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 .
Status of Claims
The present application is being examined under the claims filed 02/06/2025.
Claims 1-20 are pending.
Response to Amendment
This Office Action is in response to Applicant’s communication filed 02/06/2025 in response to office action mailed 11/06/2025. The Applicant’s remarks and any amendments to the claims or specification have been considered with the results that follow.
Response to Arguments
In Remarks pages 7-8, Argument 1
(Examiner summarizes Applicant’s arguments) Applicant argues that the cited portions of Kwak and Park are technically non workable and that the combination of Kwak and Park does not generate images simulating camera chamber images.
Examiner’s response to Argument 1
Applicant’s amendments change the interpretation of the claim to explicitly require simulated images as opposed to using a combination of simulated data and images taken with a real-world camera. Thus Examiner’s rejection of claim 1 no longer relies on a combination of Kwak and Park, but on Kwak with a new reference Fallin, rendering applicants arguments on the combination of Kwak and Park moot. Nonetheless, examiner believes the new grounds of rejection necessitated by the amendment substantially address applicant’s concerns. Note that, when the simulated spectral data taught by Kwak is modified by the images generated by Fallin, the images would depict a semiconductor chamber environment because the data of Kwak simulates a semiconductor chamber environment (see fig. 1 for example).
In Remarks pages 8-9, Argument 2
(Examiner summarizes Applicant’s arguments) Applicant argues that Kwak does not teach the feature of “labeling the one or more images with the film thickness profile for training a machine-learning model” because Kwak does not label with a dataset characterizing film thickness at various locations as disclosed by the specification, and that Kwak teaches labeling 1D curves instead of 2D images.
Examiner’s response to Argument 2
Examiner’s must not import limitations from the specification into the claim, but instead interpret limitations with their broadest reasonable interpretation in light of the specification. The broadest reasonable interpretation of the term “film thickness profile” includes any description or metric describing the thickness of a film, including the metric taught by Kwak. Furthermore, while Kwak alone does not appear to teach labeling 2D images with the film thickness profile, Kwak is combined with the new reference Fallin by using generated simulation images (taught by Fallin) for the machine learning training (taught by Kwak) and thus the combination teaches labeling 2D images with a film thickness profile.
Claim Objections
Regarding Claim 2
Claim 2 is objected to because of the following informalities: “extending from a center of the wafer to a periphery of the semiconductor substrate” should read “extending from a center of the wafer to a periphery of the wafer” or alternatively “extending from a center of the semiconductor substrate to a periphery of the semiconductor substrate”. Appropriate correction is required.
Regarding Claim 17
Claim 17 is objected to because of the following informalities: “wherein the one or images comprise” should read “wherein the one or more images comprise”. Appropriate correction is required.
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.
Claims 1, 5, 8-9, 13, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over NPL reference Kwak et al. “Non-destructive thickness characterization of 3D multilayer semiconductor devices using optical spectral measurements and machine learning” herein referred to as Kwak in view of Fallin et al. (Patent no. US11094134B1).
Regarding Claim 1
Kwak teaches:
A method of training models to characterize film thicknesses on semiconductor substrates, the method comprising:
(page 1 abstract) “In the manufacturing of 3D NAND, accurate characterisation of layer-by-layer thickness is critical to prevent the production of defective devices due to non-uniformly deposited layers. To date, electron microscopes have been used in production facilities to characterise multilayer semiconductor devices”
receiving a film thickness profile representing a film on a semiconductor substrate design;
(page 3 column 2 paragraph 2) “For the machine learning model, spectral data and layer thicknesses were used as inputs and outputs, respectively.”; (page 10 column 1 paragraph 2) “When designing the simulated outlier[*Examiner notes: substrate design] case data, the thickness of the outlier layer was uniformly distributed[*Examiner notes: receiving film thickness profile] within a ±20% variation with respect to the reference thickness, and the thicknesses of the other layers were uniformly distributed within ±4% of the reference thickness.”
simulating a light source being reflected off of the film on the semiconductor substrate
(page 5 column 1 paragraph 1) “To distinguish outlier cases from normal cases, both normal and outlier samples are required to train the machine learning model. However, because it is impossible to fabricate all possible outlier samples for this training, we used a large number of simulated spectral data for more effective and economical training. The measured reflectance showed reasonable agreement with the simulated data. Therefore, reflectance data[*Examiner notes: simulating a light source being reflected off of the film] were used for the outlier detection models. We first generated 1,000 simulated data with a wide range of thickness distributions as outlier cases.”
and labeling the one or more images with the film thickness profile for training a machine-learning model.
(page 9 column 2 section “Outlier detection methods”) “To detect outliers, we used simulated spectroscopic data for model training. The matrix method32 was used to obtain the theoretical values of reflectance (see the ‘Theoretical model of spectroscopic data’ section in ‘Methods’). To simulate the spectroscopic data, the thickness of each layer and the refractive index of each medium were required.”; (page 9 column 1 paragraph 3) “For the linear model[*Examiner notes: machine learning model], we applied L2 regularisation46 to avoid overfitting and used a conjugate gradient function51 with 1,000 iterations to minimise cost function J as follows: [Equation 5] […] yi is the actual thickness” [*Examiner notes: The “images” are taught more explicitly by Fallin below. Kwak uses the thickness profile as ground-truth labels for machine learning.]
Kwak does not explicitly teach:
simulating a light source being reflected off of the film on the semiconductor substrate to generate simulated spectral data;
converting the simulated spectral data into one or more images that simulate images that would be captured by a camera in a semiconductor processing chamber processing a wafer having the film thickness profile;
Fallin teaches:
simulating a light source being reflected off of the film on the semiconductor substrate to generate simulated spectral data;
(column 12 line 2) “Once the composite image 106 is generated, the method 400 continues by simulating, in the processing device 300 reflection or emission of at least one type of radiant energy from the surface of the object and/or the background according to a set of parameters associated with at least one of the object of the 3D image model 102 and the background image 104 (Step 408).”
converting the simulated spectral data into one or more images that simulate images that would be captured by a camera in a semiconductor processing chamber processing a wafer having the film thickness profile;
(column 12 line 8) “The processing device 300 also simulates a reflectance or emittance measurement of the at least one type of radiant energy from the surface of the object of the 3D image model 102 by a sensor device configured for detecting the at least one type of radiant energy (Step 410). The method 400 includes the step of generating, via the processing device 300, a plurality of two-dimensional (2D) simulated images of different perspectives of the object of the 3D image model 102 based on simulation data (Step 412).”
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness of Kwak by generating the images of Fallin from the simulation data of Kwak as the machine learning data labeled by Kwak because (Fallin column 3 line 54) “As a result, the object model of the present disclosure encompasses a greater percentage of possible true images than is provided by known computer vision systems, which are trained only with non-synthetic realistic or real-world images or scenes of objects where an operator modifies various pixels in the image to generate synthetic data. The object model of the present disclosure provides sufficient overlap with real-world images and addresses real-world random distribution challenges by generating a high probability of uniform samples where all points are equally likely to occur.” and (Fallin column 1 line 37) “The problem in this practice is that training Computer Vison (CV) models require robust volumes of pre-labeled empirical imagery in order to achieve adequate pattern detection and classification results. Without a sufficient supply or access to pre-trained imagery, existing computer vision solutions are ill-equipped to support real-world applications where empirical examples are not available or limited in perspectives (e.g., spatial, environmental, spectral, or depth).”
Additionally, generating images that would be found in a semiconductor manufacturing chamber environment would be beneficial to Kwak because (Kwak page 3 column 1 last paragraph) “After the spectroscopic measurements were conducted, each wafer was cut, and its cross-section was imaged by TEM. The TEM images were used as a reference for evaluating the accuracy of the proposed method”. Thus generating robust synthetic images for training machine learning would reduce the burden of creating the real and expensive images taught by Kwak, and can be used as a reference to determine accuracy (which is useful in training a machine learning model).
Regarding Claim 5
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection claim 1)
Kwak further teaches:
wherein the semiconductor substrate design comprises a design file including a film material.
[*Examiner notes: The broadest reasonable interpretation of “design file” includes a representation of a semiconductor design, for example a list of design parameters (whether the representation is stored on a computer or not). E.g. see specification paragraph 51]; (page 9 column 2 section “outlier detection methods”) “To detect outliers, we used simulated spectroscopic data for model training. The matrix method32 was used to obtain the theoretical values of reflectance (see the ‘Theoretical model of spectroscopic data’ section in ‘Methods’). To simulate the spectroscopic data, the thickness of each layer and the refractive index of each medium[*Examiner notes: design file] were required. We used the measured refractive index obtained by a single layer measurement of each material[*Examiner notes: including a film material] with an ellipsometer (Fig. S8) as the refractive index of each material (oxide, nitride, and Si substrate) in the modelling.”
Regarding Claim 8
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection of claim 1)
And Kwak further teaches:
wherein the semiconductor substrate design does not require a physical substrate to be manufactured or processed in order to simulate the light source being reflected off of the film and converting the simulated spectral data into the one or more images.
(page 5 column 1 paragraph 1) “To distinguish outlier cases from normal cases, both normal and outlier samples are required to train the machine learning model. However, because it is impossible to fabricate all possible outlier samples for this training, we used a large number of simulated spectral data for more effective and economical training.”
Regarding Claim 9:
Claim 9 is a computer system claim corresponding to method claim 1. The only difference is that claim 9 recites a computer with processors and memory:
Kwak further teaches:
A system comprising: one or more processors; and one or more memory devices comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
(page 9 column 1 paragraph 2) “For the implementation, we used a Titan X graphical processing unit (GPU).”
The remaining limitations of the claim are taught by the rejection of claim 1.
Regarding Claim 13
Kwak in view of Fallin teaches:
The system of claim 9
(see rejection of claim 9)
Kwak further teaches:
wherein simulating the light source being reflected off of the film comprises: accessing a film material and physical properties of the film material, wherein the machine-learning model is trained specifically for the film material.
(page 9 column 2 “Outlier detection methods” section) “To detect outliers, we used simulated spectroscopic data for model training[*Examiner notes: trained specifically for the film material]. The matrix method32 was used to obtain the theoretical values of reflectance (see the ‘Theoretical model of spectroscopic data’ section in ‘Methods’). To simulate the spectroscopic data, the thickness of each layer and the refractive index of each medium were required. WeWe used the measured refractive index[*Examiner notes: physical properties] obtained by a single layer measurement of each material with an ellipsometer (Fig. S8) as the refractive index of each material (oxide, nitride, and Si substrate) in the modelling[*Examiner notes: specifically for the film material]. Because the outlier detection method focuses on detecting relatively large thickness changes, precise optical modelling by accurate refractive index characterisation was not required. We assumed that all the oxide and nitride layers shared the same oxide and nitride refractive indices, respectively.”
Regarding Claim 15
Kwak in view of Fallin teaches:
The system of claim 9
(see rejection of claim 9)
And Kwak further teaches:
wherein a plurality of different film thickness profiles are simulated to generate a training data set for various film thicknesses for a specific film material.
(page 9 column 2 section “outlier detection methods”) “To detect outliers, we used simulated spectroscopic data for model training. The matrix method32 was used to obtain the theoretical values of reflectance (see the ‘Theoretical model of spectroscopic data’ section in ‘Methods’). To simulate the spectroscopic data, the thickness of each layer and the refractive index of each medium were required. We used the measured refractive index obtained by a single layer measurement of each material[*Examiner notes: specific film material] with an ellipsometer (Fig. S8) as the refractive index of each material (oxide, nitride, and Si substrate) in the modelling.”; (page 5 column 1 paragraph 1) “We first generated 1,000 simulated data with a wide range of thickness distributions[*Examiner notes: various film thicknesses for a specific material] as outlier cases.”
Regarding Claim 16
Claim 16 is a non-transitory computer-readable medium claim corresponding to method claim 1. The only difference is that claim 1 recites a non-transitory computer-readable medium with instructions executed by processors
And Kwak further teaches:
One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
(page 9 column 1 paragraph 2) “For the implementation, we used a Titan X graphical processing unit (GPU).”
The remaining limitations of the claim are taught by the rejection of claim 1.
Regarding Claim 18
Kwak in view of Fallin teaches:
The one or more non-transitory computer-readable media of claim 16
(see rejection of claim 16)
And Kwak further teaches:
wherein the film thickness profile includes a simulated wafer defect, wherein the machine- learning model is trained to recognize a wafer defect corresponding to the simulated wafer defect
(page 6 column 1 paragraph 1) “When using noisy data as input to machine learning algorithms, the trained model is robust against various measurement errors. In addition, our outlier detection method can detect significant thickness defects by using a relatively small number of TEM measurements (e.g. 18 samples used as normal cases in this work) and massive simulated data (used as outlier cases).”
Regarding Claim 19
Kwak in view of Fallin teaches:
The one or more non-transitory computer-readable media of claim 16,
(see rejection of claim 19)
And Kwak further teaches:
wherein the operations further comprise adding simulated signal noise when simulating the light source being reflected off the film on the semiconductor substrate and generating the simulated spectral data
(page 8 column 1 paragraph 3) “Data augmentation is widely used for a relatively small amount of data in many applications43–45. Because our objective was to access only a small number of normal-condition samples (in commercial device production lines), we augmented the training samples by employing a noise-injection method. For multilayer metrology of normal conditions, 125 training samples were augmented by injecting noise, resulting in a total of 5,000 augmented data points (40 augmented data points per training sample).”
Regarding Claim 20
Kwak in view of Fallin teaches:
The one or more non-transitory computer-readable media of claim 16
(see rejection of claim 16)
And Kwak further teaches:
wherein labeling the one or more images with the film thickness profile comprises: labeling the one or more images with ranges of film thicknesses.
(page 10 column 1 paragraph 2) “and 1,000 simulated data, which were designed with a relatively large thickness variation in each layer, were generated. When designing the simulated outlier case data, the thickness of the outlier layer was uniformly distributed within a ±20% variation with respect to the reference thickness[*Examiner notes: ranges of film thicknesses], and the thicknesses of the other layers were uniformly distributed within ±4% of the reference thickness.”
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Kwak in view of Fallin, and further in view of Furukawa et al. (Patent no. US6344416B1) herein referred to as Furukawa.
Regarding Claim 2
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection of claim 1)
Kwak in view of Fallin does not explicitly teach:
wherein the film thickness profile comprises measurements of a thickness of the film extending from a center of the wafer to a periphery of the semiconductor substrate
However, Furukawa teaches:
wherein the film thickness profile comprises measurements of a thickness of the film extending from a center of the wafer to a periphery of the semiconductor substrate
(column 5 line 24) “Method 2000 begins in step 2010 when a semiconductor film is deposited at a first radius of the semiconductor wafer. Generally, this radius will be an inner radius towards the center of the semiconductor wafer, but first radius may also be an outer radius towards the edge of the semiconductor wafer. The semiconductor film will be deposited to a certain thickness.”; (column 10 line 66) “FIG. 6 shows that a variety of locations 401 through 410 may have different thicknesses and/or compositions. These different thicknesses and/or compositions are chosen to reduce the radial effects caused by a subsequent semiconductor process step.”
PNG
media_image1.png
473
559
media_image1.png
Greyscale
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the thickness profile measurement taught by Furukawa because (Furukawa column 2 line 11) “One of the embodiments of the present invention radially varies the thickness and/or composition of a semiconductor film to compensate for a known radial variation in the semiconductor film that is caused by performing a subsequent semiconductor processing step on the semiconductor film.”
Regarding Claim 3
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection of claim 1)
Kwak in view of Fallin does not explicitly teach:
wherein the film thickness profile comprises thicknesses of the film at a plurality of different radii extending out from a center of the semiconductor substrate
However, Furukawa teaches:
wherein the film thickness profile comprises thicknesses of the film at a plurality of different radii extending out from a center of the semiconductor substrate
(column 5 line 24) “Method 2000 begins in step 2010 when a semiconductor film is deposited at a first radius of the semiconductor wafer. Generally, this radius will be an inner radius towards the center of the semiconductor wafer, but first radius may also be an outer radius towards the edge of the semiconductor wafer. The semiconductor film will be deposited to a certain thickness.”; (column 10 line 66) “FIG. 6 shows that a variety of locations 401 through 410 may have different thicknesses and/or compositions. These different thicknesses and/or compositions are chosen to reduce the radial effects caused by a subsequent semiconductor process step.”
PNG
media_image1.png
473
559
media_image1.png
Greyscale
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the thickness profile measurement taught by Furukawa because (Furukawa column 2 line 11) “One of the embodiments of the present invention radially varies the thickness and/or composition of a semiconductor film to compensate for a known radial variation in the semiconductor film that is caused by performing a subsequent semiconductor processing step on the semiconductor film.”
Regarding Claim 4
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection of claim 1)
Kwak in view of Fallin does not explicitly teach:
wherein the film thickness profile is specific to a film material and one or more underlying film materials.
However, Furukawa teaches:
wherein the film thickness profile is specific to a film material and one or more underlying film materials
(column 9 line 58) “Nozzles 120 and 130 may be made to change the composition of the semiconductor film by gradually changing materials in the composition as the semiconductor film is deposited. A semiconductor film may also be deposited that comprises two distinct layers of materials. Finally, “grading” of the film may be accomplished by depositing a first material[*Examiner notes: a film material] and then, at a demarcation point, depositing a second material[*Examiner notes: underlying film material], and changing the demarcation point with increasing layers of the semiconductor film.”; (column 10 line 37) “With this variety of locations and the ability to change thicknesses, grading, or compositions across one of the swathes, many different semiconductor devices having different device characteristics may be made on one semiconductor wafer.”
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the film material and underlying film materials of Furukawa because (Furukawa column 2 line 5) “Accordingly, the present invention provides methods and apparatuses that can introduce deliberate semiconductor film variation during semiconductor manufacturing to compensate for radial processing differences, to determine optimal device characteristics, or produce small production runs.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kwak in view of Fallin, and further in view of Vukkadala et al (PGPUB no. US20190333794A1) herein referred to as Vukkadala.
Regarding Claim 6
Kwak in view of Fallin teaches:
The method of claim 1
(see rejection of claim 1)
Kwak in view of Fallin does not explicitly teach:
wherein simulating the light source being reflected off of the film on the semiconductor substrate comprises: receiving a light spectra for a light source, wherein the light source comprises a laser that will be directed to a physical semiconductor substrate during a semiconductor process
However, Vukkadala teaches:
wherein simulating the light source being reflected off of the film on the semiconductor substrate comprises: receiving a light spectra for a light source, wherein the light source comprises a laser that will be directed to a physical semiconductor substrate during a semiconductor process
(paragraph [0086]) “In another embodiment, the reflection-mode characterization tool 110 is configured to measure intensity spectra (IS) of the sample 104. The intensity spectra may include intensity variation in reflected radiation over a broad spectrum of wavelengths across a film or the sample 104.”; (paragraph [0090]) “The one or more illumination sources 302 may include any illumination source known in the art. For example, the one or more illumination sources 302 may include, but are not limited to, one or more broadband illumination sources or one or more narrowband sources.”; (paragraph [0071]) “For instance, the narrowband radiation source may include, but is not limited to, a laser.”; Figure 3
PNG
media_image2.png
459
611
media_image2.png
Greyscale
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the light spectra of Vukkadala because (Vukkadala paragraph [0003]) “As semiconductor devices become smaller and smaller laterally and extended vertically, it becomes critical to develop enhanced characterization processes with increased sensitivity and throughput” and (Vukkadala paragraph [0088]) “For example, the one or more sets of optics may include any optical element known in the art suitable for focusing, suppressing, filtering, extracting, and/or directing the radiation generated by the illuminator 302 towards the surface of the sample 104.”
Regarding Claim 7
Kwak in view of Fallin and Vukkadala teaches:
The method of claim 6
(see rejection of claim 6)
Kwak further teaches:
wherein simulating the light source being reflected off of the film on the semiconductor substrate further comprises: calculating a reflected spectra from the film that will be captured by a physical camera using thin-film interference formulas
[*Examiner notes: The constructive thin-film interference formula is: mλ=2ndcosθ. The simulation equations below make use of the interference formulas to make the calculations.]
PNG
media_image3.png
445
404
media_image3.png
Greyscale
[…] physical properties of the film, a film thickness at a location based on the film thickness profile, and underlying film properties.
(page 9 column 2 section “Outlier detection methods”) “To simulate the spectroscopic data, the thickness of each layer[*Examiner notes: thickness profile] and the refractive index of each medium were required[*Examiner notes: physical properties of the film, underlying film properties].”
Claims 10, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kwak in view of Fallin, and further in view of NPL reference Kobayashi et al. “Reconstructing Shapes and Appearances of Thin Film Objects Using RGB Images” herein referred to as Kobayashi.
Regarding Claim 10
Kwak in view of Fallin teaches:
The system of claim 9
(see rejection of claim 9)
Kwak in view of Fallin does not explicitly teach:
wherein converting the simulated spectral data into the one or more images comprises: translating the simulated spectral data into RGB pixel values.
However Kobayashi teaches:
wherein converting the simulated spectral data into the one or more images comprises: translating the simulated spectral data into RGB pixel values.
(page 3777 column 2 paragraph 1) “Observed RGB values are represented by integration of observed spectra. The observed spectrum is a multiplication of the camera sensitivity, reflectance, and illumination spectrum in Eq. (9).”
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the labeling of specific locations on the semiconductor substrate taught by Kobayashi because (Kobayashi 3774) “We experimentally evaluated the accuracy of estimated shapes and appearances and found that our proposed method is effective.”
Regarding Claim 12
Kwak in view of Fallin teaches:
The system of claim 9
(see rejection of claim 9)
Kwak in view of Fallin does not explicitly teach:
wherein labeling the one or more images with the film thickness profile comprises: associating an image with a thickness measurement at a specific location on the semiconductor substrate design to generate a training pair for a machine learning model
However, Kobayashi teaches:
wherein labeling the one or more images with the film thickness profile comprises: associating an image with a thickness measurement at a specific location on the semiconductor substrate design to generate a training pair for a machine learning model
(page 3778 column 1 section 5.2 paragraph 2) “First, we store the pixel intensities in the captured image sequence. Second, we detect the maximum and minimum intensities in the captured image sequence and calculate the DOP in each pixel[*Examiner notes: specific location on semiconductor substrate]. Third, using the calculated DOP, we determine two candidates of the zenith angle. Finally, we determine the zenith angle using Section 3.2.”; Table 1; [*Examiner notes: Each pixel is labeled to determine the zenith angle, which is associated with the thickness by table 1, which can be used as a training pair for machine learning (as taught by Kwak)]
PNG
media_image4.png
222
354
media_image4.png
Greyscale
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the labeling of specific locations on the semiconductor substrate taught by Kobayashi because (Kobayashi 3774) “We experimentally evaluated the accuracy of estimated shapes and appearances and found that our proposed method is effective.”
Regarding Claim 14
Kwak in view of Fallin teaches:
The system of claim 9
(see rejection of claim 9)
Kwak further teaches:
wherein a plurality of simulated images are generated from the film thickness profile,
(page 10 column 1 paragraph 2) “Eighteen normal samples were increased to 1,800 augmented data by the noise injection method, and 1,000 simulated data[*Examiner notes: corresponds to a plurality of simulated images generated], which were designed with a relatively large thickness variation in each layer, were generated. When designing the simulated outlier case data, the thickness of the outlier layer was uniformly distributed[*Examiner notes: film thickness profile] within a ±20% variation with respect to the reference thickness, and the thicknesses of the other layers were uniformly distributed within ±4% of the reference thickness.”
Kwak in view of Fallin does not explicitly teach:
wherein each of the plurality of simulated images corresponds to a thickness value in the film thickness profile.
However, Kobayashi teaches:
wherein each of the plurality of simulated images corresponds to a thickness value in the film thickness profile.
(page 3780 column 1) “We proposed a novel method to estimate the shape and appearance of thin film objects using RGB images. By our method, we can determine both the shape and thickness of thin film objects using a regular digital still camera and measure the thin film object easily”
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the labeling of specific locations on the semiconductor substrate taught by Kobayashi because (Kobayashi 3774) “We experimentally evaluated the accuracy of estimated shapes and appearances and found that our proposed method is effective.”
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kwak, Fallin, Kobayashi, and further in view of NPL reference Abed et al. “Reconstruction of reflectance data using an interpolation technique” herein referred to as Abed.
Regarding Claim 11
Kwak in view of Fallin, Kobayashi teaches:
The system of claim 10
(see rejection 10)
Kwak in view of Fallin, Kobayashi does not explicitly teach:
wherein translating the spectral data captured by the camera into the RGB pixel values comprises: using a lookup table that stores RGB pixel values that correspond to received spectral wavelengths for the camera
However, Abed teaches:
wherein translating the simulated spectral data into the RGB pixel values comprises: using a lookup table that stores RGB pixel values that correspond to received spectral wavelengths for the camera
(page 613 abstract) “Hence, different types of lookup tables (LUTs) have been created to connect the colorimetric and spectrophotometeric data as the source and destination spaces in this approach.”
Kwak, Fallin, Abed, and the instant application are analogous because they are all directed to physics of reflections.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the spectral RGB look-up table taught by Abed because (Abed page 613 abstract) “The resultant spectra that have been reconstructed by this technique show considerable improvement in terms of RMS error between the actual and the reconstructed reflectance spectra as well as CIELAB color differences under the other light source in comparison with those obtained from the standard PCA technique.”
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kwak in view of Fallin, and further in view of NPL reference He et al. “A single sensor based multispectral imaging camera using a narrow spectral band color mosaic integrated on the monochrome CMOS image sensor” herein referred to as He.
Regarding Claim 17
Kwak in view of Fallin teaches:
The one or more non-transitory computer-readable media of claim 16
(see rejection of claim 16)
Kwak in view of Fallin does not explicitly teach:
wherein the one or images comprise monochrome images
However, He teaches:
wherein the one or images comprise monochrome images
(He page 041104-1 abstract) “Here, we demonstrate a single sensor based three band multispectral camera using a narrow spectral band red–green–blue color mosaic in a Bayer pattern integrated on a monochrome CMOS sensor.”
Kwak, Fallin, and the instant application are analogous because they are directed to semiconductors and/or optical simulations.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the semiconductor film thickness simulation of Kwak in view of Fallin with the monochrome images of He because (He 041104-1 abstract) “The demonstrated camera technology has reduced cost, weight, size, and power by almost n times (where n is the number of bands) compared to a conventional multispectral camera.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kononchuk et al. (PGPUB no. US20150300809A1) teaches optical simulations dependent on semiconductor thicknesses (see paragraph 144).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ezra J Baker whose telephone number is (703)756-1087. The examiner can normally be reached Monday - Friday 10:00 am - 8:00 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126