a wafer radius, a wavelength or a process recipe associated with the first semiconductor structure, and the transformer F40 receives the first structure parameter sets 412 and outputted(s) the updated first structure parameter sets 412 based on the at least one physical parameter. – Tenses are mixed; consider replacing the bold letters with the one in parenthesis.
[00052] … The first structure parameter is associated with the first semiconductor structure after a first process, the second structure parameter is associated with the first semiconductor structure between the first process and a second process, and the third structure parameter is associated with the first semiconductor structure before the second process. – After a first process means it happens between the first process and a second process; before the second process means that it happens between the first process and a second process. In essence there is only one structure parameter and not three. Please explain.
[00055] … In some embodiments, the transformer F50 is a pre-determined function for receiving the first structure parameter sets 512 and outputted(ing) the updated first structure parameter sets 512. – Tenses are mixed; consider replacing the bold letters with the ones in parenthesis.
[00060] In some embodiments, the at least one physical parameter associated with the first semiconductor structure includes a wafer radius, a wavelength or a process recipe associated with the first semiconductor structure, and the transformer F50 receives the first structure parameter sets 512 and outputted(s) the updated first structure parameter sets 512 based on the at least one physical parameter. – Tenses are mixed; consider replacing the bold letters with the one in parenthesis.
[00060] … For example, when the physical parameter is a wafer radius of a wafer where the first semiconductor structure is in, whether the location of the first semiconductor structure is near the center of the wafer affects a width (i.e., the structure parameter, which is associated with the present structure, of the first structure parameter set 512) of an opening of the first semiconductor structure. – This sentence is not clear as to what it is trying to communicate! Consider revising.
[00066] … The transformer is a pre-determined function for receiving the first structure parameters and outputted(ing) the updated first structure parameters, and the at least one physical parameter includes a wafer radius, a wavelength or a process recipe. – Tenses are mixed; consider replacing the bold letters with the ones in parenthesis.
[00071] … The transformer is a pre-determined function for receiving the first structure parameter sets and outputted(ing) the updated first structure parameter sets, and the at least one physical parameter includes a wafer radius, a wavelength or a process recipe. – Tenses are mixed; consider replacing the bold letters with the ones in parenthesis.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Stilian Ivanov Pandev et. al. (US 20130262044 A1) herein after Pandev.
Regarding claim 1
Pandev discloses
A method, comprising:
(Pandev, p. 2, [0011] “FIG. 1A is a flowchart illustrating representative operations in a method of determining a new parameterization, in accordance with an embodiment of the present invention.”)
generating a plurality of first structure parameters corresponding to a plurality of first spectra associated with a first semiconductor structure based on a first optical transformation model
(Pandev, p. 1, [0008] “In an embodiment, a method of optimizing parametric models for structural analysis using metrology of repeating structures on a semiconductor substrate or wafer includes determining a first model of a structure. The first model is based on a first set of parameters.”)
(Pandev, p. 1, [0010] “… Also included is an optical metrology system configured to determine the one or more process parameters of the wafer application. The optical metrology system includes a beam source and detector configured to measure a diffraction signal of the structure.”)
updating the first structure parameters based on at least one physical parameter associated with the first semiconductor structure
(Pandev, p. 1, [0008] “… A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
(Pandev, p. 10, Claim 5. “The method of claim 1, wherein modifying the first model of the structure to provide the second model of the structure comprises reparametrizing geometric parameters or material parameters, or both, to provide the second set of parameters.”)
and establishing a second optical transformation model according to the updated first structure parameters and the corresponding first spectra.
(Pandev, p. 1, [0008] “…The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
Regarding claim 2
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, further comprising:
establishing the first optical transformation model according to a plurality of data, wherein each data includes a structure parameter and a corresponding spectrum.
(Pandev, p. 8, [0091] “… In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information. Such training may be computationally intensive and/or may have to be repeated for different models and/or profile parameter domains.”)
Regarding claim 3
Pandev teaches all features of claim 2, as disclosed above and further discloses
The method of claim 2, wherein the first optical transformation model and the second optical transformation model are established based on a machine learning scheme.
(Pandev, p. 5, [0058] “Referring to operation 1002 of flowchart 1000, a library or trained machine learning systems (MLS) is developed to extract parameters from a set of measured diffraction signals. In operation 1004, at least one parameter of a structure is determined using the library or the trained MLS.”)
(Pandev, p. 8, [0091] “…In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information. Such training may be computationally intensive and/or may have to be repeated for different models and/or profile parameter domains.”)
Regarding claim 4
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, wherein the step of updating the first structure parameters based on the at least one physical parameter associated with the first semiconductor structure further comprises:
updating the first structure parameters by a transformer based on the at least one physical parameter associated with the first semiconductor structure.
(Pandev, p. 10, Claim 5. “The method of claim 1, wherein modifying the first model of the structure to provide the second model of the structure comprises reparametrizing geometric parameters or material parameters, or both, to provide the second set of parameters.”)
Regarding claim 5
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, further comprising:
generating a plurality of second structure parameters corresponding to a plurality of second spectra associated with a second semiconductor structure based on the second optical transformation model.
(Pandev, p. 2, [0026] “FIG. 14 is a first architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0027] “FIG. 15 is a second architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 7, [0077] “… The signals collected in these measurements may be analyzed to determine parameters of structures on a semiconductor wafer in accordance with embodiments of the present invention.”)
Regarding claim 6
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, wherein each first structure parameter includes a dimension.
(Pandev, p. 1, [0010] “… One or more process parameters characterize behavior of structure shape or layer thickness when the structure undergoes processing operations in the wafer application performed using the fabrication cluster.”)
(Pandev, p. 3, [0037] “… A global model of the parameterization is then generated as a function of geometric parameters.”)
(Pandev, p. 3, [0038] “… A model is generated for each coefficient in the parameterization equations, where each coefficient is as a function of the geometric parameters.”)
(Pandev, p. 4, [0053] “… Referring to FIG. 8, since there are finite possible outcomes with respect to fabricating structure 700, the model 800 focuses on a subset of parameters. As a specific, but non-limiting example, structure height (HT) 802, structure width (804), top critical dimension (TCD) 806 and bottom critical dimension (BCD) 808 are shown as possible parameters that may be analyzed in a modeling process.”)
Regarding claim 7
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, wherein the at least one physical parameter includes a wafer radius, a wavelength or a process recipe.
(Pandev, p. 2, [0036] “… Referring to flowchart 100, at operation 102, a sensitivity analysis of spectra is performed with respect to the model parameters. At operations 104 and 106, the spectral information is used to determine a new parameterization that minimizes the parameter correlation by transformation. In general, these operations involve working with signals measured as a function of wavelength, polarization, angle, and state of coherence, etc.”)
(Pandev, p. 2, [0031] “FIG. 19 is an illustrative flowchart representing operations in a method for building a real-time regression measurement recipe for making production measurements of a structure, in accordance with an embodiment of this invention.”)
Regarding claim 8
Pandev teaches all features of claim 1, as disclosed above and further discloses
The method of claim 1, further comprising: measuring the first semiconductor structure by an optical measurement device for obtaining the first spectra.
(Pandev, p. 6, [0072] “… The optical metrology system 1400 may utilize a reflectometer, an ellipsometer, or other optical metrology device to measure the diffraction beam or signal.”)
(Pandev, p. 2, [0036] “By contrast, FIG. 1A is a flowchart 100 illustrating representative operations in a method of determining a new parameterization, in accordance with an embodiment of the present invention. Referring to flowchart 100, at operation 102, a sensitivity analysis of spectra is performed with respect to the model parameters. At operations 104 and 106, the spectral information is used to determine a new parameterization that minimizes the parameter correlation by transformation. In general, these operations involve working with signals measured as a function of wavelength, polarization, angle, and state of coherence, etc.”)
Regarding claim 9
Pandev discloses
A method, comprising:
(Pandev, p. 2, [0011] “FIG. 1A is a flowchart illustrating representative operations in a method of determining a new parameterization, in accordance with an embodiment of the present invention.”)
generating a plurality of first structure parameter sets corresponding to a plurality of first spectra associated with a first semiconductor structure based on a first optical transformation model
(Pandev, p. 1, [0008] “In an embodiment, a method of optimizing parametric models for structural analysis using metrology of repeating structures on a semiconductor substrate or wafer includes determining a first model of a structure. The first model is based on a first set of parameters.”)
(Pandev, p. 1, [0010] “… Also included is an optical metrology system configured to determine the one or more process parameters of the wafer application. The optical metrology system includes a beam source and detector configured to measure a diffraction signal of the structure.”)
wherein each first structure parameter set includes a structure parameter associated with the first semiconductor structure and a structure parameter associated with a previous structure of the first semiconductor structure
(Pandev, p. 2, [0034] “One or more embodiments of the present invention provide new approaches to determining an optimized parametric model for structure analysis using optical metrology of repeating structures on a semiconductor substrate or wafer.”)
(Pandev, p. 3, [0037] “As mentioned above, the method of flowchart 100 may include use PCA parameterization. The use of PCA for sensitivity analysis may be different for each point of the parameter space. A first exemplary case involves performing PCA on the nominal and ignoring the errors/correlations for different points in parameter space. A second exemplary case involves performing PCA using data from all multiple points in parameter space (e.g., using standard or detailed analysis from AcuShape). The values are averaged and then sensitivities are collected from all points and PCA is applied on the entire dataset.”)
(Pandev, p. 4, [0048] “… performing the methods above by using the combined spectra sensitivity data obtained from multiple points in parameter space, …)
updating the first structure parameter sets based on at least one physical parameter associated with the first semiconductor structure
(Pandev, p. 1, [0008] “… A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
(Pandev, p. 10, Claim 5. “The method of claim 1, wherein modifying the first model of the structure to provide the second model of the structure comprises reparametrizing geometric parameters or material parameters, or both, to provide the second set of parameters.”)
and establishing a second optical transformation model according to the updated first structure parameter sets and the corresponding first spectra.
(Pandev, p. 1, [0008] “…The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
Regarding claim 10
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, further comprising:
establishing the first optical transformation model according to a plurality of data, wherein each data includes a structure parameter set and a corresponding spectrum.
(Pandev, p. 8, [0091] “… In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information. Such training may be computationally intensive and/or may have to be repeated for different models and/or profile parameter domains.”)
Regarding claim 11
Pandev teaches all features of claim 10, as disclosed above and further discloses
The method of claim 10, wherein the first optical transformation model and the second optical transformation model are established based on a machine learning scheme.
(Pandev, p. 5, [0058] “Referring to operation 1002 of flowchart 1000, a library or trained machine learning systems (MLS) is developed to extract parameters from a set of measured diffraction signals. In operation 1004, at least one parameter of a structure is determined using the library or the trained MLS.”)
(Pandev, p. 8, [0091] “…In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information. Such training may be computationally intensive and/or may have to be repeated for different models and/or profile parameter domains.”)
Regarding claim 12
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, wherein the step of updating the first structure parameter sets based on at least one physical parameter associated with the first semiconductor structure further comprises:
updating the first structure parameter sets by a transformer based on the at least one physical parameter associated with the first semiconductor structure.
(Pandev, p. 10, Claim 5. “The method of claim 1, wherein modifying the first model of the structure to provide the second model of the structure comprises reparametrizing geometric parameters or material parameters, or both, to provide the second set of parameters.”)
Regarding claim 13
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, further comprising:
generating a plurality of second structure parameter sets corresponding to a plurality of second spectra associated with a second semiconductor structure based on the second optical transformation model.
(Pandev, p. 2, [0026] “FIG. 14 is a first architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0027] “FIG. 15 is a second architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 7, [0077] “… The signals collected in these measurements may be analyzed to determine parameters of structures on a semiconductor wafer in accordance with embodiments of the present invention.”)
Regarding claim 14
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, wherein in each first structure parameter set, the structure parameter associated with the first semiconductor structure includes a dimension, and the structure parameter associated with the previous structure of the first semiconductor structure includes a dimension.
(Pandev, p. 1, [0010] “… One or more process parameters characterize behavior of structure shape or layer thickness when the structure undergoes processing operations in the wafer application performed using the fabrication cluster.”)
(Pandev, p. 3, [0037] “… A global model of the parameterization is then generated as a function of geometric parameters.”)
(Pandev, p. 3, [0038] “… A model is generated for each coefficient in the parameterization equations, where each coefficient is as a function of the geometric parameters.”)
(Pandev, p. 4, [0053] “… Referring to FIG. 8, since there are finite possible outcomes with respect to fabricating structure 700, the model 800 focuses on a subset of parameters. As a specific, but non-limiting example, structure height (HT) 802, structure width (804), top critical dimension (TCD) 806 and bottom critical dimension (BCD) 808 are shown as possible parameters that may be analyzed in a modeling process.”)
(Pandev, p. 2, [0034] “One or more embodiments of the present invention provide new approaches to determining an optimized parametric model for structure analysis using optical metrology of repeating structures on a semiconductor substrate or wafer.”)
(Pandev, p. 3, [0037] “As mentioned above, the method of flowchart 100 may include use PCA parameterization. The use of PCA for sensitivity analysis may be different for each point of the parameter space. A first exemplary case involves performing PCA on the nominal and ignoring the errors/correlations for different points in parameter space. A second exemplary case involves performing PCA using data from all multiple points in parameter space (e.g., using standard or detailed analysis from AcuShape). The values are averaged and then sensitivities are collected from all points and PCA is applied on the entire dataset.”)
(Pandev, p. 4, [0048] “… performing the methods above by using the combined spectra sensitivity data obtained from multiple points in parameter space, …)
Regarding claim 15
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, wherein the at least one physical parameter includes a wafer radius, a wavelength or a process recipe.
(Pandev, p. 2, [0036] “… Referring to flowchart 100, at operation 102, a sensitivity analysis of spectra is performed with respect to the model parameters. At operations 104 and 106, the spectral information is used to determine a new parameterization that minimizes the parameter correlation by transformation. In general, these operations involve working with signals measured as a function of wavelength, polarization, angle, and state of coherence, etc.”)
(Pandev, p. 2, [0031] “FIG. 19 is an illustrative flowchart representing operations in a method for building a real-time regression measurement recipe for making production measurements of a structure, in accordance with an embodiment of this invention.”)
Regarding claim 16
Pandev teaches all features of claim 9, as disclosed above and further discloses
The method of claim 9, wherein each first structure parameter set further includes a structure parameter associated with another previous structure of the first semiconductor structure.
(Pandev, p. 2, [0034] “One or more embodiments of the present invention provide new approaches to determining an optimized parametric model for structure analysis using optical metrology of repeating structures on a semiconductor substrate or wafer.”)
(Pandev, p. 3, [0037] “As mentioned above, the method of flowchart 100 may include use PCA parameterization. The use of PCA for sensitivity analysis may be different for each point of the parameter space. A first exemplary case involves performing PCA on the nominal and ignoring the errors/correlations for different points in parameter space. A second exemplary case involves performing PCA using data from all multiple points in parameter space (e.g., using standard or detailed analysis from AcuShape). The values are averaged and then sensitivities are collected from all points and PCA is applied on the entire dataset.”)
(Pandev, p. 4, [0048] “… performing the methods above by using the combined spectra sensitivity data obtained from multiple points in parameter space, …)
Regarding claim 17
Pandev discloses
A system, comprising:
(Pandev, p. 1, [0009] “In another embodiment, a machine-accessible storage medium has instructions stored thereon which cause a data processing system to perform a method of optimizing parametric models for structural analysis using metrology of repeating structures on a semiconductor substrate or wafer.”)
a storage unit, being configured to store a first optical transformation model
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 2, [0030] “FIG. 18 is an illustrative flowchart representing operations in a method for building a library for making production measurements of a structure, in accordance with an embodiment of this invention.”)
(Pandev, p. 3, [0040] “… The Function+Delta type of parameterization may be applied to linear and non-liner parameter correlation. Modeled parameter space reduction (e.g., library size reduction) may be achieved for linear and non-linear parameter spaces “)
a processor, being connected to the storage unit electrically and configured to
(Pandev, p. 1, [0010] “… The optical metrology system also includes a processor configured to determine a first model of a structure, the first model based on a first set of parameters,…)
(Pandev, p. 2, [0022] “FIG. 11 is an exemplary block diagram of a system for determining and utilizing structural parameters for automated process and equipment control, in accordance with an embodiment of the present invention.”)
(Pandev, p. 2, [0028] “FIG. 16 illustrates a block diagram of an exemplary computer system, in accordance with an embodiment of the present invention.”)
input a plurality of first spectra associated with a first semiconductor structure into the first optical transformation model to output a plurality of first structure parameters
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 2, [0036] “… Referring to flowchart 100, at operation 102, a sensitivity analysis of spectra is performed with respect to the model parameters. At operations 104 and 106, the spectral information is used to determine a new parameterization that minimizes the parameter correlation by transformation.”)
(Pandev, p. 4, [0044] “… At operation 304, spectra and ray sensitivities are calculated and, then, PCA is performed at operation 306. The new PCA parameterization is performed at operations 308 and 310.”)
update the first structure parameters based on at least one physical parameter associated with the first semiconductor structure
(Pandev, p. 1, [0008] “… A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
(Pandev, p. 10, Claim 5. “The method of claim 1, wherein modifying the first model of the structure to provide the second model of the structure comprises reparametrizing geometric parameters or material parameters, or both, to provide the second set of parameters.”)
establish a second optical transformation model according to the updated first structure parameters and the corresponding first spectra
(Pandev, p. 1, [0008] “…The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
and store the second optical transformation model in the storage unit.
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 2, [0030] “FIG. 18 is an illustrative flowchart representing operations in a method for building a library for making production measurements of a structure, in accordance with an embodiment of this invention.”)
(Pandev, p. 3, [0040] “… The Function+Delta type of parameterization may be applied to linear and non-liner parameter correlation. Modeled parameter space reduction (e.g., library size reduction) may be achieved for linear and non-linear parameter spaces “)
Regarding claim 18
Pandev teaches all features of claim 17, as disclosed above and further discloses
The system of Claim 17, wherein the processor is further configured to:
establish the first optical transformation model according to a plurality of data, wherein each data includes a structure parameter and a corresponding spectrum.
(Pandev, p. 8, [0091] “… In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information. Such training may be computationally intensive and/or may have to be repeated for different models and/or profile parameter domains.”)
Regarding claim 19
Pandev teaches all features of claim 17, as disclosed above and further discloses
The system of claim 17, wherein the processor is further configured to:
input the first structure parameters into a transformer to update the first structure parameters.
(Pandev, p. 1, [0008] “… A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data.”)
(Pandev, p. 8, [0091] “… Library generation 1708 may include a machine learning system, such as a neural network, generating simulated spectral information for each of a number of profiles, each profile including a set of one or more modeled profile parameters. In order to generate the library, the machine learning system itself may have to undergo some training based on a training data set of spectral information.”)
(Pandev, p. 9, [0095] “… If the results do not meet expectations, then the library and/or parametric model need to be updated and the resulting new library retested (operation 1808). One or more embodiments of the present invention can used to determine what changes have to be made to the library or parametric model to improve the results.”)
Regarding claim 20
Pandev teaches all features of claim 17, as disclosed above and further discloses
The system of claim 17, wherein the processor is further configured to:
input a plurality of second spectra associated with a second semiconductor structure into the second optical transformation model to output a plurality of second structure parameters.
(Pandev, p. 2, [0026] “FIG. 14 is a first architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0027] “FIG. 15 is a second architectural diagram illustrating the utilization of optical metrology to determine parameters of structures on a semiconductor wafer, in accordance with embodiments of the present invention.”)
(Pandev, p. 2, [0029] “FIG. 17 is a flowchart representing operations in a method for a building parameterized model and a spectral library beginning with sample spectra, in accordance with an embodiment of the present invention.”)
(Pandev, p. 7, [0077] “… The signals collected in these measurements may be analyzed to determine parameters of structures on a semiconductor wafer in accordance with embodiments of the present invention.”)
Conclusion
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/R.S./Examiner, Art Unit 2851
/JACK CHIANG/Supervisory Patent Examiner, Art Unit 2851
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