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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-16 are directed to a method, claim 17-18 are directed to using a wind farm controller to perform the method, and claims 19-20 are directed to a wind farm controller. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method comprising:
obtaining at least one first trained model being operable to derive local performance parameter data from high resolution metrology data, wherein the local performance parameter data describes a local component, or one or more local contributors thereto, of a performance metric associated with a pattern etched into a layer on a substrate using an etching step of a semiconductor manufacturing process;
obtaining high resolution metrology data relating to the pattern prior to the etching step;
determining local performance parameter data from the high resolution metrology data using the at least one first trained model, wherein the local performance parameter and the high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring the semiconductor manufacturing process, and wherein the at least one first trained model has been trained on training data comprising first training high resolution metrology data obtained from one or more training substrates prior to the etching step and second training high resolution metrology data obtained from the one or more training substrates subsequent to the etching step; and
determining the performance metric from a combination of the local performance parameter data and the global performance parameter data.
Step 2A Prong 1:
“obtaining high resolution metrology data relating to the pattern prior to the etching step” is directed to mental step of data gathering.
“determining local performance parameter data from the high resolution metrology data using the at least one first trained model, wherein the local performance parameter and the high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring the semiconductor manufacturing process” is directed to math because developing and training a “trained model” requires advanced mathematical techniques like optimization algorithms, linear algebra, calculus, and probability theory to learn the relationship between metrology data and performance parameters. Comparing and analyzing data with different spatial resolutions relies on concepts from spatial statistics, signal processing, and numerical analysis. This involves mathematical techniques for sampling, interpolation, and potential down sampling or up sampling of data while maintaining integrity. The core task of determining local performance parameter data from metrology data involves statistical modeling, hypothesis testing, and quality control methodologies used to monitor the semiconductor manufacturing process.
“determining the performance metric from a combination of the local performance parameter data and the global performance parameter data” is directed to math because this process involves applying mathematical expressions, statistical methods, and algorithms to collected data to quantify performance, such as efficiency, accuracy, or quality.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “performance metric” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of " determining local performance parameter data from the high resolution metrology data using the at least one first trained model, wherein the local performance parameter and the high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring the semiconductor manufacturing process, and wherein the at least one first trained model has been trained on training data comprising first training high resolution metrology data obtained from one or more training substrates prior to the etching step and second training high resolution metrology data obtained from the one or more training substrates subsequent to the etching step; and determining the performance metric from a combination of the local performance parameter data and the global performance parameter data” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“obtaining at least one first trained model being operable to derive local performance parameter data from high resolution metrology data, wherein the local performance parameter data describes a local component, or one or more local contributors thereto, of a performance metric associated with a pattern etched into a layer on a substrate using an etching step of a semiconductor manufacturing process” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining high resolution metrology data relating to the pattern prior to the etching step” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining local performance parameter data from the high resolution metrology data using the at least one first trained model, wherein the local performance parameter and the high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring the semiconductor manufacturing process, and wherein the at least one first trained model has been trained on training data comprising first training high resolution metrology data obtained from one or more training substrates prior to the etching step and second training high resolution metrology data obtained from the one or more training substrates subsequent to the etching step” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining the performance metric from a combination of the local performance parameter data and the global performance parameter data” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. at least one first trained model, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the at least one first trained model merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of at least one first trained model to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“obtaining at least one first trained model being operable to derive local performance parameter data from high resolution metrology data, wherein the local performance parameter data describes a local component, or one or more local contributors thereto, of a performance metric associated with a pattern etched into a layer on a substrate using an etching step of a semiconductor manufacturing process” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining high resolution metrology data relating to the pattern prior to the etching step” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining local performance parameter data from the high resolution metrology data using the at least one first trained model, wherein the local performance parameter and the high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring the semiconductor manufacturing process, and wherein the at least one first trained model has been trained on training data comprising first training high resolution metrology data obtained from one or more training substrates prior to the etching step and second training high resolution metrology data obtained from the one or more training substrates subsequent to the etching step” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining the performance metric from a combination of the local performance parameter data and the global performance parameter data” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 17 cites a computer program product comprising a non-transitory computer-readable medium comprising processor readable instructions therein, which instruction, when run on suitable processor controlled apparatus, cause the processor controlled apparatus to perform the steps as in claim 1. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 14 for the same reason discussed above with regard to the rejection of claim 1.
Dependent claims 2-16, and 18-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below: there is no additional element(s) in the dependent claims that adds a meaningful limitation to the abstract idea to make the claim significantly more than the judicial exception (abstract idea).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kenneth Mason ("Computational EPE-driven SPC for semiconductor manufacturing processes").
Regarding claims 1 and 17, Kenneth Mason discloses computer program and method of monitoring a semiconductor manufacturing process, the method comprising:
obtaining at least one first trained model being operable to derive local performance parameter data from high resolution metrology data (page 2, in the process setup stage: "LCDU After Develop to After Etch (ADI to AEI) transfer function could also be determined for instance by measuring LCDU of target features at ADI and AEI stages" corresponds to training a model that derives local CD uniformity from high resolution metrology data (ADI local CD uniformity), wherein said local performance parameter data describes a local component, or one or more local contributors thereto, of a performance metric associated with a pattern etched into a layer on a substrate using an etching step of said semiconductor manufacturing process (page 1: local CD uniformity is described as a component of local placement error (LPE) which is a local performance parameter. As the calibration mentioned above is performed to obtain transferability of the ADI measurements after etch, it is considered implicit that the LPE is considered after etch);
obtaining high resolution metrology data relating to the pattern prior to said etching step (implicit when using the ADi to AEI transfer function of page 2);
determining local performance parameter data from said high resolution metrology data using said first trained model (the document refers to "Predicted LCDU" at the bottom of page 2), wherein said local performance parameter and said high resolution metrology data have a spatial resolution higher than global performance parameter data used in monitoring said semiconductor manufacturing process (the local uniformity necessarily has a higher spatial resolution than global CD uniformity), and wherein said first trained model has been trained on training data comprising first training high resolution metrology data obtained from one or more training substrates prior to said etching step and second training high resolution metrology data obtained from said one or more training substrates subsequent to said etching step (see the second step of the "Process setup stage" on page 2); and
determining said performance metric from a combination of said local performance parameter data and said global performance parameter data (According to the second paragraph on page 1, global parameters like overlay and global CD uniformity are "the most relevant layer parameters" relating to EPC, and the reference to ''All EPE contributors" in the last point of the "Process setup" on page 2 implies that global parameters are taken into account. Also, in the last five lines on page 2, local and global CD uniformity data are upsampled to obtain predicted the predicted LCDU).
Regarding claims 2, 3, Kenneth Mason discloses the local performance parameter data and/or the high resolution metrology data relates to process variations at a spatial scale of less than 100 μm.; wherein the local performance parameter data and/or the high resolution metrology data relates to process variations at a spatial scale of less than 10 times the size of a pitch of product structures on the substrate, to which the high resolution metrology data relates (Kenneth Mason refers to local CD uniformity which typically meets the conditions of spatial scale).
Regarding claim 4, Kenneth Mason discloses wherein the performance metric is a metric indicative of yield for the semiconductor manufacturing process (Kenneth Mason relates to EPE which is a metric indicative of yield).
Regarding claim 5, Kenneth Mason discloses wherein the high resolution metrology data comprises data which has been obtained using non-destructive metrology (Kenneth Mason refers toe-beam and scatterometry in the fourth paragraph of page 1).
Regarding claim 6, Kenneth Mason discloses wherein the high resolution metrology data comprises e-beam metrology data (Kenneth Mason refers toe-beam and scatterometry in the fourth paragraph of page 1).
Regarding claim 7, Kenneth Mason discloses wherein the at least one first trained model is trained such that the local performance parameter data comprises local contributor performance parameter data (Kenneth Mason refers a local contributor to EPE.).
Regarding claim 8, Kenneth Mason discloses wherein the local contributor performance parameter data is described in terms of one or more selected from: local critical dimension, local overlay, local tilt of any structure or feature formed by the semiconductor manufacturing process, local side wall angle of any structure or feature formed by the lithographic process, and/or local line placement (page 3).
Regarding claim 9, Kenneth Mason discloses the performance metric comprises edge placement error and/or contact area between two structures formed by the semiconductor manufacturing process (Kenneth Mason relates to edge placement error).
Regarding claim 10, Kenneth Mason discloses wherein the local performance parameter data comprises at least some metrology data which could only be directly measured by a destructive metrology technique (Kenneth Mason deals with determining computed EPE).
Regarding claim 11, Kenneth Mason discloses obtaining second metrology data; obtaining at least one second model being operable to derive the global performance parameter data from second metrology data, wherein the global performance parameter data describes a global component, or one or more global contributors thereto, of the performance metric indicative of yield; and using the at least one second model to determine the global performance parameter data for combination with the local performance parameter data from the second metrology data (page 2 that the global parameters like overlay mentioned in page 1 are taken into account).
Regarding claim 12, Kenneth Mason discloses wherein the second metrology data comprises metrology data measured using an optical metrology tool (page 2, last paragraph).
Regarding claim 13, Kenneth Mason discloses performing inspection on an area of the substrate identified as having a determined performance metric indicative of poor performance and/or a defect (page 3 indicates that the method should be integrated in a "conventional SPC framework").
Regarding claim 14, Kenneth Mason discloses the result of the inspection is used to update at least the at least one first trained model (page 3 indicates that the method should be integrated in a "conventional SPC framework").
Regarding claim 15, Kenneth Mason discloses wherein the at least one second model is trained such that the global performance parameter data comprises data directly describing the global component of the of the performance metric (Kenneth Mason: train a model to obtain a global component like overlay or global CD uniformity).
Regarding claim 16, Kenneth Mason discloses obtaining a relationship between the performance metric and yield; and determining yield for the semiconductor manufacturing process based on the determined performance metric and the relationship (Kenneth Mason discloses "the metric most related to the layer yield" (page 1 second paragraph), it is considered that the additional step of determining yield).
Regarding claim 18, Kenneth Mason discloses obtain second metrology data; obtain at least one second model being operable to derive the global performance parameter data from second metrology data, wherein the global performance parameter data describes a global component, or one or more global contributors thereto, of the performance metric indicative of yield; and use the at least one second model to determine the global performance parameter data for combination with the local performance parameter data from the second metrology data (page 2, last paragraph: global parameters like overlay mentioned in page 1 are taken into account to use a model to derive overlay and use an optical tool to measure overlay).
Regarding claim 19, Kenneth Mason discloses the instructions are further configured to cause the processor controlled apparatus to identify an area on the substrate having a determined performance metric indicative of poor performance and/or a defect (page 2 indicates that the method should be integrated in a "conventional SPC framework").
Regarding claim 20, Kenneth Mason discloses the processor controlled apparatus to update at least the at least one first trained model based on a result of inspection of the identified area on the substrate (page 2 indicates that the method should be integrated in a "conventional SPC framework").
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Adel et al. (USP 10,409,171) disclose a process control system may include a controller configured to receive after-development inspection (ADI) data after a lithography step for the current layer from an ADI tool, receive after etch inspection (AEI) overlay data after an exposure step of the current layer from an AEI tool, train a non-zero offset predictor with ADI data and AEI overlay data to predict a non-zero offset from input ADI data, generate values of the control parameters of the lithography tool using ADI data and non-zero offsets generated by the non-zero offset predictor, and provide the values of the control parameters to the lithography tool for fabricating the current layer on the at least one production sample.
Lin et al. (US 2021/0389677 A1) disclose a method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method comprising: obtaining yield distribution data comprising a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, the metrology data of each set comprising a spatial variation of a process parameter over the substrate or part thereof and each set corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857