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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on Jan. 21, 2022. It is noted, however, that applicant has not filed a certified copy of the CN202210071920.7 application as required by 37 CFR 1.55.
Response to Arguments
Applicant's arguments filed 05/22/2025 regarding the rejection under 35 USC 101 have
been fully considered but they are not persuasive.
Applicant argues, see especially pages 9-11, that claims 1 and 7, are -patent eligible because “According to MPEP 2106.05(f), claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743. In the semiconductor manufacturing process, a wafer needs to go through thousands of processes to produce the final product. Therefore, researchers need to use appropriate prediction methods for the semiconductor process to predict the electrical function and yield of the final product, so as to avoid a large number of defective products in the final product.” Examiner respectfully disagrees. The claims do not provide enough specific details about how the invention achieves the solution to the problem. The claims state that soft process data and hard process data are used in a two-stage procedure for prediction. All of these terms are generally stated and do not provide enough specific detail about the process to demonstrate a specific method of achieving the proposed solution. The terms soft process data and hard process data are very broad terms that are not redefined in the specification to have any specific meaning. The specification merely states that these data “can” be some specific data but does not limit the terms to being those data that are specified.
The rejection under 35 USC 101 is maintained.
Applicant's arguments filed 05/22/2025 regarding the rejection under 35 USC 103 have been fully considered but they are not persuasive.
Applicant argues, see especially pages 8-9, that claims 1 and 7 are -patent eligible because “It is noted that Jung, Vajaria, Fabish, and David do not disclose "the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, where the two-stage procedure includes a first stage and a second stage, in which a soft process data is used in the first stage, and a hard process data having a greater influence on yield than the soft process data is used in the second stage. Specifically: … David (page 9, ¶0035) teaches adjusting yield calculations using feedback from previous predictions, but again, does not disclose the claimed two-stage procedure, nor does it describe the use of soft and hard process data in separate prediction stages. Therefore, none of these references disclose or suggest a two-stage prediction procedure in which soft process data is used in the first stage and hard process data is used in the second stage, as claimed.“ Examiner respectfully disagrees. The David reference teaches the use of multiple algorithms and machine learning processes in the predication of semiconductor yield. In David paragraph [0030] it teaches the gathering of parametric test data (i.e. soft process data) that is later used in the yield prediction process. In David paragraph [0031] it teaches the gathering of defect data (i.e. hard process data) that is later used in the yield prediction process. In David paragraphs [0053] – [0056] it teaches the ability tp use multiple algorithms and machine learning methods that each do their own yield prediction and the results are combined (i.e. two-step process).
The rejection under 35 USC 103 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 7 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.
The term “greater” in claim 1 and 7 is a relative term which renders the claim indefinite. The term “greater” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear how much greater the influence on the yield the hard process data has over the soft process data.
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 7-12 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because it is not clear from the specification if the embodiment of the claimed invention is physical. Claim 7 states multiple units (a receiving unit, a prediction unit, a modifying unit, and an adjustment unit) that under the broadest reasonable interpretation in light of the specification include the units comprising of code with no other structure (see specification, paragraph 0016). See also Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d, 1400, 1402 (2007) (software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment). Examiner notes that if Applicant amends to overcome the software per se rejection, claims 7-12 will still be rejected under 35 U.S.C. 101. Claim 7-11 recites a mental process (see 101 rejection of claims 7-12 below). In addition, claim 7-12 do not have any additional elements that provide significantly more to the abstract idea.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A semiconductor manufacturing process prediction method. A method is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
executing, according to the process data, a prediction (a person can mentally make a prediction by a process of simply evaluating the data and making a judgement of what the data means.)
determining whether the prediction confidence is lower than a predetermined level; (a person can mentally determine the confidence of a prediction and compare it to a certain level by a process of simply evaluating the confidence of a prediction and making a judgement on if it is lower than a predetermined level.)
adjusting the prediction yield according to the process data. (a person can mentally adjust a prediction yield by a process of simply evaluating the data judgement on how it should be adjusted.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
obtaining a plurality of process data (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
via a machine learning model, to obtain a prediction confidence and a prediction yield; (Merely utilizing a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f))).
modifying the machine learning model, if the prediction confidence is lower than the predetermined level; and (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
the two-stage procedure includes a first stage and a second stage, a soft process data is used in the first stage, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
a hard process data having a greater influence on yield than the soft process data is used in the second stage. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
obtaining a plurality of process data (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)) )), Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
via a machine learning model, to obtain a prediction confidence and a prediction yield; (Merely utilizing a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
modifying the machine learning model, if the prediction confidence is lower than the predetermined level; and (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
the two-stage procedure includes a first stage and a second stage, a soft process data is used in the first stage, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
a hard process data having a greater influence on yield than the soft process data is used in the second stage. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein in the step of modifying the machine learning model, a parameter or a weight of the machine learning model is modified. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 3 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites wherein the in the step of modifying the machine learning model, a training dataset of the machine learning model is modified. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 4 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data, at least one physical measurement data and at least one physical defect data, in the step of executing the prediction, the prediction is executed according to the equipment setting data, the equipment detecting data, the electrical measurement data and the physical measurement data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data, at least one physical measurement data and at least one physical defect data, in the step of adjusting the prediction yield, the prediction yield is adjusted according to the physical defect data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 6 it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis applied to claim 5. Further, claim 6 recites wherein the prediction yield is adjusted via a statistical model, and the statistical model is different from the machine learning model. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 7, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim does not recite one of the four statutory categories (see rejection above). The 101 analysis below for claims 7-12 are provide herein for the sake of clarity if the above rejection were to be overcome.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
according to the process data, a prediction (a person can mentally make a prediction by a process of simply evaluating the data and making a judgement of what the data means.)
determining whether the prediction confidence is lower than a predetermined level, wherein if the prediction confidence is lower than the predetermined level,
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a receiving unit, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
configured to obtain a plurality of process data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))).
a prediction unit, configured to execute, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
via a machine learning model, to obtain a prediction confidence and a prediction yield; (Merely utilizing a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f))).
a modifying unit, configured to (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
the modifying unit modifies the machine learning model; and (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
an adjustment unit, configured to adjust the prediction yield according to the process data. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
the two-stage procedure includes a first stage and a second stage, a soft process data is used in the first stage, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
a hard process data having a greater influence on yield than the soft process data is used in the second stage. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a receiving unit, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
configured to obtain a plurality of process data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)) Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
a prediction unit, configured to execute, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
via a machine learning model, to obtain a prediction confidence and a prediction yield; (Merely utilizing a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
a modifying unit, configured to (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
the modifying unit modifies the machine learning model; and (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
an adjustment unit, configured to adjust the prediction yield according to the process data. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
the two-stage procedure includes a first stage and a second stage, a soft process data is used in the first stage, (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
a hard process data having a greater influence on yield than the soft process data is used in the second stage. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Furthermore, the additional element is directed to generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of significantly more.).
Regarding claim 8 it is dependent upon claim 7, and thereby incorporates the limitations
of, and corresponding analysis applied to claim 7. Further, claim 8 recites wherein the modifying unit modifies a parameter or a weight of the machine learning model. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 9 it is dependent upon claim 7, and thereby incorporates the limitations
of, and corresponding analysis applied to claim 7. Further, claim 9 recites wherein the modifying unit modifies a training dataset of the machine learning model. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 10 it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Further, claim 10 recites wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data, at least one physical measurement data and at least one physical defect data, and the prediction unit executes the prediction according to the equipment setting data, the equipment detecting data, the electrical measurement data and the physical measurement data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 11 it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis applied to claim 7. Further, claim 11 recites wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data, at least one physical measurement data and at least one physical defect data, and the adjustment unit adjusts the prediction yield according to the physical defect data. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Regarding claim 12 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 12 recites wherein the adjustment unit adjusts the prediction yield via a statistical model, and the statistical model is different from the machine learning model. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more).
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-8, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. Pub. No.: US 20190286983 in view of Vajaria et al. Patent No.: US 10290088 B2, David et al. Pub. No.: US 20190064253 A1, and Fabish Pub No. US 20210125053 A1.
Regarding Claim 1 Jung teaches A semiconductor manufacturing process prediction method, comprising: obtaining a plurality of process data; (Jung, page 8, paragraphs 0045 – 0048 teaches the extracting of a plurality of data from the semiconductor manufacturing process for use in a machine learning algorithm to predict yield) executing, according to the process data, a prediction via a machine learning model, to obtain a … and a prediction yield; (Jung, page 9-10, paragraphs 0074 – 0084 teaches the use of various neural network implementations (i.e., machine learning models) to make predictions on the yield of semiconductor manufacturing processes.)
Jung does not teach …prediction confidence… However, Vajaria in analogous art teaches this limitation (Vajaria, page 10-11, column 6 line 65 – 70 and column 7 line 0-10, teaches a semiconductor manufacturing process prediction method that produces a prediction confidence along with its predicted yield.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Vajarias teaching of producing a prediction confidence along with a prediction yield with Jungs teaching of generating a predicted yield using machine learning. The motivation to do so would be to allow for the machine learning process to evaluate its prediction performance and allow it to make adjustments in real time as new data is gathered.
Further the combination of Jung and Vajaria does not teach determining whether the prediction confidence is lower than a predetermined level; modifying the machine learning model, if the prediction confidence is lower than the predetermined level;
However, Fabish in analogous art teaches this limitation (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fabish’s teaching of comparing a prediction confidence with a predetermined threshold value with the combination of Jung and Vajaria’s teaching of producing a prediction confidence along with the predicted yield generated by a machine learning model. The motivation to do so would be to allow a user to set a predetermined level of when the model needs to reevaluate its training to allow for more accurate results to be produced.
Further the combination of Jung, Vajaria and Fabish does not teach and adjusting the prediction yield according to the process data. However, David in analogous art teaches this limitation (David, page 9, paragraph 0035, teaches the adjusting of the yield calculations using feedback from the previous predictions made.)
wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, the two-stage procedure includes a first stage and a second stage, (David, page 10-11, paragraph 0053-0056, teaches the use of multiple algorithms in multiple steps using different datas that are then combined to get the final yield prediction.) a soft process data is used in the first stage, (David, page 8, paragraph 0030, teaches the collection and use of parametric test data (i.e. soft process data) that can be used in one of the stages in the yield prediction process.) a hard process data having a greater influence on yield than the soft process data is used in the second stage. (David, page 8-9, paragraph 0031, teaches the collection and use of defect data (i.e. hard process data) that can be used in one of the stages in the yield prediction process.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine David’s teaching of adjusting the yield calculations using the feedback from the previous predictions with the combination of Jung, Vajaria, and Fabish’s teaching of generating a predicted yield. The motivation to do so would be to allow for the machine learning model to adjust and adapt to real world data in real time.
Regarding claim 2 the combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction method according to claim 1 (and thus the rejection of Claim 1 is incorporated), wherein in the step of modifying the machine learning model, a parameter or a weight of the machine learning model is modified. (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted)
Regarding claim 4 the combination of Jung, Vajaria, Fabish and David teaches The semiconductor manufacturing process prediction method according to claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data (David, page 8, paragraph 0030, teaches the use Wafer Acceptance Test data (i.e., electrical measurement data) in the execution of predictions.), at least one physical measurement data and at least one physical defect data, (Jung, page 8, paragraphs 0038 – 0044, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.).) in the step of executing the prediction, the prediction is executed according to the equipment setting data, the equipment detecting data, the electrical measurement data and the physical measurement data. (Jung, page 9, paragraphs 0075, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.) in the execution of the prediction making process.)
Regarding claim 5 the combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction method according to claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data (David, page 8, paragraph 0030, teaches the use Wafer Acceptance Test data (i.e., electrical measurement data) in the execution of predictions.), at least one physical measurement data and at least one physical defect data, (Jung, page 8, paragraphs 0038 – 0044, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.).) in the step of adjusting the prediction yield, the prediction yield is adjusted according to the physical defect data. (David, pages 8-9 and 11, paragraph 0031, 0035, and 0058, teaches the use of circuit probe data (i.e. physical defect data) that is used to determine predictions of semi-conductor yield and that prediction and data is fed back to improve the prediction yield calculation (i.e., prediction yield).)
Regarding claim 6 the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction method according to claim 5 (and thus the rejection of Claims 1 and 5 are incorporated), wherein the prediction yield is adjusted via a statistical model, and the statistical model is different from the machine learning model. (Jung, page 10, paragraph 0091, teaches the use of a machine learning algorithm to make an initial yield prediction that is then fed into a bagging or boosting model (i.e., a statistical model different from the machine learning model.) that then produces an adjusted predicted yield.)
Regarding claim 7 Jung teaches A semiconductor manufacturing process prediction device, comprising: a receiving unit, configured to obtain a plurality of process data; ; (Jung, page 8, paragraphs 0045 – 0048 teaches a prediction device that preforms the extracting of a plurality of data by an extracting unit (i.e., a receiving unit) from the semiconductor manufacturing process for use in a machine learning algorithm to predict yield) a prediction unit, configured to execute, according to the process data, a prediction via a machine learning model, to obtain a … and a prediction yield; (Jung, page 9-10, paragraphs 0074 – 0084 teaches the use of various neural network implementations (i.e., machine learning models) via a prediction unit to make predictions on the yield of semiconductor manufacturing processes.)
Jung does not teach …prediction confidence… However, Vajaria in analogous art teaches this limitation (Vajaria, page 10-11, column 6 line 65 – 70 and column 7 line 0-10, teaches a semiconductor manufacturing process prediction method that produces a prediction confidence along with its predicted yield.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Vajarias teaching of producing a prediction confidence along with a prediction yield with Jungs teaching of generating a predicted yield using machine learning. The motivation to do so would be to allow for the machine learning process to evaluate its prediction performance and allow it to make adjustments in real time as new data is gathered.
Further the combination of Jung and Vajaria does not teach a modifying unit, configured to determining whether the prediction confidence is lower than a predetermined level, wherein if the prediction confidence is lower than the predetermined level, the modifying unit modifies the machine learning model; However, Fabish in analogous art teaches this limitation (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted. The operations are taking place on a computer unit that is doing the modification (i.e., a modifying unit).)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Fabish’s teaching of comparing a prediction confidence with a predetermined threshold value with the combination of Jung and Vajaria’s teaching of producing a prediction confidence along with the predicted yield generated by a machine learning model. The motivation to do so would be to allow a user to set a predetermined level of when the model needs to reevaluate its training to allow for more accurate results to be produced.
Further the combination of Jung, Vajaria and Fabish does not teach and an adjustment unit, configured to adjust the prediction yield according to the process data. However, David in analogous art teaches this limitation (David, page 9, paragraph 0035, teaches the adjusting of the yield calculations using feedback from the previous predictions made.) wherein the semiconductor manufacturing process prediction method adopts a two-stage procedure for prediction, the two-stage procedure includes a first stage and a second stage, (David, page 10-11, paragraph 0053-0056, teaches the use of multiple algorithms in multiple steps using different datas that are then combined to get the final yield prediction.) a soft process data is used in the first stage, (David, page 8, paragraph 0030, teaches the collection and use of parametric test data (i.e. soft process data) that can be used in one of the stages in the yield prediction process.) a hard process data having a greater influence on yield than the soft process data is used in the second stage. (David, page 8-9, paragraph 0031, teaches the collection and use of defect data (i.e. hard process data) that can be used in one of the stages in the yield prediction process.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine David’s teaching of adjusting the yield calculations using the feedback from the previous predictions with the combination of Jung, Vajaria, and Fabish’s teaching of generating a predicted yield. The motivation to do so would be to allow for the machine learning model to adjust and adapt to real world data in real time.
Regarding claim 8 the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction device according to claim 7 (and thus the rejection of Claim 7 is incorporated), wherein the modifying unit modifies a parameter or a weight of the machine learning model. (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted)
Regarding claim 10 the combination of combination of Jung, Vajaria, Fabish and David teaches The semiconductor manufacturing process prediction device according to claim 7 (and thus the rejection of Claim 7 is incorporated), wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data (David, page 8, paragraph 0030, teaches the use Wafer Acceptance Test data (i.e., electrical measurement data) in the execution of predictions.), at least one physical measurement data and at least one physical defect data, (Jung, page 8, paragraphs 0038 – 0044, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.).) and the prediction unit executes the prediction according to the equipment setting data, the equipment detecting data, the electrical measurement data and the physical measurement data. (Jung, page 9, paragraphs 0075, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.) in the execution of the prediction making process.)
Regarding claim 11 the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction device according to claim 7 (and thus the rejection of Claim 7 is incorporated), wherein the process data includes at least one equipment setting data, at least one equipment detecting data, at least one electrical measurement data(David, page 8, paragraph 0030, teaches the use Wafer Acceptance Test data (i.e., electrical measurement data) in the execution of predictions.), at least one physical measurement data and at least one physical defect data, (Jung, page 8, paragraphs 0038 – 0044, teaches the use of production-related data (i.e., equipment settings data), equipment-related data (i.e., equipment detecting data), measurement data (i.e., physical measurement data) and fault data (i.e., physical defect data.).) and the adjustment unit adjusts the prediction yield according to the physical defect data. (David, pages 8-9 and 11, paragraph 0031, 0035, and 0058, teaches the use of circuit probe data (i.e. physical defect data) that is used to determine predictions of semi-conductor yield and that prediction and data is fed back to improve the prediction yield calculation (i.e., prediction yield).)
Regarding claim 12 the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction device according to claim 11 (and thus the rejection of Claims 7 and 11 are incorporated), wherein the adjustment unit adjusts the prediction yield via a statistical model, and the statistical model is different from the machine learning model. (Jung, page 10, paragraph 0091, teaches the use of a machine learning algorithm to make an initial yield prediction that is then fed into a bagging or boosting model (i.e., a statistical model different from the machine learning model.) that then produces an adjusted predicted yield.)
Claims 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. Pub. No.: US 20190286983 in view of Vajaria et al. Patent No.: US 10290088 B2, David et al. Pub. No.: US 20190064253 A1, and Fabish Pub No. US 20210125053 A1., and further in view of Shiue et al. Pub No.: US 20210406346 A1.
Regarding claims 3, the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction method according to claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the in the step of modifying the machine learning model, (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted)
The combination of Jung, Vajaria, Fabish and David does not teach a training dataset of the machine learning model is modified. However, Shiue in analogous art teaches this limitation (Shiue, page 24, paragraph 0038, teaches the fine-tuning (i.e., adjusting) of a training dataset after the prediction process in order to optimize the model’s performance.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Shiue’s teaching of adjusting the training dataset after the prediction process with the combination of Jung, Vajaria, Fabish, and David’s teaching of A semiconductor manufacturing prediction method. The motivation to do so would be to enable the machine learning algorithm to look at the training data and remove any outliers or anomalous data that might be skewing the prediction results.
Regarding claim 9 the combination of combination of Jung, Vajaria, Fabish and David teaches the semiconductor manufacturing process prediction device according to claim 7 (and thus the rejection of Claim 7 is incorporated), wherein the modifying unit (Fabish, page 67, paragraph 0209 - 0210, teaches the use of a prediction confidence that is compared to a predetermined threshold (i.e., level) and if it is determined to be below that level the weights of the NNA (i.e., machine learning model) are adjusted)
The combination of Jung, Vajaria, Fabish and David does not teach modifies a training dataset of the machine learning model. However, Shiue in analogous art teaches this limitation (Shiue, page 24, paragraph 0038, teaches the fine-tuning (i.e., adjusting) of a training dataset after the prediction process in order to optimize the model’s performance.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Shiue’s teaching of adjusting the training dataset after the prediction process with the combination of Jung, Vajaria, Fabish, and David’s teaching of A semiconductor manufacturing prediction method. The motivation to do so would be to enable the machine learning algorithm to look at the training data and remove any outliers or anomalous data that might be skewing the prediction results.
Conclusion
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