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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 22 is objected to because of the following informalities: The limitation “at least one of microscopy method” appear to contain a typographical/clerical error and has been interpreted as “at least one of a plurality of microscopy methods” or “at least one microscopy method.”
Appropriate correction is required.
Claim Interpretation
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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: “[a] neural network unit” and “at least one processing unit”, “ in claim 31.
One of ordinary skill in the art would understand that the neural network unit using a processing unit, i.e., processor, is an algorithm performed on a computer, and therefore the claim recite(s) sufficient structure, materials, or acts to entirely perform the recited function of claim 31.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 and 25-31 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “with a propensity for fabrication deviations”. The limitation renders the claim indefinite because the term “propensity” is relative and/or subjective, not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
A claim that requires the exercise of subjective judgment without restriction renders the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); see also Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1373, 112 USPQ2d 1188 (Fed. Cir. 2014).
For the purpose of further examination, the claim has been interpreted as identifying the structural features with deviations.
Claims 2-17 depend from claim 1 and therefore inherit all of the deficiencies of claim 1 discussed above.
Claims 2-3 further recite the limitation “manufacturing feasibility”. The limitation renders the claims indefinite because the term “feasibility is relative and/or subjective. For the purpose of further examination, the claims have been interpreted as determining manufacturing optimization criterion.
Claim 4 further recites the limitation “at least one of the microscopy method”. There is no antecedent basis for this limitation in the claims. For the purpose of further examination, the limitation has been interpreted as “at least one microscopy method” or “at least one of a plurality of microscopy methods.”
Claim 7 further recites the limitation “a set of diverse generated patterns”. The limitation renders the claim indefinite because the term “diverse” is relative and/or subjective. For the purpose of further examination, the limitation has been interpreted as “a set of different generated patterns.”
Claims 8-11 depend from claim 7 and therefore inherit all of the deficiencies of claim 7 discussed above.
Claim 8 further recites the limitations “or other fabrication technologies” and “(SiN, III-V, metal, polymer)”. The limitations render the claim indefinite because it is not what the “other technologies” correspond to and whether the limitations recited in parenthesis are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of further examination, the claim has been interpreted as using a silicon-on-insulator (SOI) platform using a lithography technique.
Claims 9-11 depend from claim 8 and therefore inherit all of the deficiencies of claim 8 discussed above.
Claim 11 further recites the limitations “roughness”, “narrow”, and “small”. The limitations render the claim indefinite because the terms are relative and/or subjective. For the purpose of further examination, the limitations have been interpreted as “edge contours,” “filling of channels and holes,” and “erosion or loss of features.”
Claim 14 further recites the limitation “the certainty”. There is no antecedent basis for this limitation in the claim. For the purpose of further examination, the limitation has been interpreted as “certainty of the model.”
Claims 15-17 depend form claim 14 and therefore inherit all of the deficiencies of claim 14 discussed above.
Claim 17 further recites the limitation “a smoother and more accurate”. The limitation renders the claim indefinite because the phrase is relative and/or subjective. For the purpose of further examination, the claim has been interpreted as each feature being predicted away from slicing boundaries.
Claim 25 recites the limitation “reasonable feature sizes” and “the degree of filtering”. The limitations render the claim indefinite because the term “reasonable” is relative and/or subjective and there is no antecedent basis for “the degree” in the claim. For the purpose of further examination, the claim has been interpreted as producing reduced feature sizes and using an adaptive method for filtering.
Claims 26-30 depend from claim 25 and therefore inherit all of the deficiencies of claim 25 discussed above.
Claim 31 recites the limitation “useful”, “propensity”, and “desired”. The limitations render the claim indefinite because the terms are relative and/or subjective. For the purpose of further examination, the claim has been interpreted as using the predictive and corrector models for completing tasks, identifying the fabricated devices with deviations, and receiving a layout satisfying a predetermined criterion, e.g. layouts with reduced deviations.
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)(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.
Claim(s) 1-13, 18-24, and 31 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fujimura et al. (US 2022/0128899 A1), hereinafter referred to as Fujimura.
Regarding claim 1, Fujimura teaches a computer-implemented method comprising the steps of:
with an imaging device, acquiring a plurality of images of at least one fabricated structure associated with a device, wherein the at least one fabricated structure comprises structural features (Fujimura ¶¶0041: “FIGS. 3A-3F illustrates some various types of shots”; Fujimura ¶¶0058: “a composite of substrate layers, some of which are separated into mask layers, may be created from the physical design”; Fujimura ¶¶0066: “In FIG. 8 an image 800 representing a physical design or CAD data is provided as input to a CNN 810, such as a FCN, and image 820 representing manufactured output shapes is generated by CNN 810”; Fujimura ¶¶0090: “the input image may consist of two channels”; Fujimura Fig. 9);
preprocessing the plurality of images (Fujimura ¶¶0120: “A sequential process for deep learning comprises: loading/preprocessing data, and fitting a model to make predictions”);
creating at least one image dataset from the preprocessed plurality of images (Fujimura ¶¶0139: “the creation of a sufficient volume of the input image data (CAD data) may be relatively quick” );
generating a predictor model (Fujimura ¶¶0072: “the main neural network architecture for a FCN is essentially an encoder-decoder network as illustrated in FIG. 9, in which the encoding side on the left side and bottleneck layer 910 guide the model to learn a low dimensional encoding of the input image 900”);
training the predictor model with the at least one image dataset to identify the structural features of the device with a propensity for fabrication deviations (Fujimura ¶¶0074: “A set of model weights are determined and after training of the FCN on semiconductor manufacturing image data”; Fujimura ¶¶0081: “The mean image 1303 may be computed by taking the per-pixel sum divided by number of process corners, or pixel-wise mean across all per-corner output images …The more white pixels are present, the more susceptible is the design to manufacturing process variation”; Fujimura ¶¶0083: “in order to compute metrics that represent a design/process combination's susceptibility or immunity to process variations, it may also be desirable to produce two additional images”; Fujimura ¶¶0085: “The intent is for a single, trained multiple-output network 1501 to produce an image corresponding to the manufactured output for each of the individual process corners 1502”).
Regarding claim 2, Fujimura teaches the computer-implemented method of claim 1, comprising a further step of determining manufacturing feasibility of the device based on the predicted fabrication deviations (Fujimura ¶¶0074 , ¶¶0081, ¶¶0083 discussed above; Fujimura ¶¶0052: “The amount of the size variation is an essential manufacturing optimization criterion … edge slope, or dose margin, is a critical optimization factor for particle beam writing of surfaces. In this disclosure, edge slope and dose margin are terms that are used interchangeably”; Fujimura ¶¶0056: “curvilinear patterns are extremely compute-intensive, and thus being able to optimize patterns by calculating the cumulative effects of variations from multiple manufacturing stages as in the present embodiments is extremely valuable”; Fujimura ¶¶0058: “OPC may be performed on the physical design pattern to produce a plurality of possible mask designs 710”; Fujimura ¶¶0061: “a VSB shot list or exposure information for multi-beam may be generated to produce a plurality of possible mask images 718 from the possible mask designs 710”).
Regarding claim 3, Fujimura teaches the computer-implemented method of claim 1, comprising a further step of determining manufacturing feasibility of the device based on the predicted fabrication uncertainties (Fujimura ¶¶0074 , ¶¶0081, ¶¶0083 discussed above; Fujimura ¶¶0081: “each white pixel represents a region of uncertainty due to process variation”; Fujimura ¶¶0097: “Let VB (Variation Band) be the number of white pixels in the variation band plot which can serve as an upper bound on the uncertainty associated with metal deposits due to process variations”; Fujimura ¶¶0106: “Let VBI=VB/TP2 … the numerator VB still contains an uncertainty term, the amount of pixels for which the manufacturing output is uncertain. VB is then normalized by the denominator TP2, the amount of pixels for which metal can be realistically expected, on average, across the process variations”; Fujimura ¶¶0107: “A second measure VBI′=VB/TP serves as the ratio of the manufacturing uncertainty to the originally drawn number of white pixels (expected result in an unrealistic, but ideal manufacturing scenario)”).
Regarding claim 4, Fujimura teaches the computer-implemented method of claim 1, wherein the imaging device comprises at least one of the microscopy method (Fujimura Fig. 2 & ¶¶0011: “FIG. 2 illustrates an example of an electro-optical schematic diagram of a multi-beam exposure system, as known in the art”; Fujimura Fig. 4 & ¶¶0018: “FIG. 4 illustrates an example of a multi-beam charged particle beam system, as known in the art”).
Regarding claim 5, Fujimura teaches the computer-implemented method of claim 4, wherein the predictor model comprises at least one of a machine learning method (Fujimura ¶¶0065: “A neural network is a framework of machine learning algorithms that work together to predict patterns based on a previous training process”).
Regarding claim 6, Fujimura teaches the computer-implemented method of claim 5, wherein the at least one of a machine learning method is trained on image examples obtained from a plurality of input layout images associated with a structure for fabrication, and their corresponding acquired images to learn a relationship between the plurality of input layout images and the corresponding acquired images (Fujimura Fig. 9 & ¶¶0041, ¶¶0058, ¶¶0066, ¶¶0090 discussed above; also see Fujimura Figs. 10-12).
Regarding claim 7, Fujimura teaches the computer-implemented method of claim 6, wherein at least one image dataset comprises a set of diverse generated patterns pertaining to fabrication features (Fujimura Fig. 3 discussed above; also see Fujimura Fig. 10-16; Fujimura ¶¶0111-¶¶0112: “The CNN architecture convolves learned features with input data, and typically uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images …The CNN works by extracting features directly from images. The relevant features are not pre-trained …This automated feature extraction makes deep learning models highly accurate for general computer vision tasks such as object classification, and for semiconductor manufacturing image-to-image transformation tasks such as in the present invention”).
Regarding claim 8, Fujimura teaches the computer-implemented method of claim 7, wherein the generated patterns are fabricate don a silicon-on-insulator (SOI) platforms or other fabrication technologies (SiN, III-V, metal, polymer) using a lithography technique (Fujimura ¶¶0036: “silicon wafer”; Fujimura ¶¶0063: “In a substrate simulation step 720, calculating possible substrate patterns 722 may comprise lithography simulation using the calculated mask images 718”; Fujimura ¶¶0082: “a metal manufacturing step”).
Regarding claim 9, Fujimura teaches the computer-implemented method of claim 8, wherein the device comprises at least one of a photonic device, an electronic or a radio-wave device (Fujimura ¶¶0038: “electron beam”; Fujimura ¶¶0044: “Multi-beam system 400 has an electron beam source 402 that creates an electron beam 404”).
Regarding claim 10, Fujimura teaches the computer-implemented method of claim 9, wherein the features comprise at least one of edges, corners, circles, and curves (Fujimura Figs. 8-16).
Regarding claim 11, Fujimura teaches the computer-implemented method of claim 9, wherein the fabrication deviations comprise at least one of over/under-etching, depth of etch material layer thicknesses, roughness of edges, concentrations of impurities, corner rounding, filling of narrow channels and holes, erosion or loss of small features, over-etched convex bends, and under-etched concave bends (Fujimura ¶¶0052: “the effects of a high CD variation may be observed as line edge roughness (LER)”; Fujimura ¶¶0078: “Though similar at first glance, it is apparent upon closer inspection that the three images are different, for example a different amount of corner rounding is apparent in each”).
Regarding claim 12, Fujimura teaches the computer-implemented method of claim 5, wherein the at least one machine learning method performs feature extraction from the at least one image dataset (Fujimura ¶¶0111-¶¶0112 discussed above).
Regarding claim 13, Fujimura teaches the computer-implemented method of claim 12, wherein the model comprises 2D convolutional layers to detect the features in the input layer images and relate them to transformed output acquired images (Fujimura ¶¶0111-¶¶0112 discussed above).
Regarding claim 18, Fujimura teaches a computer-implemented method comprising the steps of:
with an imaging device, acquiring a plurality of images of at least one fabricated structure associated with a device, wherein the at least one fabricated structure comprises structural features (Fujimura ¶¶0041, ¶¶0058, ¶¶0066, ¶¶0090 & Fig. 9 discussed above);
preprocessing the plurality of images (Fujimura ¶¶0120 discussed above);
creating eat least one image dataset from the preprocessed plurality of images (Fujimura ¶¶0139 discussed above);
generating a corrector model (Fujimura ¶¶0074, ¶¶0081, ¶¶0083, ¶¶0085 discussed above);
training the corrector model with the at least one image dataset to automatically correct the device design to minimize the impact of fabrication deviations (Fujimura Abstract: “training the neural network with the calculated plurality of patterns, and adjusting the set of parameters to reduce the manufacturing variation for the calculated plurality of patterns to be manufactured on a substrate”; Fujimura ¶¶0091: “Methods also include training (e.g., in the loop from step 725 to physical design 702) the neural network with the calculated plurality of patterns, where the training is performed using a computing hardware processor; and adjusting the set of parameters (e.g., in step 725) to reduce manufacturing variation for the calculated plurality of patterns to be manufactured on the substrate”).
Regarding claim 19, Fujimura teaches the computer-implemented method of claim 18, wherein the corrector model comprises at least an inverse model that learns an inverse translation from fabrication design whereby a nominal design having a desired fabrication outcome is inputted, and a corrected design is outputted (Fujimura ¶¶0036: “Inverse Lithography Technology (ILT) is one type of OPC technique”; Fujimura ¶¶0052: “CD variation is, among other things, inversely related to the slope of the dosage curve at the resist threshold, which is called edge slope. Therefore, edge slope, or dose margin, is a critical optimization factor for particle beam writing of surfaces. In this disclosure, edge slope and dose margin are terms that are used interchangeably”; Fujimura ¶¶0065: “adjusting a set of parameters for the neural network to reduce manufacturing variation for the calculated plurality of patterns, as part of the process of training the neural network … transform a physical design pattern to a pattern to be manufactured on the substrate”).
Regarding claim 20, Fujimura teaches the computer-implemented method of claim 18, wherein the data preprocessing step matches each acquired image to a corresponding design image associated with a structure for fabrication by at least one of resizing, aligning, and binarizing (Fujimura Fig. 7: 702- 716; Fujimura ¶¶0082: “Image thresholding compares each pixel value to a predetermined threshold value (e.g., 0.5), such that pixel values above the threshold value are converted to white (1.0), while those below the threshold value are converted to black (0.0)”; Fujimura Figs. 8-16).
Regarding claim 21, Fujimura teaches the computer-implemented method of claim 18, wherein the corrector model comprises at least one machine learning model, wherein the at least one machine learning model is trained on image examples obtained from a plurality of layout images associated with a structure for fabrication and their corresponding acquired images to learn a relationship between the plurality of layout images and the corresponding acquired images (Fujimura ¶¶0065 discussed above; Fujimura Abstract: “Methods for calculating a pattern to be manufactured on a substrate include inputting a physical design pattern, determining a plurality of possible neighborhoods for the physical design pattern, generating a plurality of possible mask designs for the physical design pattern, calculating a plurality of possible patterns on the substrate, calculating a variation band from the plurality of possible patterns, and modifying the physical design pattern to reduce the variation band”; Fujimura Figs. 8-16).
Regarding claim 22, Fujimura teaches the computer-implemented method of claim 18, wherein the imaging device comprises at least one of microscopy method (Fujimura Fig. 2 & ¶¶0011: “FIG. 2 illustrates an example of an electro-optical schematic diagram of a multi-beam exposure system, as known in the art”; Fujimura Fig. 4 & ¶¶0018: “FIG. 4 illustrates an example of a multi-beam charged particle beam system, as known in the art”).
Regarding claim 23, Fujimura teaches the computer-implemented method of claim 21, wherein the corrector model comprises a tandem model connecting a pretrained forward model to the end of a to-be-trained inverse model (Fujimura ¶¶0036, ¶¶0052, ¶¶0065 discussed above; Fujimura Figs. 9-10, 12; Fujimura ¶¶0077: “multiple sets of process conditions may be represented via multiple copies of the single-output network as shown in FIG. 10, with one network per unique set of process conditions. Each of these single-output networks 1001, 1002 through 1010, may be trained in parallel”).
Regarding claim 24, Fujimura teaches the computer-implemented method of claim 23, wherein an output of the tandem model is a prediction of a correction and is compared to the corresponding input in the training process (Fujimura Figs. 9-10, 12, ¶¶0036, ¶¶0052, ¶¶0065, ¶¶0077 discussed above).
Regarding claim 31, Fujimura teaches a neural network unit comprising:
at least one processing unit (Fujimura ¶¶0065: “using a computing hardware processor”); and
a non-transitory memory communicatively coupled to the at least one processing unit and comprising computer-readable program instructions that when executed by the at least one processing unit (Fujimura ¶¶0140: “Computing hardware device 1700 comprises a central processing unit (CPU) 1702, with attached main memory 1704”), cause the neural network to perform operations including the steps described in claims 1and 18.
Fujimura further teaches predicting planar fabrication deviations in silicon photonic devices (Fujimura Figs. 7-16 & ¶¶0036, ¶¶0074, ¶¶0081, ¶¶0083, ¶¶0085 discussed above);
predicting fabrication deviations validating pre-fabrication correction (Fujimura Abstract, ¶¶0074, ¶¶0081, ¶¶0083, ¶¶0085, ¶¶0091 discussed above);
minimizing the impact of fabrication deviations (Fujimura Abstract, ¶¶0091 discussed above);
wherein the corrector model is useful for completing tasks comprising of at least: receiving a desired layout as input and generating a layout output, and automatically correcting the device design to minimize fabrication deviations (Fujimura Abstract, ¶¶0091 discussed above).
Therefore, claim 31 is rejected using the same rationale as applied to claims 1 and 18 discussed above.
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) 25-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujimura et al. (US 2022/0128899 A1), in view of Torunoglu et al. (US 8,490,034 B1), hereinafter referred to as Fujimura and Torunoglu, respectively.
Regarding claim 25, Fujimura teaches the computer-implemented method of claim 23, wherein the tandem model comprises a binarization layer to produce binarized designs with reasonable feature sizes, wherein the level of binarization and the degree of filtering is fine-tuned for further optimization (Fujimura Fig. 9, ¶¶0052, ¶¶0056, ¶¶0082 discussed above).
However, Fujimura does not appear to explicitly teach using a low-pass filter.
Pertaining to the same field of endeavor, Torunoglu teaches using a low-pass filter (Torunoglu col. 11 lines 50-52: “The low-pass filtering effects of the process, such as the lens system, etching characteristics, and so forth, are introduced”; Torunoglu col. 11 lines 53-55: “An inverse filtering process is applied to compensate for the low-pass filtering effects introduced in the previous step”).
Fujimura and Torunoglu are considered to be analogous art because they are directed to image processing for correcting fabricated devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and systems for determining shapes for semiconductor or flat panel display fabrication (as taught by Fujimura) to use a low-pass filter (as taught by Torunoglu) because the combination can filter out unwanted signals for optimizing the process (Torunoglu col. 30 lines 25-31).
Regarding claim 26, Fujimura, in view of Torunoglu, teaches the computer-implemented method of claim 25, wherein the tandem model comprises an ensemble model having a collection of identically structured forward models that are trained with different random weight initializations, thereby minimizing bias (Note that no patentable distinction is made by an intended use or result limitations unless some structural difference is imposed by the use or result on the structure or material recited in the claim. Fujimura Figs. 9-10, 12; Fujimura ¶¶0077 discussed above; Fujimura ¶¶0133: “by combining multiple deep learned models into an ensemble. This is a direct extension from the iterative process needed to fit those models. A common form of creating an ensemble is averaging the predictions from multiple trained models. There are more advanced ways to combine multiple models, but the iteration needed to fit multiple models is the same. Determining an appropriate combination/ensemble for each of the various deep learned models is an iterative process”).
Regarding claim 28, Fujimura, in view of Torunoglu, teaches the computer-implemented method of claim 26, wherein a full device design is corrected by making a plurality of smaller corrections and stitching them together (Torunoglu col. 10 lines 46-52: “The optical proximity correction calculation procedures may combine results by stitching together the results by removing the overlapping regions”; Torunoglu col. 18 lines 40-50: “The results of the calculation or calculations are collected (such as at a single node) where the information is stitched together by removing the overlapping regions. Stitching may be performed using a single node or multiple nodes in parallel”; Torunoglu Fig. 4.3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and systems for determining shapes for semiconductor or flat panel display fabrication (as taught by Fujimura) to stitch together smaller corrections (as taught by Torunoglu) because the combination can split up larger dataset into subregions and remove overlapping regions (Torunoglu col. 18 lines 40-50).
Regarding claim 29, Fujimura, in view of Torunoglu, teaches the computer-implemented method of claim 28, wherein an ensemble of identically structured tandem models, with different random initializations of the weights, are used to further reduce training bias and increase overall correction accuracy (Note that no patentable distinction is made by an intended use or result limitations unless some structural difference is imposed by the use or result on the structure or material recited in the claim. Torunoglu col. 30 lines 32-44: “To account for effects of process conditions during the optimization, error (fitness) of mask M is computed in four focus-exposure conditions and at nominal (ideal) conditions and their effect is combined with different weights”; Torunoglu col. 55 lines 18-29: “to account for the effects of process conditions during the optimization, we compute error (e.g., fitness) of the mask, M, in four focus-exposure conditions and at nominal (ideal) conditions and combine their effect with different weights”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and systems for determining shapes for semiconductor or flat panel display fabrication (as taught by Fujimura) to use different weights (as taught by Torunoglu) because the combination can account for the effects of process conditions (Torunoglu col. 55 lines 18-29).
Claim(s) 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujimura et al. (US 2022/0128899 A1), in view of Torunoglu et al. (US 8,490,034 B1), and further in view of Izumi et al. (US 2021/0277261 A1), hereinafter referred to as Fujimura, Torunoglu, and Izumi, respectively.
Regarding claim 30, Fujimura, in view of Torunoglu, teaches the computer-implemented method of claim 28, but does not appear to explicitly teach that the device design is automatically corrected by adding silicon at locations that are predicted to have an insufficient amount of silicon, and removing silicon for locations that are predicted to have an excess amount of silicon.
Pertaining to the same field of endeavor, Izumi teaches that the device design is automatically corrected by adding silicon at locations that are predicted to have an insufficient amount of silicon, and removing silicon for locations that are predicted to have an excess amount of silicon (Izumi ¶¶0096: “The amount of addition of the surfactant depends on the type of the surfactant, or the type and amount of the silicon compound … the amount of addition can be adjusted to 1 to 100 parts by mass with respect to 100 parts by mass of the total amount”; Izumi ¶¶0168: “this coating film can be applied to the use application as a … semiconductor”).
Fujimura, in view of Torunoglu, and Izumi are considered to be analogous art because they are directed to fabricating device materials. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods and systems for determining shapes for semiconductor or flat panel display fabrication (as taught by Fujimura, in view of Torunoglu) to adjust the amount of silicon (as taught by Izumi) because the combination can produce a proper ratio of materials based on the type of surfactant (Izumi ¶¶0096).
Allowable Subject Matter
Claims 14-17 and 27 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 14, the prior art of record teaches that it was known to use the computer-implemented method of claim 5 before the application was filed.
However, the prior art, alone or in combination, does not appear to teach or suggest, wherein a single raw prediction is outputted at a final, fully connected output layer of a neural network structure, wherein at a raw output, each predicted pixel is at least one of a first value associated with silicon, a second value associated with silica, and an intermediate value between silicon and silica based on the certainty of the model.
Claims 15-17 are objected to for the same reason as claim 14 due to dependency.
Regarding claim 27, the prior art of record teaches that it was known to use the computer-implemented method of claim 26 before the application was filed.
The prior art further teaches that wherein the networks are trained with a binary cross-entropy (BCE) loss function, wherein for each pixel of an inputted image, (Fujimura Figs. 8-16 & ¶¶0082 discussed above; Fujimura ¶¶0127: “’Loss’ may be a metric that quantifies the cost of wrong prediction, such as mean squared error, mean absolute error, cross entropy, etc.”); and
training the neural network with an adaptive moment estimation method (Adam), wherein for each pixel of an inputted SEM image (Wang pg. 23: “In order to realize a high-precision multiplication matrix, the relative permittivity distribution of a specific 4x4 OSU was optimized by an adjoint-based training process (i.e., Adam optimizer)”; Wang Fig. 7: “SEM image”).
However, the prior art, alone or in combination, does not appear to teach or suggest that the corrector model classifies the probability of the corresponding pixel of the correction being silicon or silica.
Note that the examiner’s statement of reasons for indicating allowable subject matter applies to the claims only as interpreted by the examiner due to the 35 U.S.C. 112(b) issues. The statement may no longer apply if future amendments change the scope of the claims.
Conclusion
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
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