DETAILED ACTION
The office action is responsive to an application filed on 6/22/23 and is being
examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending.
Priority
The current application filed on 6/22/23 claims priority from provisional application
63/355,053 filed on 6/23/2022.
Claim Interpretation
The examiner notes that the current independent claims do not contain language that
would fall within step 2A Prong 1 of the 35 U.S.C. 101 analysis. Therefore, the current claims do not contain a 101 rejection.
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “means for obtaining measured signals for the structure on the sample from a first metrology device”,
“means for extracting measurement results from a first physical model for the structure on the sample based on the measured signals” and “and means for determining parameters of interest for the structure on the sample with a machine learning model based on the measurement results extracted from the first physical model, and further based on at least one of: data from measured signals from the first metrology device not used in extracting the measurement results from the first physical model, second measured signals obtained for the structure on the sample from a second metrology device, process parameters used to generate the structure on the sample, Advanced Process Control (APC) parameters used to generate the structure on the sample, context data for the structure on the sample, and sensor data from production equipment used to generate the structure on the sample.”
in claim 19. In paragraphs [0077] – [0079] of the specification, it discloses the corresponding structure for the limitations shown above.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35
U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8, 10-15, 17 and 19-20 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Cho et al. (U.S. PGPub 2020/0193290) (from IDS dated 10/19/23) in view of Prager et al. (U.S. PGPub 2006/0247816) (from IDS dated 10/19/23).
With respect to claim 1, Cho et al. discloses “A method of characterizing a structure on a sample” as [Cho et al. (paragraph [0005] “According to an aspect, there is provided a thickness prediction network learning method including measuring spectrums of optical characteristics of a plurality of semiconductor structures each including a substrate and first and second semiconductor material layers alternately stacked on the substrate to generate sets of spectrum measurement data”)];
“obtaining measured signals for the structure on the sample from a first metrology device” as [Cho et al. (paragraph [0029] “According to some embodiments, the optical measurement module 1200 may include various devices for inspecting the semiconductor structure 100 formed by the deposition module 1100. According to some embodiments, the optical measurement module 1200 may include a reflectance measurement device 1210 (see FIG. 4A), an ellipsometer 1230 (see FIG. 4B), and a group delay dispersion (GDD) measurement device 1250 (see FIG. 4C). According to some embodiments, the optical measurement module 1200 may measure reflectance, polarization reflectance, polarization reflection delay, and GDD spectrums of the semiconductor structure 100.”, Cho et al. paragraph [0036] “The measured physical amount may include spectrums of a reflectance, a polarization reflectance, polarization reflection delay, and GDD of each of the semiconductor structures 100. The reflectance, the polarization reflectance, the polarization reflection delay, and the GDD may be measured at a plurality of positions of the semiconductor structures 100. For convenience of description, data of each of the spectrums of the reflectance, polarization reflectance, polarization reflection delay, and GDD of the semiconductor structures 100 measured in a learning process of a thickness prediction network may be referred to as first spectrum measurement data.”, Fig. 3B)];
“extracting measurement results from a first physical model for the structure on the sample based on the measured signals” as [Cho et al. (paragraph [0033] “According to some embodiments, as in FIG. 5A, the simulation network 1500a may generate sets of spectrum simulation data of a reflectance, a polarization reflectance, polarization reflection delay, and GDD according to a thickness of the first and second semiconductor material layers 111 and 112, based on a physical model.”, Cho et al. paragraph [0060] “According to some embodiments, in a learning process of the model base network 1520a, sets of first spectrum measurement data of the semiconductor structure 100 corresponding to thickness measurement data for calculating the sets of model base spectrum data may be an output O of the model base network 1520a, which is illustrated by an arrow A4 of FIG. 3B.”, Fig. 3B)];
“and determining parameters of interest for the structure on the sample with a machine learning model based on the measurement results extracted from the first physical model” as [Cho et al. (paragraph [0060] “According to some embodiments, in a learning process of the model base network 1520a, the sets of model base spectrum data may be an input I of the model base network 1520a, which is illustrated by an arrow A3 of FIG. 3B. According to some embodiments, in a learning process of the model base network 1520a, sets of first spectrum measurement data of the semiconductor structure 100 corresponding to thickness measurement data for calculating the sets of model base spectrum data may be an output O of the model base network 1520a, which is illustrated by an arrow A4 of FIG. 3B. That is, a process of allowing the model base network 1520a to learn, i.e., training the model base network 1520a, may include a process of updating parameters of hidden layers included in the model base network 1520a under a condition where the sets of model base spectrum data are the input I and the sets of first spectrum measurement data corresponding thereto are the output.”, Cho et al. paragraph [0082] “The thickness prediction network 1600 may be one of a neural network and a deep neural network. An input I of the thickness prediction network 1600 may include sets of first spectrum measurement data obtained by the optical measurement module 1200 and sets of spectrum simulation data obtained by the simulation networks 1500a to 1500c. An output O of the thickness prediction network 1600 may include measurement data of thicknesses of the first and second semiconductor material layers (111 and 112 see FIG. 2) included in the semiconductor structure (100 see FIG. 2)”)];
While Cho et al. teaches having a first metrology device that can extract measurement results from a physical model, Cho et al. does not explicitly disclose “and further based on at least one of: data from measured signals from the first metrology device not used in extracting the measurement results from the first physical model, second measured signals obtained for the structure on the sample from a second metrology device, process parameters used to generate the structure on the sample, Advanced Process Control (APC) parameters used to generate the structure on the sample, context data for the structure on the sample, and sensor data from production equipment used to generate the structure on the sample.”
Prager et al. discloses “and further based on at least one of: data from measured signals from the first metrology device not used in extracting the measurement results from the first physical model, second measured signals obtained for the structure on the sample from a second metrology device, process parameters used to generate the structure on the sample, Advanced Process Control (APC) parameters used to generate the structure on the sample, context data for the structure on the sample, and sensor data from production equipment used to generate the structure on the sample.” As [Prager et al. (paragraph [0038] “In one exemplary embodiment, the second metrology tool is a scatterometer and the first metrology tool is a CDSEM. In another exemplary embodiment, the second metrology tool is one type of scatterometer, and the first metrology tool is another type of scatterometer. For example, the second metrology tool is a reflectometer, and the first metrology tool is an ellipsometer.”, Prager et al. paragraph [0065] “Assume further that the second metrology tool selected is a CDSEM and the first metrology tool for measuring the structure parameters is a scatterometer and that the statistical metric criteria values set are an offset average α equal to or less than 4.0, a slope β of at least 0.80, and a R2 of 0.95 or better.”)];
Cho et al. and Prager et al. are analogous art because they are from the same field endeavor of analyzing the results from metrology devices.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Cho et al. of having a first metrology device that can extract measurement results from a physical model by incorporating and further based on at least one of: data from measured signals from the first metrology device not used in extracting the measurement results from the first physical model, second measured signals obtained for the structure on the sample from a second metrology device, process parameters used to generate the structure on the sample, Advanced Process Control (APC) parameters used to generate the structure on the sample, context data for the structure on the sample, and sensor data from production equipment used to generate the structure on the sample as taught by Prager et al. for the purpose of evaluating the adequacy of a profile model of a wafer structure.
Cho et al. in view of Prager et al. teaches and further based on at least one of: data from measured signals from the first metrology device not used in extracting the measurement results from the first physical model, second measured signals obtained for the structure on the sample from a second metrology device, process parameters used to generate the structure on the sample, Advanced Process Control (APC) parameters used to generate the structure on the sample, context data for the structure on the sample, and sensor data from production equipment used to generate the structure on the sample.
The motivation for doing so would have been because Prager et al. teaches that by evaluating the adequacy of a profile model of a wafer structure, the ability to provide quality to the fabrication process can be accomplished. This allows for fabrication process of a structure to be more efficient (Prager et al. paragraph [0004] – [0006]).
With respect to claim 2, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Cho et al. further discloses “wherein the data from measured signals comprises one of at least one data channel comprising a measurement subsystem defined by at least one of a light source, an optical path directed by optical parts, a detector, or a combination thereof” as [Cho et al. (paragraph [0036] “Referring to FIGS. 1 to 3B, in operation P110, the physical amount of the semiconductor structures 100 may be measured. The measured physical amount may include spectrums of a reflectance, a polarization reflectance, polarization reflection delay, and GDD of each of the semiconductor structures 100. The reflectance, the polarization reflectance, the polarization reflection delay, and the GDD may be measured at a plurality of positions of the semiconductor structures 100.”, Cho et al. paragraph [0037] “Referring to FIG. 4A for a more detailed description, the reflectance measurement device 1210 may include first and second light sources 1211 and 1212, first and second splitters 1213 and 1214, an objective lens 1215, a mirror 1216, a grating mirror 1217, and a charge coupled device (CCD) camera 1218.”)];
“and at least one data chunk comprising a subset of wavelengths, frequencies, angles, time span, or any combination thereof from a full data set provided by the at least one data channel.” as [Cho et al. (paragraph [0038] “According to some embodiments, a reflectance may be measured in a first wavelength band, e.g., an ultraviolet (UV) band, a visible light band, a near-infrared band, an infrared band, and the like, depending upon the semiconductor structure being measured.”, Cho et al. paragraph [0039] “According to some embodiments, the first light source 1211 may generate light in a UV band. According to some embodiments, the second light source 1212 may generate light of a visible light band, a near-infrared band, or an infrared band. Therefore, lights of various wavelength bands from infrared light to UV light may be irradiated onto the semiconductor structures 100.”)];
With respect to claim 3, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Cho et al. further discloses “wherein the machine learning model is generated based on measurement results extracted by the first physical model for one or more reference samples for the structure and at least one of reference data and design of experiment information, and at least one of: data from measured signals not used in generating the first physical model, second measured signals obtained for the one or more reference samples from the second metrology device, process parameters used to generate the one or more reference samples, APC parameters used to generate the one or more reference samples, context data for the one or more reference samples, and sensor data from production equipment used to generate the one or more reference samples.” as [Cho et al. (paragraph [0034] “According to some embodiments, the thickness prediction network 1600 may be trained based on measurement spectrums obtained by the optical measurement module 1200, measurement data obtained by the thickness measurement module 1300, and sets of spectrum simulation data generated by the simulation networks 1500a to 1500c.”)];
With respect to claim 4, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Prager et al. further discloses “wherein the measurement results are extracted from the firs physical model for the structure on the sample further based on the second measured signals for the structure on the sample from the second metrology device.” as [Prager et al. (paragraph [0038] “In one exemplary embodiment, the second metrology tool is a scatterometer and the first metrology tool is a CDSEM. In another exemplary embodiment, the second metrology tool is one type of scatterometer, and the first metrology tool is another type of scatterometer.”, Prager et al. paragraph [0065] “Assume further that the second metrology tool selected is a CDSEM and the first metrology tool for measuring the structure parameters is a scatterometer and that the statistical metric criteria values set are an offset average α equal to or less than 4.0, a slope β of at least 0.80, and a R2 of 0.95 or better.”, Prager et al. paragraph [0071] “In step 504, the correlation data is utilized to control the one or more process variables of the subsequent fabrication step with profile parameter data obtained using the first metrology tool and the second metrology tool. In step 506, after the subsequent fabrication step is completed, the structure profile is determined using the second metrology tool and the first metrology tool. For example, after an etch fabrication step, the profile of the structure is measured with both a CDSEM as the second or reference metrology tool and a scatterometer as the first metrology tool. The measurements are designed to capture the data needed for the process control type selected.”)];
With respect to claim 5, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Cho et al. further discloses “wherein the measurement results that are extracted from the first physical model for the structure on the sample are further based on at least one of the process parameters, the APC parameters, the context data, and the sensor data from production equipment.” as [Cho et al. (paragraph [0031] “According to some embodiments, the process controller 1400 may adjust various process parameters used to perform and/or select a process. Examples of the process parameters may include a temperature, pressure, a period time, a process gas composition, a process gas concentration, a chamber application voltage, etc., but are not limited thereto. According to some embodiments, the process controller 1400 may adjust the process parameters, based on a feedback signal from the thickness prediction network 1600.”, Cho et al. (paragraph [0058] “Referring to FIGS. 1, 2, and 5A, the spectrum modeling simulator 1510a may generate sets of model base spectrum data by applying data of a thickness of each of the first and second semiconductor material layers 111 and 112 measured by the thickness measurement module 1300 and a known refractive index of each of the first and second semiconductor material layers 111 and 112 to a physical model corresponding to a multi-layer structure including a plurality of dielectric layers described above with reference to FIG. 5A.”)];
With respect to claim 6, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Prager et al. further discloses “extracting second measurement results from a second physical model for the structure on the sample based on the second measured signals from the second metrology device” as [Prager et al. (paragraph [0038] “In one exemplary embodiment, the second metrology tool is a scatterometer and the first metrology tool is a CDSEM. In another exemplary embodiment, the second metrology tool is one type of scatterometer, and the first metrology tool is another type of scatterometer. For example, the second metrology tool is a reflectometer, and the first metrology tool is an ellipsometer.”, Prager et al. paragraph [0065] “Assume further that the second metrology tool selected is a CDSEM and the first metrology tool for measuring the structure parameters is a scatterometer and that the statistical metric criteria values set are an offset average α equal to or less than 4.0, a slope β of at least 0.80, and a R2 of 0.95 or better.”)];
“wherein the machine learning model determines the parameters of interest for the structure on the sample further based on the second measurement results extracted from the second physical model.” as [Prager et al. (paragraph [0076] “Still referring to FIG. 6, coupled to the processor 606 are a metrology library 600 and a trained machine learning system (MLS) 602. The library 600 includes a set of diffraction signals and corresponding structure profile model parameters of the structure. The library 600 may include a sub-library for profile extraction of the wafer structure after a process step in the first semiconductor fabrication device 616 and another sub-library for profile extraction of the wafer structure after a process step in the second semiconductor fabrication device 624. Similarly, the trained MLS 602 may include a subsystem trained to determine the structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the first semiconductor fabrication device 616 and a subsystem trained to determine the wafer structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the second semiconductor fabrication device 624. The processor 606 uses the appropriate sub-library 600 or trained MLS subsystem 602 to extract profile model parameters 604 corresponding to the output signals 608 or 610 received from the metrology tools 612 and 620 respectively.”)];
With respect to claim 8, the combination of Cho et al. and Prager et al. discloses the method of claim 6 above, and Prager et al. further discloses “wherein the second measurement results are extracted from the second physical model for the structure on the sample further based on at least one of the process parameters, the APC parameters, the context data, and the sensor data from production equipment.” as [Prager et al. (paragraph [0076] “Still referring to FIG. 6, coupled to the processor 606 are a metrology library 600 and a trained machine learning system (MLS) 602. The library 600 includes a set of diffraction signals and corresponding structure profile model parameters of the structure. The library 600 may include a sub-library for profile extraction of the wafer structure after a process step in the first semiconductor fabrication device 616 and another sub-library for profile extraction of the wafer structure after a process step in the second semiconductor fabrication device 624. Similarly, the trained MLS 602 may include a subsystem trained to determine the structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the first semiconductor fabrication device 616 and a subsystem trained to determine the wafer structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the second semiconductor fabrication device 624. The processor 606 uses the appropriate sub-library 600 or trained MLS subsystem 602 to extract profile model parameters 604 corresponding to the output signals 608 or 610 received from the metrology tools 612 and 620 respectively.”)];
With respect to claim 10, Cho et al. discloses “A computer system configured for characterizing a structure on a sample” as [Cho et al. (paragraph [0005] “According to an aspect, there is provided a thickness prediction network learning method including measuring spectrums of optical characteristics of a plurality of semiconductor structures each including a substrate and first and second semiconductor material layers alternately stacked on the substrate to generate sets of spectrum measurement data”)];
“at least one memory configured store measured signals, measurement results, a first physical model, a machine learning model, and parameters of interest for the structure” as [Cho et al. (paragraph [0031] “According to some embodiments, the process controller 1400 may be a computing device including one or more software products for controlling operations of the deposition module 1100 and the optical measurement module 1200, e.g., a workstation computer, a desktop computer, a laptop computer, and a tablet computer.”, Cho et al. paragraph [0106] “Alternatively, each block, unit, module, and/or method may be implemented by dedicated hardware, or as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.”, Fig. 1)];
“and at least one processor coupled to the at least one memory” as [Cho et al. (paragraph [0031] “According to some embodiments, the process controller 1400 may be a computing device including one or more software products for controlling operations of the deposition module 1100 and the optical measurement module 1200, e.g., a workstation computer, a desktop computer, a laptop computer, and a tablet computer.”, Cho et al. paragraph [0106] “Alternatively, each block, unit, module, and/or method may be implemented by dedicated hardware, or as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.”, Fig. 1)];
The other limitations of the claim recite the same substantive limitations as claim 1 above and are rejected using the same teachings.
With respect to claims 11-15, the claims recite the same substantive limitations as claims 2-6 above and are rejected using the same teachings.
With respect to claim 17, the claim recites the same substantive limitations as claim 8 above and is rejected using the same teachings.
With respect to claim 19, Cho et al. discloses “A system configured for characterizing a structure on a sample” as [Cho et al. (paragraph [0005] “According to an aspect, there is provided a thickness prediction network learning method including measuring spectrums of optical characteristics of a plurality of semiconductor structures each including a substrate and first and second semiconductor material layers alternately stacked on the substrate to generate sets of spectrum measurement data”, Cho et al. paragraph [0053] “A configuration of optical measurement equipment (1200 see FIG. 1) illustrated in FIGS. 4A to 4C is an example, but the optical measurement equipment (1200 see FIG. 1) may include an optical system having an arbitrary configuration for measuring a reflectance, a polarization reflectance, polarization reflection delay, and GDD.”, Fig. 1)];
The other limitations of the claim recite the same substantive limitations as claim 1 above and are rejected using the same teachings.
With respect to claim 20, the claim recites the same substantive limitations as claim 6 above and is rejected using the same teachings.
Claim(s) 7, 9, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Cho et al. in view of Prager et al. in further view of Pandev (U.S. PGPub 2021/0109453) (from IDS dated 10/19/23).
With respect to claim 7, the combination of Cho et al. and Prager et al. discloses the method of claim 6 above.
While the combination of Cho et al. and Prager et al. teach extracting second measurement results from a second physical model for a structure, Cho et al. and Prager et al. do not explicitly disclose “wherein the second measurement results are extracted from the second physical model for the structure on the sample further based on third measured signals for the structure on the sample from a third metrology device.”
Pandev discloses “wherein the second measurement results are extracted from the second physical model for the structure on the sample further based on third measured signals for the structure on the sample from a third metrology device.” as [Pandev (paragraph [0005] “Second metrology data are obtained for a plurality of instances of a metrology target, wherein the metrology target contains structures distinct from the semi-periodic or non-periodic structures in the device area……Third metrology data are obtained for an instance of the device area on a first semiconductor die that is distinct from the semiconductor die having the first plurality of instances of the device area. Using the trained machine-learning model, fourth metrology data are predicted for the metrology target based on the third metrology data.”, Pandev paragraph [0038] “Alternatively, the first metrology data 302 and the third metrology data 308 each include a first type of metrology data, while the second metrology data 304 and the fourth metrology data 310 each include a second type of metrology data distinct from the first type of metrology data. In one such example, the first metrology data 302 and the third metrology data 308 each include data for optical spectra (e.g., ellipsometry data or reflectometry data), while the second metrology data 304 and the fourth metrology data 310 each include SEM data (or vice-versa).”)];
Cho et al., Prager et al. and Pandev are analogous art because they are from the same field endeavor of analyzing the results from metrology devices.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Cho et al. and Pranger et al. of extracting second measurement results from a second physical model for a structure by incorporating wherein the second measurement results are extracted from the second physical model for the structure on the sample further based on third measured signals for the structure on the sample from a third metrology device as taught by Pandev for the purpose of transforming metrology data to obtain parameter measurements for semiconductor structures.
Cho et al. in view of Prager et al. in further view of Pandev teaches wherein the second measurement results are extracted from the second physical model for the structure on the sample further based on third measured signals for the structure on the sample from a third metrology device.
The motivation for doing so would have been because Pandev teaches that by transforming metrology data to obtain parameter measurements for semiconductor structures, the ability to model complex semiconductor structures can be accomplished. This allows for an improved way to predict a metrology target (Pandev paragraphs [0003] – [0005]).
With respect to claim 9, the combination of Cho et al. and Prager et al. discloses the method of claim 1 above, and Prager et al. further discloses “wherein the machine learning model determines the parameters of interest for the structure on the sample further based on the second measured signals from the second metrology device” as [Prager et al. (paragraph [0038] “In one exemplary embodiment, the second metrology tool is a scatterometer and the first metrology tool is a CDSEM. In another exemplary embodiment, the second metrology tool is one type of scatterometer, and the first metrology tool is another type of scatterometer. For example, the second metrology tool is a reflectometer, and the first metrology tool is an ellipsometer.”, Prager et al. paragraph [0065] “Assume further that the second metrology tool selected is a CDSEM and the first metrology tool for measuring the structure parameters is a scatterometer and that the statistical metric criteria values set are an offset average α equal to or less than 4.0, a slope β of at least 0.80, and a R2 of 0.95 or better.”, Prager et al. paragraph [0076] “Still referring to FIG. 6, coupled to the processor 606 are a metrology library 600 and a trained machine learning system (MLS) 602. The library 600 includes a set of diffraction signals and corresponding structure profile model parameters of the structure. The library 600 may include a sub-library for profile extraction of the wafer structure after a process step in the first semiconductor fabrication device 616 and another sub-library for profile extraction of the wafer structure after a process step in the second semiconductor fabrication device 624. Similarly, the trained MLS 602 may include a subsystem trained to determine the structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the first semiconductor fabrication device 616 and a subsystem trained to determine the wafer structure profile model parameters of the wafer structure from measured diffraction signals after the process step in the second semiconductor fabrication device 624. The processor 606 uses the appropriate sub-library 600 or trained MLS subsystem 602 to extract profile model parameters 604 corresponding to the output signals 608 or 610 received from the metrology tools 612 and 620 respectively.”)];
While the combination of Cho et al. and Prager et al. teach extracting second measurement results from a second physical model for a structure, Cho et al. and Prager et al. do not explicitly disclose “and further based on third measured signals for the structure on the sample from a third metrology device.”
Pandev discloses “and further based on third measured signals for the structure on the sample from a third metrology device.” as [Pandev (paragraph [0005] “Second metrology data are obtained for a plurality of instances of a metrology target, wherein the metrology target contains structures distinct from the semi-periodic or non-periodic structures in the device area……Third metrology data are obtained for an instance of the device area on a first semiconductor die that is distinct from the semiconductor die having the first plurality of instances of the device area. Using the trained machine-learning model, fourth metrology data are predicted for the metrology target based on the third metrology data.”, Pandev paragraph [0006] “The one or more programs also include instructions for using the trained machine-learning model to predict fourth metrology data for the metrology target based on third metrology data for an instance of the device area on a first semiconductor die that is distinct from the semiconductor die having the first plurality of instances of the device area.”)];
Cho et al., Prager et al. and Pandev are analogous art because they are from the same field endeavor of analyzing the results from metrology devices.
Before the effective filing date of the invention, it would have been obvious to a person
of ordinary skill in the art to modify the teachings of Cho et al. and Pranger et al. of extracting second measurement results from a second physical model for a structure by incorporating and further based on third measured signals for the structure on the sample from a third metrology device as taught by Pandev for the purpose of transforming metrology data to obtain parameter measurements for semiconductor structures.
Cho et al. in view of Prager et al. in further view of Pandev teaches and further based on third measured signals for the structure on the sample from a third metrology device.
The motivation for doing so would have been because Pandev teaches that by transforming metrology data to obtain parameter measurements for semiconductor structures, the ability to model complex semiconductor structures can be accomplished. This allows for an improved way to predict a metrology target (Pandev paragraphs [0003] – [0005]).
With respect to claim 16, the claim recites the same substantive limitations as claim 7 above and is rejected using the same teachings.
With respect to claim 18, the claim recites the same substantive limitations as claim 9 above and is rejected using the same teachings.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The relevance of Wu et al. (U.S. PGPub 2020/0184372) is a metrology system that generates a geometric model for determining a profile of a test HAR structure from metrology data from a reference metrology tool.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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