DETAILED ACTION
This Office Action is responsive to the amended claims filed on April 1, 2026.
Claims 1-4, 6-17, and 21-27 are under examination. Claims 5 and 18-20 are canceled. New claims 25-27 are added.
Claims 1-4, 6-17, and 21-27 are rejected under 35 USC 112(a) for reciting new matter.
Claims 1-4, 6-17, and 21-27 are rejected under 35 USC 112(b) as indefinite.
Claims 1-4, 6-17, and 21-27 are rejected under 35 USC 101 as ineligible.
Claims 1-4, 6-17, and 21-27 are rejected under 35 USC 102 as anticipated by Li.
Response To Arguments/Amendments
35 USC 112 – The Amendments and arguments appear to overcome most of the existing 35 USC 112 issues. The ones that remain will be addressed in the order presented in the Applicant’s response.
With regard to the “Using the thick mask model [with the modified pattern] [as an input to the thick mask model]” feature rejection, the Applicant’s arguments are not persuasive. The current punctuation still lends itself to both interpretations, confounding the potential scope of the claim. The Applicant is expected to clarify this with appropriate punctuation or wording.
35 USC 101 – The amendments and arguments are not persuasive. The arguments presented will be addressed in the order they were presented in the response.
Research Corp. – In Research Corp., the claims made a novel data processing method for processing image data using a specialized and inventive approach to constructing and using a known data structure, the blue noise mask, at least according to the court. Unlike the claims in research corp., the Applicant’s use of filters or other smoothing functions to simplify the math used in determining the alternative representations with less data are all longstanding mathematical practices that extend back to the eighteenth century and are used in numerous imaging techniques. Including those in wafer production. Any determination otherwise would make eligibly any conventional use of filters on data, which is not the holding of Research Corp. Contrary to the assertion of the Applicant, the issue is not whether the use of filters renders a claim ineligible. The question is whether the use of conventional filters renders a claim eligible. It clearly does not. The analogy to the claims in Research Corp. is insufficient, so the argument is not persuasive. The Applicant’s claims use longstanding and conventional methods to accomplish longstanding and conventional results, for longstanding and conventional purposes. Research Corp. does not apply to the Applicant’s claims.
Mental Process – The Applicant argues that the operations of the claim require advanced calculations that a person cannot allegedly accomplish manually. As an aside, this essentially is an express admission that the improvement is mathematical in nature, and improvements to math are per se abstract ideas that cannot, without additional limitations, confer eligibility. That aside, the claims do not require any particular methods of smoothing or simulation be applied. That is, the smoothing could be accomplished by simply removing points that appear to be sharp (which makes the sharp, high-frequency data lower frequency). There are several numerical methods including in the scope of the claim limitations that could practically be performed mentally, or with the aid of pen, paper, and/or a calculator. Even if, arguendo, the Applicant were to limit the claim by amendment to methods that would take (even with simplifying assumptions and heuristics) thousands of years, the court in Recentive Analytics specifically ruled,
Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”); compare McRo, 837 F.3d at 1314– 16 (finding eligibility of claims to use specific computer techniques different from those humans use on their own to produce natural-seeming lip motion for speech).
The test of whether operations are mental processes, that is, whether the processes are practically performable in the mind or with the aid of pen and paper, is not an inquiry of whether it is practical for a single person to conduct the calculations, even within a lifetime of effort. The question turns more on whether a computer is necessary to accomplish the features of the operations. For example, a person cannot transmit network packets over a network mentally or manually with the simple aids of pen, paper and a calculator (obviously, a calculator without network functionality). In this sense, transmitting network packets is not merely automating an otherwise manual task. By contrast, the Applicant’s claims do math that, given sufficient time, one or more humans could conduct manually. For these reasons, the Applicant’s arguments with regard to mental processes are not persuasive.
Cases Too Close To Call – The Applicant asserts that the claims so clearly border on eligible that it is too close to call. When assessing the Applicant’s claims as whole, the claims merely use longstanding practices to reduce the sharpness and complexity of data for easier analysis. This is a longstanding problem that has been conventionally addressed in the same manner as it has been in other context. The use of a modified mask in a known simulation method is merely applying the modified mask, modified by conventional methods, to the only application for which a mask is suitable. Rather than integrating the abstract idea into a practical application, this demonstrates that the allegedly inventive solution is contained entirely within the abstract idea, with the simulation being either a secondary mental process (e.g., another abstract idea) or insignificant extra-solution activity and well-understood, routine, and conventional activity. The reason it would be considered insignificant extra-solution activity is addressed in MPEP 2106.05(g), which generically categorizes insignificant extra-solution activity as activity that is well-known and significant. Here, the use of the model in simulation of its only application is well-known and does not significantly distinguish the abstract idea from conventional simulations that have been conducted in the manner of the specification and in the manner of the references of record for a long time. It is for this reason that the simulation also qualifies as a mere “apply it” step under MPEP 2106.05(f) and well-understood, routine and conventional activity under MPEP 2106.05(d) based on the references of record that recognize the conventionality of the simulation methods once a thick mask is determined. Further, because the use of the allegedly inventively determined thick mask for wafer fabrication or simulation thereof has no other purpose, there is even an argument that the simulation itself does nothing more than limit the abstract idea to a particular field under MPEP 2106.05(h). Because the improvement is conferred entirely by the identified abstract idea, and because the simulation and any other additional limitations fail to confer eligibility under MPEP 2106.05(f), MPEP 2106.05(g), 2106.05(d), and 2106.05(h), the Applicant’s arguments are not persuasive, and the claims are ineligible.
35 USC 102 – The Applicant’s amendments and arguments have been considered but are not persuasive. The Applicant’s arguments will be addressed in the order presented in the Applicant’s response.
The Li Reference Allegedly Does not Modify A Pattern – The Applicant attempts to provide a narrow interpretation of the claims. That is, the Applicant attempts to distinguish Li’s thin mask from a pattern. However, the broadest reasonable interpretation of a thin mask model is a 2D image that can be reasonably characterized as a patter or, at the very least, a 2D image that includes a pattern. The claim merely states that a pattern that is used as a source of a thick mask model is modified. There is nothing in the claim that would exclude the pattern that Li’s thin mask is, or, at the very least includes. For this reason, the Applicant’s argument is not persuasive. Therefore, even considering the Applicant’s amendments, the rejection is maintained.
Claim Rejections - 35 USC § 112
35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-17, and 21-27 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Subset
Specifically, independent claims 1, 13, and 15 recite “wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern.” However, the specification is silent as to a pattern being “a subset” of another pattern. This feature presents new matter, so the Applicant is advised to remove the new matter by amendment, without introducing further new matter.
Predicted
The independent claims also recite, “predicted by the mask model.” However, the mask model does not predict anything. According to the specification, the lithography simulation is the only element that predicts anything. In fact, the thick mask is not even a direct input into the simulation it appears. The closest input into the simulation appears to be the design layout model 35. The specification is silent as to how the thick mask model is reflected in, or otherwise leads to, the design layout model 35 for the lithography simulation in FIG. 2 (e.g., design layout model 35 relative to the patterning device/mask MA). Accordingly, this is new matter and must be removed.
Generating a thick mask transmission function
Claims 25-27 recite, “generating a thick mask transmission function based on modelling electromagnetic field interaction with finite mask topography for the modified target pattern, the generating comprising applying one or more edge filters to edges and/or contours of the modified target pattern, the number of edge filters being reduced relative to a number that would have been applied absent the modifying of the target pattern.” However, the specification is silent as to subsequent modification of edges or vertices after the original modification of the design that is the input to the thick mask model. Accordingly, this is new matter and must be removed.
Dependent claims that depend from the rejected claims are rejected based on the dependence.
35 USC § 112(b)
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 1-4, 6-17, and 21-27 are 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.
A Subset of the Pattern
The independent claims recite, “wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern.” It is unclear what this means. How can a pattern include a subset of a pattern? The Applicant’s specification paragraph [0059] states, “The GDS may be treated as an image, and a graphical low pass filter may be applied to that image. This may be, for example, implemented by averaging nearby pixels in the image. The averaging may be a straight average (e.g., a matrix that is an array of ones divided by the number of elements within the kernel), or a weighted average, where pixels further from the center have a different weight from those near the edges. In either case, higher frequency information in the image is reduced, while low frequency information is maintained. The filtered GDS image can then be subjected to a thresholding operation to obtain a filtered GDS contour.” According to the Applicant’s specification, the target pattern is different from, and not a subset of, an unmodified pattern. The averaging of points makes an entirely different contour with different points, so the averaged points that remain cannot be considered to be a subset of the original points. This means that the unmodified pattern and the target pattern are not subsets of one another in any way. They are completely different.
This also creates problems for the independent claim limitation, “generating a modified pattern that includes the target pattern with the higher frequency information removed.” By the terms of the claim the pattern includes the target pattern and a plurality of edges and vertices. This means that the target pattern is already a separate element of the plurality of edges and vertices. Therefore, the plurality of edges and vertices cannot be removed (already not elements of the target pattern) for the limitation, “generating a modified pattern that includes the target pattern with the higher frequency information removed.” This would mean that the target pattern would remain unmodified, defeating the purpose of those limitations. This wording does not make sense and needs to be corrected, without the introduction of new matter. For purposes of examination, the limitation, “wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern” will be interpreted to mean that the target pattern is an initial pattern that includes the plurality of edges and vertices, and the “generating a modified pattern that includes the target pattern with the higher frequency information removed” will be interpreted to mean that the modified pattern includes a modified target pattern with the higher frequency information removed.
Copy and Paste Error?
Claim 13 recites, “performing smoothing of the target pattern to reduce higher frequency information in the target pattern by changing or removing one or more of the edges and vertices an edge or a portion of a to change the shape of the target pattern […].” This does not make sense. It appears the Applicant intended to make a copy and paste parallel amendment among the independent claims but fell a bit short for claim 13. For purposes of examination, the features of claim 13 will be interpreted as if the amendments were parallel between the independent claims. Appropriate correction is still required.
“Using the thick mask model [with the modified pattern] [as an input to the thick mask model]”
Claims 1, 13, and 15 recite, “using the thick mask model with the modified pattern as input to the thick mask model” The claim is unclear as to what the “as an input to the thick mask model” qualifies. For example, the thick mask model with the pattern as input could be input into the thick mask model. Alternatively, this could be merely stating that the thick mask model is one that has already been modified based on the modified pattern, and that it is not used as an input to the same or another thick mask model. For purposes of examination, the latter interpretation will be applied to the feature. That is, this feature will be interpreted as only one thick mask, the thick mask has previously been modified based on the modified pattern, and that the resulting thick mask that is altered based on the modified pattern is not used as input to the same or another mask model.
PRODUCT AND PROCESS IN THE SAME CLAIM
Claim 26 is an apparatus claim, but the limitations of claim 26 are method steps. This is a violation of 35 USC 112(b) under MPEP 2173.05(p). This appears to be a typo, so the claim will be interpreted as if the recited operations were intended to e claimed as operations of the recited hardware.
Dependent claims that depend from rejected claims are rejected based on the dependence.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Independent Claims
Claim 13 (Statutory Category – Machine)
(NOTE TO APPLICANT: SEE IF THERE REMAINS A 35 USC 112(b) REJECTION WHERE CLAIM 13 IS BEING INTERPRETED AS IF IT WAS INTENDED TO BE A PARALLEL AMENDMENT WITH THE OTHER INDEPENDENT CLAIMS)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 13 recites (claim features in italics, paragraph references are to the Applicant’s specification):
[…]
modify the pattern to improve image prediction by a thick mask model, the modifying comprising: performing smoothing of the target pattern to reduce higher frequency information in the target pattern by changing or removing one or more of the edges and vertices an edge or a portion of a to change the shape of the target pattern, and generating a modified pattern that includes the target pattern with the higher frequency information removed; and (evaluation, mathematical operation: [0059] “The GDS may be treated as an image, and a graphical low pass filter may be applied to that image. This may be, for example, implemented by averaging nearby pixels in the image. The averaging may be a straight average (e.g., a matrix that is an array of ones divided by the number of elements within the kernel), or a weighted average, where pixels further from the center have a different weight from those near the edges. In either case, higher frequency information in the image is reduced, while low frequency information is maintained.”; The use of B-Splines or filters to smooth edges and reduce dimensionality/computational complexity was done before the use of computers to automate them. This is an evaluation of mathematical operations on a model that is a set of mathematical relationships. These operations, including performing calculations to modify an image, are practically performable in the mind or with the aid of pen, paper, and a calculator. Therefore, the features are mental processes and mathematical processes, abstract ideas.)
computer simulate, using the thick mask model with the modified pattern as an input to the thick mask model, an image of the modified pattern, predicted by the thick mask model.
(evaluation, mathematical operation: [0031] “To simulate the near field due to mask thickness and topographical structures, a first principle is to solve Maxwell's equations. Due to a complicated shape of the light source and complex boundary conditions defined by mask patterns, the Maxwell's equations can typically be solved numerically only.”; [0044] “In an Abbe model the specifics of the inputs source model 31, projection optics model 32 and design layout model 35 form the inputs, and a complete computation of the resulting resist pattern can be made. In a Hopkins model, the design layout model 35 is an input into a combined model incorporating the source model 31 and the projection optics model 32, such that a majority of the computations in the modeling reside in the simulation of the optics and source (31 and 32) such that the computational load is largely pattern independent.” - This is an evaluation of mathematical operations on a model that is a set of mathematical relationships. These operations, including performing calculations to render an image, are practically performable in the mind or with the aid of pen, paper, and a calculator. Therefore, the features are mental processes and mathematical processes, abstract ideas.)
The modify and simulate steps of claim 13 are elements of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. ([0044], [0059]) Further, the modify and simulate steps of claim 13 as described in the claim and specification include and/or are expressed as mathematical calculations or mathematical relationships, which are mathematical concepts. ([0044], [0059]). Being a mental process and a mathematical concept, the modify and simulate steps are an abstract idea.
Claim 13 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional limitations:
A system for simulating a pattern to be imaged onto a substrate using a photolithography system, the system comprising:
a memory for storing […]; and
a processor configured and arranged to:
The claimed system includes a generic memory and a processor with no specific system alterations to execute the claimed steps. The computer implementation is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and, under MPEP 2106.05(f) does not integrate the abstract idea into a practical application at Step 2A Prong Two.
[…]a pattern to be imaged onto the substrate, wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern;
The storing merely gathers existing information (respective candidate traces) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g), The storing adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application at Step 2A Prong Two.
Should it be found otherwise, this feature merely limits the abstract idea to a field of technology, which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application at Step 2A Prong Two.
Claim 13 fails to provide an additional limitation that integrates the abstract idea into a practical application.
Claim 13 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
The additional limitations:
A system for simulating a pattern to be imaged onto a substrate using a photolithography system, the system comprising:
a memory configured for storing […]; and
a processor configured and arranged to:
The claimed system includes a generic memory and a processor with no specific system alterations to execute the claimed steps. The computer implementation is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, the computer implementation implements the recited abstract idea on a generic computer, and, under MPEP 2106.05(f) fails to combine with the other elements of the claim to provide significantly more, and, therefore, fails to confer an inventive concept.
[…]storing a pattern to be imaged onto the substrate, wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern;
This storing is storing and retrieving information from memory and also indicative of sending or reciting data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) representing well-understood, routine, and conventional functions.
Further, should it be found otherwise, this limitation merely limits the abstract idea to a technological field, which, under MPEP 2106.05(h) fails to combine with the other elements of the claim to provide significantly more than the abstract idea.
The additional limitation of the storing is insignificant extra-solution activity (as illustrated under Step 2A Prong 2), merely limits the abstract idea to a field and is a well-understood, routine, and conventional function. Therefore, none of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(g) and MPEP 2106.05(d) respectively.
Therefore, there are no additional limitations in claim 13 that furnish claim 13 with an inventive concept to ensure that claim 13, as a whole, amounts to significantly more than the bolded abstract idea.
Claim 13 is ineligible.
Claim 1 (Statutory Category – Process)
Claim 1 recites the same method steps as the steps in claim 13 that represent the abstract idea. Clam 1 also affirmatively claims an obtaining step that is treated the same way as the storing in claim 13 under the eligibility analysis. Claim 1 also recites that the simulating step is performed “by a hardware computer system,” but this is treated the same way as the generic processor and memory in claim 13 for purposes of subject matter eligibility. Accordingly, claim 1 recites an abstract idea and fails to recite any additional limitations that would render claim eligible under Step 2A, Prong 2 or Step 2B for at least the same reasons as claim 13.
Claim 1 is ineligible.
Claim 15 (Statutory Category – Machine)
Claim 15 recites a CRM for conducting the method steps recited in claim 13 that are the abstract idea. The CRM is a generic memory device and is treated the same way as the memory in claim 13. Clam 15 also affirmatively claims an obtain step that is treated the same way as the storing in claim 13 under the eligibility analysis. Accordingly, claim 15 recites an abstract idea and fails to recite any additional limitations that would render claim elibigle under Step 2A, Prong 2 or Step 2B for at least the same reasons as claim 13.
Claim 15 is ineligible.
Dependent Claims
Dependent claims 2-12, 14, and 16-20 are also ineligible for the following reasons.
Claims 2, 14, 16
Claim 16 recites,
apply a low pass filter to the target pattern.
The modifying/modify step is an evaluation and mathematical operation for the reasons demonstrated with respect to claim 13. The apply/applying operation merely qualifies how that abstract idea is performed and is itself an evaluation and mathematical operation on a set of mathematical relationships (model). The apply/applying operation is, therefore, as a mental process and mathematical concept, an element of the abstract idea. This merges with the abstract idea of the claim(s) from which the dependent claim depends. This provides no additional limitations to integrate the abstract idea into a practical application or render the claim inventive by providing significantly more in combination with the rest of the claim elements.
wherein the instructions configured to cause the computer system to modify the pattern are configured to
These additional limitations merely represent execution on a generic computer, which, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application and fail to combine with the other elements of the claim to provide significantly more and confer an inventive concept.
The features of claim 16 do not provide further additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept. Claims 2 and 14 recite similar features and are treated the same under the eligibility analysis.
Claims 2, 14, and 16 are ineligible.
Claims 3 and 17
Claim 17 recites,
wherein the pattern comprises a plurality of edges and vertices in a staircase pattern; and
The wherein clause specifies elements of the patterns (edges and vertices in a staircase pattern), which are parameters for use in the modify/modifying step and merge with the abstract idea of the claim(s) from which the dependent claim depends.
[…] reduce a number of edges and vertices in the staircase pattern.
The reduce/reduces operation qualifies the evaluation/mathematical operation of the modify/modifying operation of the claim(s) from which the dependent claim depends. Also, the reduce/reduces operation is an evaluation and mathematical operation and merges with the abstract idea of the claim(s) from which the dependent claim depends
The wherein clause and reduce/reduces step merge with the abstract idea of the claim(s) from which the dependent claim depends. This provides no additional limitations to integrate the abstract idea into a practical application or render the claim inventive by providing significantly more in combination with the rest of the claim elements.
wherein the instructions configured to cause the computer system to modify the pattern are configured to […]
These additional limitations merely represent execution on a generic computer, which, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application and fail to combine with the other elements of the claim to provide significantly more and confer an inventive concept.
The features of claim 17 do not provide further additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept. Claim 3 recites similar features and is treated the same under the eligibility analysis.
Claims 3 and 17 are ineligible.
Claims 4 and 21
Claim 4 recites,
wherein the simulating comprises:
modeling […] mask effects on imaging;
modeling source effects on imaging; and
modeling optical effects of an imaging optical system of the photolithography system.
These features merely qualify the simulating step of claim 1, which is part of the abstract idea. Further, the modeling steps are evaluations, which are mental processes, and are mathematical operations on mathematical relationships (models), so the modeling steps are elements of the abstract idea.
three dimensional mask
The modeling of a three-dimensional mask is merely applying a general modeling concept generically, which is tantamount to “apply it” under MPEP 2106.05(f). Because this feature merely “appl[ies] it,” the three dimensional mask fails to integrate the abstract idea into a practical application and fails to combine with the other elements of the claim to provide significantly more and confer an inventive concept.
The features of claim 4 do not provide further additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 21 recites features similar to the features of claim 4.
Claims 4 and 21 are ineligible.
Claims 6 and 22
Claim 6 recites,
wherein the simulating further comprises applying a plurality of edge filters.
This merely qualifies the simulating operation, which is an element of the abstract idea. Further, applying edge filters is an evaluation, which is a mental process, and is achieved by mathematical operations on mathematical relationships (models), so the applying operation is an abstract element that merges with the abstract idea.
The features of claim 6 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 22 recites features similar to the features of claim 6.
Claims 6 and 22 are ineligible.
Claims 7 and 23
Claim 7 recites,
wherein each edge filter is selected dependent on a location of an edge to which it is to be applied, and on a geometry of the edge to which it is to be applied.
Edge filter selection is an evaluation, which is a mental process. Further, the edge filter selection qualifies the simulating operation, which is an element of the abstract idea. Therefore, the features of claim 7 are abstract elements that merge with the abstract idea of the claims from which claim 7 depends.
The features of claim 7 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 23 recites features similar to the features of claim 7.
Claims 7 and 23 are ineligible.
Claim 8
Claim 8 recites,
wherein each edge filter is further selected dependent on a geometry of features proximate the edge to which it is to be applied.
Edge filter selection is an evaluation, which is a mental process. Further, the edge filter selection qualifies the simulating operation, which is an element of the abstract idea. Therefore, the features of claim 8 are abstract elements that merge with the abstract idea of the claims from which claim 8 depends.
The features of claim 8 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 8 is ineligible.
Claim 9
Claim 9 recites,
wherein the applying comprises applying the plurality of edge filters to each horizontal and each vertical edge.
This merely qualifies the simulating operation, which is an element of the abstract idea. Further, applying edge filters is an evaluation, which is a mental process, and is achievable by mathematical operations on mathematical relationships (models), so the applying operation is an abstract element that merges with the abstract idea.
The features of claim 9 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 9 is ineligible.
Claim 10
Claim 10 recites,
wherein the applying comprises applying the plurality of edge filters to all edges.
This merely qualifies the simulating operation, which is an element of the abstract idea. Further, applying edge filters is an evaluation, which is a mental process, and is achievable by mathematical operations on mathematical relationships (models), so the applying operation is an abstract element that merges with the abstract idea.
The features of claim 10 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 10 is ineligible.
Claims 11 and 24
Claim 11 recites,
wherein the simulating is used to perform source mask optimization.
This merely qualifies the simulating operation, which is an element of the abstract idea. Further, performing source mask optimization is an evaluation, which is a mental process, and is achieved by mathematical operations on mathematical relationships ([0052]), so the applying operation is an abstract element that merges with the abstract idea.
The features of claim 11 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 24 recites features similar to claim 11.
Claims 11 and 24 are ineligible.
Claim 12
MPEP 2106.05(d)(II): “Below are examples of other types of activity that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: […] iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;”
Subject Matter Eligibility; “October 2019 Examples 43-46”; Example 46, Claim 1; Page 35, Third Paragraph; Step 2B: […] “displaying data is well known.” (https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_app1.pdf)
Claim 12 recites,
wherein a mask and source resulting from the source mask optimization are used to image the pattern onto the substrate.
Note: Claim 12 is rejected under 35 USC 112(b), and the limitation, “image the pattern onto the substrate,” is interpreted to include merely displaying a simulated image on a substrate. Displaying data output from an abstract process is post-solution insignificant extra-solution activity akin to the post-solution activity example in MPEP 2106.05(g) (“a printer that is used to output a report of fraudulent transactions”) and a component of one of the selecting a particular data source or type of data to be manipulated (“iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Because this feature is insignificant extra-solution activity, it does not integrate the abstract idea into a practical application. Claim 12 is directed to the abstract idea under Step 2A, Prong 2.
The features of claim 12 are also akin to an example of well-known, routine, and conventional functions (iv. Presenting offers and gathering statistics) in MPEP 2106.05(d)(II). Further, Subject Matter Eligibility Example 46 explicitly states that displaying data is well known (e.g., a well-known, routine, and conventional function).
Because the display/presentation is a well-known, routing, and conventional function, and because the display/presentation is insignificant extra-solution activity, the limitations of claim 12 do not include an additional limitation that combines with the other elements of the claim to provide significantly more than the abstract idea that could confer an inventive concept.
The features of claim 12 do not provide additional limitations to integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 12 is ineligible.
Claims 25-27
Claim 25 recites:
simulating, with the thick mask model, the image to represent an electromagnetic field at an image plane after interacting with finite mask topography, the simulating comprising: determining edges and/or contours of the modified target pattern; generating a thick mask transmission function based on modelling electromagnetic field interaction with finite mask topography for the modified target pattern, the generating comprising applying one or more edge filters to edges and/or contours of the modified target pattern, the number of edge filters being reduced relative to a number that would have been applied absent the modifying of the target pattern; and generating the image by applying the thick mask transmission function to an incident electromagnetic field to obtain a modified electromagnetic field, and simulating propagating the modified electromagnetic field through a projection optics model of the photolithography system to the image plane.
Mental Process, Mathematical Concept – These simulating steps, as indicated in the specification and the comments of the Applicant on the record during this prosecution, is merely calculating a significant number of mathematical calculations. This is practically performable in the mind or with the aid of pen, paper, and/or a calculator, so it is an evaluation, a mental process, an abstract idea. This is also a placeholder for conducting mathematical calculations, which is a mathematical concept, an abstract idea. Claim 25 fails to provide any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Should it be found otherwise, the simulation using the thick mask determined based on the allegedly inventive operation is merely simulating the mask in its ordinary course of use. This is an apply-it step akin to the 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair,” because the thick mask is only used to generate wafers, and the simulation thereof is merely simulating this singular purpose for which the thick mask is determined. Similarly, this is insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “i. Cutting hair after first determining the hair style.” Also, the references of record demonstrate that the simulation and other additional limitations of the claims are well-understood, routine, and conventional steps that would be taken in due course as the only uses of the thick mask model under MPEP 2106.05(d). Further, because the use of the allegedly inventively determined thick mask for wafer fabrication or simulation thereof has no other purpose, there is even an argument that the simulation itself does nothing more than limit the abstract idea to a particular field under MPEP 2106.05(h).
Claims 26-27 recite substantially the same features as claim 25.
Accordingly claims 25-27 are ineligible.
Claim Rejections - 35 USC § 102
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.
Claims 1-4, 6-17, and 21-27: Li
Claim(s) 1-4, 6-17, and 21-27 are rejected under pre-AIA 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by US Patent Publication No. 2018/0341173 A1 to Li et al. (Li).
Claims 1, 13, 15
Regarding Claim 13, Li teaches:
A system for simulating a pattern to be imaged onto a substrate using a photolithography system, the system comprising: (Li Paragraph [0024] “Computational photolithography is a set of computer-based mathematical and algorithmic approaches (referred to as a “model”) specifically designed to solve optical and process proximity problems and improve the attainable resolution in photolithography. The capability of the model to simulate the optical imaging system and predict the wafer patterns is essential, and tools can be built to compensate optical proximity effects and enable RET.” – A photolithography system for simulating a pattern)
a memory for storing a pattern to be imaged onto a substrate; and a processor configured and arranged to, wherein the pattern includes a plurality of edges and vertices having corresponding frequency information, the pattern also including a target pattern that is a subset of the pattern: (Li Paragraph [0036] “The apparatus 200 can have an internal configuration of hardware including a processor 202 and a memory 204. […] The memory 204 can be any device or devices capable of storing codes and data that can be accessed by the processor (e.g., via a bus). For example, the memory 204 can be accessed by the processor 202 via a bus 212. […] “The memory 204 can include data 216, an operating system 220 and an application 218. The data 216 can be any data for photolithography simulation (e.g., computerized data files of mask design layout or database clips of Mask Topography filters).” [0033] “According to implementations of this disclosure, a near field image (MI3D) considering effects due to mask topology or topography (e.g., thickness and topographical structures) can be obtained by processing a thin mask image (MI2D) through an artificial neural network (ANN).” – Memory stores elements for carrying out a photolithography simulation, including a thin mask model that is the basis for a thick mask model. Processors execute the elements in stored memory. [0030] “In some implementations, the thin mask image can be pixelated. In some implementations, the thin mask image can be gray - scale. Rasterization techniques from image processing can be used, which can convert a vector graphics format of the geometry shapes of the patterns into a pixelated raster image format. However, this approach has been proven not accurate enough for sub - wavelength technology nodes.” – The image has vertices and edges (e.g., of pixels in a rasterized image) [0029] “OPC is a photolithography enhancement technique commonly used to improve edge integrity of processed original design (layout) placed into the etched image on the silicon wafer. OPC can compensate for image errors due to both optical (e.g., diffraction) or physical process (e.g., resist and etch) effects. OPC can manipulate amplitudes of wave fronts of light, allowing more or less amount of light to travel through, to counteract imaging effects that distort the reproduced patterns on the wafer. The idea of OPC is to pre-compensate for the process losses by modifying original layout, which can improve pattern transfer fidelity and resolution. For example, OPC techniques can include lengthening features, displacing edges of a pattern (referred to as " edge - biasing”), and creating serifs to reduce corner rounding. Another OPC technique can include adding sub resolution assistant features (SRAF) to the original layout , which are very small features with dimensions below resolution of the imaging system .” – Also, the design layout is modified by OPC to remove edges and vertices.)
modify the pattern to improve image prediction by a thick mask model, the modifying comprising: performing smoothing of the target pattern to reduce higher frequency information in the target pattern by changing or removing one or more of the edges and vertices an edge or a portion of a to change the shape of the target pattern, and generating a modified pattern that includes the target pattern with the higher frequency information removed; and
(Li [0025] “This projection can be described by a light-source-dependent projection function (referred to as a "pupil function"). When the source is incoherent and has a shape consisting of a region on a plane of the source, aerial images resulting from sampled points of the light source can be summed to produce a total aerial image, which determines final patterns projected on the wafer. This scheme can be modified under some assumptions and through various derivations for speed improvement. However, the mask image remains the starting point of the simulation.” – After the thick mask is created and ready for projection as an image of a pattern as expected to be produced by a photolithographic system. [0043] “According to implementations of this disclosure, the near field image (MI3n) can be obtained from a thin mask image (MI2D) based on an image processing method.” ). – The thick mask is derived from the thin mask, e.g., by use of an ML model. [0044] In principle, the MI3n can be obtained from the MI2D using the Mask Topography filters ( e.g., gradient filters and curvature filters Some image processing methods, referred to as "geometry detectors," can detect geometric features from MI2n. For example, gradient detectors and curvature detectors can detect edges and comers, respectively. The Mask Topography filters can filter the edges and corners to account for mask topography effects and determine geometric data. For example, the geometric data can include a partial near filed image centered on an edge or a corner. The outputs of the Mask Topography filters ( e.g., corner-centered and edge-centered partial near field images) can be summed with MI2D to determine MI3n. “ [0050] – “Various render filters (e.g., a low-pass filter) 304 can render the mask layout 302 (e.g., polygons) at operation 306 (e.g., a convolution operation). The thin mask image 308 (e.g., a pixelated thin mask image) can be outputted from the operation 306. For example, the operation 306 can include a rasterization operation to generate the thin mask image 308. The thin mask image 308 can carry information of all edges and corners as well as their neighboring features of mask patterns.” – The target design can be modified (e.g., at the thin mask stage prior to the thick mask stage) so that the thick mask/near-field image is modified, including by render filters (e.g., for polygons) or curvature filters (e.g., for contours). [0053]-[0054] “In the process 300B, the thin mask image 308 can be generated in the same way as in the process 300A. The mask topography effect are caused by scattering from mask edges. Mask image gradients (e.g., a vector image) can be determined to represent the edges in the thin mask image 308. Gradients 314 can be determined from the thin mask image 308. The gradients 314 include one or more vector images.” [0055] “The ANN 316 can take the gradients 314 as input. The ANN 316 can output predicted differences between thin mask images (e.g., the thin mask image 308) and thick mask images (e.g., the thick mask image 312).” – Vectorizing the image to determine gradients for input into an ANN; [0059] “The thick mask image 312 can be determined by an ANN 320. The inputs of the ANN 320 can include a thin mask image 308. The ANN 320 can be similar to the ANN 310 in the process 300A, similar to the ANN 316 in the process 300B, or any other ANN that can determine near field images using thin mask images as inputs.” – ANN using thin mask as input; The pattern can be modified to improve the thick mask by rigorous solvers, cropping, vectorization, gradient determination, and ANN mask improvement. [0029] “OPC is a photolithography enhancement technique commonly used to improve edge integrity of processed original design (layout) placed into the etched image on the silicon wafer. OPC can compensate for image errors due to both optical (e.g., diffraction) or physical process (e.g., resist and etch) effects. OPC can manipulate amplitudes of wave fronts of light, allowing more or less amount of light to travel through, to counteract imaging effects that distort the reproduced patterns on the wafer. The idea of OPC is to pre-compensate for the process losses by modifying original layout, which can improve pattern transfer fidelity and resolution. For example, OPC techniques can include lengthening features, displacing edges of a pattern (referred to as " edge - biasing”), and creating serifs to reduce corner rounding. Another OPC technique can include adding sub resolution assistant features (SRAF) to the original layout , which are very small features with dimensions below resolution of the imaging system .” – Also, the design layout is modified by OPC to remove edges and vertices.)
computer simulate, using the thick mask model with the modified pattern as an input to the thick mask model, an image of the modified pattern as expected to be produced by the photolithography system, predicted by the thick mask model. (Li [0038] “In some implementations, besides the processor 202 and the memory 204, the apparatus 200 can include an output device 208. The output device 208 can be implemented in various ways, for example, it can be a display that can be coupled to the apparatus 200 and configured to display a rendering of graphic data.” – NOTE, As indicated with respect to the 35 USC 112(b) rejections of the claims, it is unclear what simulating an image is. For purposes of examination, it will be interpreted to mean creating data for a display. Li [0038] “In some implementations, besides the processor 202 and the memory 204, the apparatus 200 can include an output device 208. The output device 208 can be implemented in various ways, for example, it can be a display that can be coupled to the apparatus 200 and configured to display a rendering of graphic data. – Li teaches a display of graphic data which would include the thick mask. [0062] “Each node of the input layer and the output layer in FIG. 4 is a graphic representation of input data and output data of the ANN, respectively.” – FIG. 4 includes a simulation of graphic representation of a modified thick mask image, as element 312. [0025] “In a standard framework commonly employed in fast computational photolithography models, an image representing the mask patterns (referred to as a "mask image") is created from computer-stored data files. The mask image can be projected to an optical intensity profile (referred to as an "aerial image"), onto the surface of the wafer. This projection can be described by a light-source-dependent projection function (referred to as a "pupil function"). When the source is incoherent and has a shape consisting of a region on a plane of the source, aerial images resulting from sampled points of the light source can be summed to produce a total aerial image, which determines final patterns projected on the wafer. This scheme can be modified under some assumptions and through various derivations for speed improvement.” – Li teaches that a simulation of the aerial image projected from the thick mask model is projected on the substrate itself, a simulation. Also, this teaches more image data that can be displayed on Li’s display.)
Claim 1 recites a method including the modify and simulate operations of claim 13 and recites an obtaining step that is rejected using the same rationale and art as the memory storing a pattern to be imaged onto the substrate in claim 13. Therefore, claim 1 is rejected for at least the same reasons as claim 13, and the rejection of claim 13 is applied mutatis mutandis to the rejection of claim 1.
Claim 15 recites a CRM that functions as the memory does in claim 13 and is for execution by a processor, such as the processor of claim 13. Therefore, claim 15 is rejected for at least the same reasons as claim 13, and the rejection of claim 13 is applied mutatis mutandis to the rejection of claim 15.
Claims 2, 14, and 16
Regarding Claim 14 (and 2 and 16), Li teaches the features of claim 13 (and 1 and 15). Li further teaches:
wherein the processor is configured to modify the pattern by applying a low pass filter to the target pattern. (Li [0050] – “Various render filters (e.g., a low-pass filter) 304 can render the mask layout 302 (e.g., polygons) at operation 306.” - Li applies a low-pass filter to the pattern/target pattern.) Claims 2 and 16 recite similar features and are rejected for at least the same reasons as claim 14.
Claims 3 and 17
Regarding claim 17 (and 3), Li teaches the features of claim 15 (and 1). Li further teaches:
wherein the pattern comprises a plurality of edges and vertices (Li [0030] “This thin layer can be simulated by building a two-dimensional image (referred to as a “thin mask image”) through rendering and low-pass filtering (also called “blurring” or “smoothing”) for noise reduction.” - Low pass filters smooth images, by removing rough edges.; [0032] “In addition, the near field image not only depends on geometries (e.g., edges or corners), but also is affected by adjacent patterns. When an inter-geometry distance (e.g., an edge-to-edge, edge-to-corner, or corner-to-corner distance) is so small that secondary optical scattering effects can significantly change the near field” The short edge-to-edge distance in adjacent patterns is a staircase pattern that can be smoothed using the low-pass filter.) Claim 3 recites features similar to the features of claim 17 and is rejected for at least the same reasons.
Claims 4 and 21
Regarding claims 4 and 21, Li teaches the features of claims 1 and 15. Li further teaches:
wherein the simulating comprises: modeling three dimensional mask effects on imaging; (Li [0044] “In principle, the MI3D can be obtained from the MI2D using the Mask Topography filters (e.g., gradient filters and curvature filters). Some image processing methods, referred to as “geometry detectors,” can detect geometric features from MI2D. For example, gradient detectors and curvature detectors can detect edges and corners, respectively. The Mask Topography filters can filter the edges and corners to account for mask topography effects and determine geometric data. For example, the geometric data can include a partial near filed image centered on an edge or a corner. The outputs of the Mask Topography filters (e.g., corner-centered and edge-centered partial near field images) can be summed with MI2D to determine MI3D.” – 3D mask effects on imaging are modeled)
modeling source effects on imaging; and (Li [0025] “The mask image can be projected to an optical intensity profile (referred to as an “aerial image”), onto the surface of the wafer. This projection can be described by a light-source-dependent projection function (referred to as a “pupil function”). When the source is incoherent and has a shape consisting of a region on a plane of the source, aerial images resulting from sampled points of the light source can be summed to produce a total aerial image, which determines final patterns projected on the wafer.” – Source effects are modeled using a light-source dependent light function)
modeling optical effects of an imaging optical system of the photolithography system. (Li [0030] “In model-based OPC processes, a lithographic model can be built to simulate the optical or electromagnetic near field due to proximity to mask topology or topography features (e.g., thickness and topographical structures). The effects on the optical or electromagnetic near field due to the proximity to the mask topography features can be referred to as “mask topography effects” hereinafter.” – Optical effects of the imaging optical system (near field effects) are modeled.)
Claims 6 and 22
Regarding claims 6 and 22, Li teaches the features of claims 4 and 15. Li further teaches:
wherein the simulating further comprises applying a plurality of edge filters. (Li [0032] “The library-based approach can solve Maxwell's equations for selected simple patterns, crop a resulted near field image that covers a region surrounding a single geometry (e.g., a single edge or a single corner), and record the resulted near field image into a library database. The database clips of geometry-centered (e.g., edge-centered or corner-centered) near field images are called geometry filters (e.g., edge filters or corner filters), which can be stored in a digital file storing system. Database clips of near field images with other geometries (e.g., areas or other geometric features of the mask patterns) centered are also possible, which can be collectively called “Mask Topography filters.”” – Edge filters are applied.)
Claims 7 and 23
Regarding claims 7 and 23, Li teaches the features of claims 6 and 22. Li further teaches:
wherein each edge filter is selected dependent on a location of an edge to which it is to be applied, and on a geometry of the edge to which it is to be applied. (Li [0032] “The library-based approach can solve Maxwell's equations for selected simple patterns, crop a resulted near field image that covers a region surrounding a single geometry (e.g., a single edge or a single corner), and record the resulted near field image into a library database. The database clips of geometry-centered (e.g., edge-centered or corner-centered) near field images are called geometry filters (e.g., edge filters or corner filters), which can be stored in a digital file storing system. Database clips of near field images with other geometries (e.g., areas or other geometric features of the mask patterns) centered are also possible, which can be collectively called “Mask Topography filters.” In the library-based approach, when an OPC model is used to simulate a near field image of a complex pattern for a semiconductor chip, the OPC model can determine (e.g., loop through) all edges and corners on the layout of the chip, look up corresponding recorded edge filters and corner filters in the library, and copy the corresponding edge-centered or corner-centered near field images to assemble the near field image for the chip. Although faster than the rigorous solvers with sufficient accuracy, the library-based approach cannot support unlimited geometric features of patterns (e.g., unlimited edge directions and feature sizes).” – Edge filters are selected based on a location (centered) and a geometry (edge directions) of the edge.)
Claim 8
Regarding claim 8, Li teaches the features of claim 7. Li further teaches:
wherein each edge filter is further selected dependent on a geometry of features proximate the edge to which it is to be applied. (Li [0032] “The database clips of geometry-centered (e.g., edge-centered or corner-centered) near field images are called geometry filters (e.g., edge filters or corner filters), which can be stored in a digital file storing system. Database clips of near field images with other geometries (e.g., areas or other geometric features of the mask patterns) centered are also possible, which can be collectively called ‘Mask Topography filters.’ In the library-based approach, when an OPC model is used to simulate a near field image of a complex pattern for a semiconductor chip, the OPC model can determine (e.g., loop through) all edges and corners on the layout of the chip, look up corresponding recorded edge filters and corner filters in the library, and copy the corresponding edge-centered or corner-centered near field images to assemble the near field image for the chip. […] In addition, the near field image not only depends on geometries (e.g., edges or corners), but also is affected by adjacent patterns. When an inter-geometry distance (e.g., an edge-to-edge, edge-to-corner, or corner-to-corner distance) is so small that secondary optical scattering effects can significantly change the near field, or when accuracy requirement is so high that numerous segments and shapes of the layout are involved” – The edge filter is selected based on features proximate to the edge to which the filter is applied.)
Claim 9
Regarding claim 9, Li teaches the features of claim 6. Li further teaches:
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wherein the applying comprises applying the plurality of edge filters to each horizontal and each vertical edge. (Li [0032] “In the library-based approach, when an OPC model is used to simulate a near field image of a complex pattern for a semiconductor chip, the OPC model can determine (e.g., loop through) all edges and corners on the layout of the chip, look up corresponding recorded edge filters and corner filters in the library, and copy the corresponding edge-centered or corner-centered near field images to assemble the near field image for the chip.” – Appropriate edge and corner filters are applied to all edges and corners; FIG. 5 – Includes horizontal and vertical edges; The OPC loops through all edges and corners, including the horizontal and vertical edges illustrated in FIG. 5)
Claim 10
Regarding claim 10, Li teaches the features of claim 6. Li further teaches:
wherein the applying comprises applying the plurality of edge filters to all edges. (Li [0032] “In the library-based approach, when an OPC model is used to simulate a near field image of a complex pattern for a semiconductor chip, the OPC model can determine (e.g., loop through) all edges and corners on the layout of the chip, look up corresponding recorded edge filters and corner filters in the library, and copy the corresponding edge-centered or corner-centered near field images to assemble the near field image for the chip.” – Appropriate edge and corner filters are applied to all edges and corners.)
Claims 11 and 24
Regarding claims 11 and 24, Li teaches the features of claims 1 and 15. Li further teaches:
Wherein the simulating is used to perform source mask optimization. (Li [0057] “Optimizations for computational lithography applications, such as OPC, Source-Mask co-Optimization (SMO), and ILT, can use gradients generated based on thick mask images. For example, the thick mask images 312 can be inputted to imaging or models for optimization. The optimization results can be compared with wafer images to calculate a value of a cost function (referred to as a “cost value”). For example, the cost function can include a root-mean-square (RMS) of an error associated with a thick mask image. To tune optimization variables, the gradients of the cost function with respect to the optimization variables can be used.” – The simulating is used for source mask optimization.)
Claim 12
Regarding claim 12, Li teaches the features of claim 11. Li further teaches:
wherein a mask and source resulting from the source mask optimization are used to image the pattern onto the substrate. (Li [0025] “In a standard framework commonly employed in fast computational photolithography models, an image representing the mask patterns (referred to as a “mask image”) is created from computer-stored data files. The mask image can be projected to an optical intensity profile (referred to as an “aerial image”), onto the surface of the wafer. This projection can be described by a light-source-dependent projection function (referred to as a “pupil function”). When the source is incoherent and has a shape consisting of a region on a plane of the source, aerial images resulting from sampled points of the light source can be summed to produce a total aerial image, which determines final patterns projected on the wafer.” – The mask and source are used to image the pattern onto the substrate.)
Claims 25-27
Regarding claim 25,
simulating, with the thick mask model, the image to represent an electromagnetic field at an image plane after interacting with finite mask topography, the simulating comprising: (Li [0005] “Photolithography simulations incorporating OPC and RET techniques can be used for increased pattern complexity of mask patterns. In simulations for sub-wavelength photolithography, mask images considering near field effects due to mask topology or topography (“near field image”) can be used.” [0030]-[0031] “In model-based OPC processes, a lithographic model can be built to simulate the optical or electromagnetic near field due to proximity to mask topology or topography features (e.g., thickness and topographical structures). The effects on the optical or electromagnetic near field due to the proximity to the mask topography features can be referred to as “mask topography effects” hereinafter. For example, the mask topography effects can consider materials, thickness, and sidewall angles or layer differences in a photomask stack. To simulate the near field due to mask thickness and topographical structures, a first principle is to solve Maxwell's equations. Due to a complicated shape of the light source and complex boundary conditions defined by mask patterns, the Maxwell's equations can typically be solved numerically only. Several rigorous numerical methods (referred to as “rigorous solvers”) can be used to solve Maxwell's equations, such as Finite-Difference Time-Domain (FDTD) method and Rigorously Coupled Wave Analysis (RCWA) method. The mask image with mask topography effects (referred to as a “near field image” or a “thick mask image”) from the rigorous solvers can be inserted into the pupil function to determine the aerial image. Although the rigorous solvers can generate relatively accurate simulation results, in some cases, it is difficult to compute the near field image for a full chip, due to the computing capability of the rigorous solvers is limited.” – Li simulates, with a thick mask model, an image to represent an EM field at an image plane after interacting with finite mask topography.)
determining edges and/or contours of the modified target pattern; (Li [0032] “A library-based approach can be used to speed up the rigorous solvers to compute the near field image for OPC and other RET applications. The library-based approach can solve Maxwell's equations for selected simple patterns, crop a resulted near field image that covers a region surrounding a single geometry (e.g., a single edge or a single corner), and record the resulted near field image into a library database. The database clips of geometry-centered (e.g., edge-centered or corner-centered) near field images are called geometry filters (e.g., edge filters or corner filters), which can be stored in a digital file storing system. Database clips of near field images with other geometries (e.g., areas or other geometric features of the mask patterns) centered are also possible, which can be collectively called “Mask Topography filters.” In the library-based approach, when an OPC model is used to simulate a near field image of a complex pattern for a semiconductor chip, the OPC model can determine (e.g., loop through) all edges and corners on the layout of the chip, look up corresponding recorded edge filters and corner filters in the library, and copy the corresponding edge-centered or corner-centered near field images to assemble the near field image for the chip. Although faster than the rigorous solvers with sufficient accuracy, the library-based approach cannot support unlimited geometric features of patterns (e.g., unlimited edge directions and feature sizes). For example, if the library is built for edges in selected orientations, it cannot be used to accurately simulate the near field image for edges in orientations other than the selected ones. In addition, the near field image not only depends on geometries (e.g., edges or corners), but also is affected by adjacent patterns. When an inter-geometry distance (e.g., an edge-to-edge, edge-to-corner, or corner-to-corner distance) is so small that secondary optical scattering effects can significantly change the near field, or when accuracy requirement is so high that numerous segments and shapes of the layout are involved, the library-based approach can face high dimensionality and complexity, in which the size and structure of the library database can be largely increased.” – Edges and contours of the modified target pattern (as demonstrated in the independent claim) are determined.)
generating a thick mask transmission function based on modelling electromagnetic field interaction with finite mask topography for the modified target pattern, the generating comprising applying one or more edge filters to edges and/or contours of the modified target pattern, the number of edge filters being reduced relative to a number that would have been applied absent the modifying of the target pattern; and generating the image by applying the thick mask transmission function to an incident electromagnetic field to obtain a modified electromagnetic field, and simulating propagating the modified electromagnetic field through a projection optics model of the photolithography system to the image plane. (Li [0033] “By using the ANN, support for mask patterns are not limited to certain edge directions or feature sizes, while accuracy of the predicted near field image can be up to a level suitable for OPC modeling. MI3D can be inputted into the pupil function of a photolithography simulation model to determine the aerial image.” [0044] “In principle, the MI3D can be obtained from the MI2D using the Mask Topography filters (e.g., gradient filters and curvature filters). Some image processing methods, referred to as “geometry detectors,” can detect geometric features from MI2D. For example, gradient detectors and curvature detectors can detect edges and corners, respectively. The Mask Topography filters can filter the edges and corners to account for mask topography effects and determine geometric data. For example, the geometric data can include a partial near filed image centered on an edge or a corner. The outputs of the Mask Topography filters (e.g., corner-centered and edge-centered partial near field images) can be summed with MI2D to determine MI3D.” [0050] “Various render filters (e.g., a low-pass filter) 304 can render the mask layout 302 (e.g., polygons) at operation 306 (e.g., a convolution operation). The thin mask image 308 (e.g., a pixelated thin mask image) can be outputted from the operation 306. For example, the operation 306 can include a rasterization operation to generate the thin mask image 308. The thin mask image 308 can carry information of all edges and corners as well as their neighboring features of mask patterns.”– The combination of different methods to determine a thick mask from a thin mask include different methods that sequentially trim edges from the design of the thin mask model to make a simple, intermediate thick mask, and subsequent further refinement of the intermediate thick mask to yield a final thick mask model used in simulation. [0078] “In another implementation, the training termination condition can be that there is a match between the determined MI3D′ and a defined image (a “template image”). The template image can be a near field image indicative of the same photomask feature considering mask topographical effects. The template image can have a simulation accuracy higher than or equal to a simulation accuracy of the determined MI3D′. The template image can be determined using a different method. In some implementations, the different method can include rigorous solvers and/or the library-based approach.” – The resulting image simulation is used to train an ANN in one implementation. [0005] “Photolithography simulations incorporating OPC and RET techniques can be used for increased pattern complexity of mask patterns. In simulations for sub-wavelength photolithography, mask images considering near field effects due to mask topology or topography (“near field image”) can be used.” [0030]-[0031] “In model-based OPC processes, a lithographic model can be built to simulate the optical or electromagnetic near field due to proximity to mask topology or topography features (e.g., thickness and topographical structures). The effects on the optical or electromagnetic near field due to the proximity to the mask topography features can be referred to as “mask topography effects” hereinafter. For example, the mask topography effects can consider materials, thickness, and sidewall angles or layer differences in a photomask stack. To simulate the near field due to mask thickness and topographical structures, a first principle is to solve Maxwell's equations. Due to a complicated shape of the light source and complex boundary conditions defined by mask patterns, the Maxwell's equations can typically be solved numerically only. Several rigorous numerical methods (referred to as “rigorous solvers”) can be used to solve Maxwell's equations, such as Finite-Difference Time-Domain (FDTD) method and Rigorously Coupled Wave Analysis (RCWA) method. The mask image with mask topography effects (referred to as a “near field image” or a “thick mask image”) from the rigorous solvers can be inserted into the pupil function to determine the aerial image. Although the rigorous solvers can generate relatively accurate simulation results, in some cases, it is difficult to compute the near field image for a full chip, due to the computing capability of the rigorous solvers is limited.” – Also, Li teaches simulating, with the determined thick mask model, an image to represent an EM field at an image plane after interacting with finite mask topography.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From Prior Action)
NPL: “A Fast and Flexible Library-based Thick Mask Near-field Calculation Method” by Ma et al. (This reference teaches the features of the independent claims and most of the dependent claims.)
US Patent No. 8,918,743 B1 to Yan et al. (This reference teaches the features of the independent claims and most of the dependent claims.)
US Patent No. 11,137,690 B2 to Hsu (This reference teaches details of the source-mask optimization and optimization of the projection system.)
US Patent No. 10,838,305 B2 to Yu et al. (This reference discusses pattern-independent pre-computed kernels.)
US Application 20030008215 A1 to Mukherjee (This reference discusses using a low pass filter on a mask with a staircase pattern.)
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