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 .
THIS ACTION IS MADE FINAL.
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 02/04/2026, Applicant, on 05/04/2026.
Status of Claims
Claims 1-21 and 23-24 are original.
Claim 22 is canceled.
Claims 1-21 and 23-24 are currently pending following this response.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/26/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered and found to be persuasive.
While the present claims involve physical models and machine learning which can be categorized as mental process or mathematical concept, the combination of physical models and machine learning using fed-forward (pre-process) and fed-back (post-process) data is presented as a specific method to improve the final value of parameter. The claims integrate any abstract idea into a practical application by improving a technical process of structure metrology.
Accordingly, the Examiner withdraws the rejections of the pending claims under 35 USC § 101 in the present office action.
Response to Arguments - 35 USC § 102
The arguments have been fully considered, but they are not persuasive.
The Examiner respectfully disagrees.
The Examiner explained in details the anticipation of Yerushalmi to claim 1 in the 35 USC § 102 (below). Nevertheless, the Examiner submits that the present independent claims are broad in a sense that Yerushalmi reads on to the independent claims. Claim 1 of Yerushalmi teaches the first machine learning module receives measured signals which is equivalent to the claimed extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals.
The existing recipes are from at least one different production line running a different process step is equivalent to receiving and analyzing pre-process and post-process steps metrology signals because the pre-process and post-0process steps are included in the limitation “one different production line running a different process step”.
Applicant’s arguments in page 22 about the machine learning models do not determine parameters are not persuasive because Yerushalmi teaches in claim 1 that the second machine learning model determine a final recipe or setting which is equivalent to determining parameters. Further, Yerushalmi teaches in claim 1 “a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function” wherein the second machine learning model determine a final recipe or settings from the set of recipes indicates that the second machine learning model also receive recipe or receive second metrology signal (Applicant’s arguments page 22).
Applicant, in pages 23-24, is arguing the same arguments that were addressed by the Examiner (above).
As a result, the Examiner maintains the rejections of the pending claims under 35 USC § 102 in the present office action.
Allowable Subject Matter
Claims 5, 7, 13, 15, 19, 21, and 23 objected to as being dependent upon rejected base claims, but it appears they would be allowed if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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 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.
Claims 1-4, 6, 8-12, 14, 16-18, 20, and 24 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Yerushalmi Liran (TW1898147B) hereinafter Liran
Regarding claim 1. Liran teaches A method for measuring at least one parameter of interest from a structure of interest (SOI), comprising: obtaining post-process step measured signals from a metrology device for a SOI on one or more samples at a post-process step; extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals and at least one of a value of a first parameter for the SOI at a pre-process step that is fed forward to the post-process physical model, a value of a second parameter for the SOI at the post-process step that is fed back to the post-process physical model, and a combination thereof; predicting a final value of the second parameter for the SOI at the post-process step from a trained post-process machine learning model based on the post-process measurement results extracted from the post-process physical model; and providing at least the final value of the second parameter for the SOI [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest; and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein a first machine learning module receives a first metrology signal and determine parameters, a second machine learning module receives a second metrology signal and determine parameters. Wherein the “wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” is equivalent to receiving and analyzing pre-process and post-process steps metrology signals. Wherein “whereby the second machine learning module guides the development of the first machine learning module, and wherein “the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function” is equivalent to extracting post-process measurement results from a post-process physical model for the SOI based on the post-process step measured signals and at least one of a value of a first parameter for the SOI at a pre-process step and calculating a final value].
Regarding claim 2. wherein the value of the first parameter for the SOI is determined from at least one of a pre-process physical model and a trained pre-process machine learning model based on pre-process step measured signals obtained from the SOI on the one or more samples at the pre-process step [Liran, claim 1, Liran teaches “a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” emphasis added wherein samples from a pre-process step].
Regarding claim 3. further comprising: obtaining the pre-process step measured signals from the metrology device for the SOI on the one or more samples at the pre-process step; and determining the value of the first parameter from extracted pre-process measurement results for the SOI from the pre-process physical model based on the pre-process step measured signals [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” emphasis added wherein samples from a pre-process step].
Regarding claim 4. further comprising: obtaining the pre-process step measured signals from the metrology device for the SOI on the one or more samples at the pre-process step; extracting pre-process measurement results for the SOI from the pre-process physical model based on the pre-process step measured signals; and predicting the value of the first parameter for the SOI at the pre-process step from the trained pre-process machine learning model based on the pre-process measurement results extracted from the pre-process physical model [Liran, claim 1, Liran teaches “A system for semiconductor inspection and metrology, comprising: a first machine learning module configured to determine a set of recipes, wherein the first machine learning module receives measured signals, wherein each recipe in the set of recipes converts the measured signals into a parameter of interest…wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step” wherein predicting parameters value from first (pre-process) metrology measurements].
Regarding claim 6. wherein the final value of the second parameter for the SOI at the post-process step is predicted from the trained post-process machine learning model further based on a pre-process step measured signals from the SOI at the pre-process step [Liran, claim 1, Liran teaches “and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein “whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes” is equivalent to “wherein the final value of the second parameter for the SOI at the post-process step is predicted from the trained post-process machine learning model further based on a pre-process step measured signals from the SOI at the pre-process step”].
Regarding claim 8. further comprising determining one or more additional parameters for the SOI using at least one of the post-process physical model or the trained post- process machine learning model [Liran, claim 1, Liran teaches “and a second machine learning module configured to determine a final recipe or settings from the set of recipes and a cost function, wherein the second machine learning module determines the settings if the set of recipes does not pass an evaluation using the cost function, whereby the second machine learning module guides the development of the first machine learning module, and wherein the second machine learning module determines the final recipe from the set of recipes that passes the evaluation using the cost function, wherein the second machine learning module is trained to evaluate the performance of multiple existing recipes, wherein the existing recipes are from at least one different production line running the same product, at least one different production line running a different product, at least one different production line running a different process step, or at least one different production line running a different goal” wherein the second machine learning module determines a final recipe is equivalent to the determining an additional parameter from an additional machine learning model].
Regarding claim 9, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 9 is directed to a system which is anticipated by Liran claim 1.
Regarding claims 10-12, 14, and 16, claims 10-12, 14, and 16 recite substantially similar limitations as claim 2-4, 6, and 8, respectively; therefore, claims 10-12, 14, and 16 are rejected with the same rationale, reasoning, and motivation provided above for claims 2-4, 6, and 8, respectively. Claims 2-4, 6, and 8 are method claims while claims 10-12, 14, and 16 are directed to a system which is anticipated by Liran claim 1.
Regarding claim 17, the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 17 is directed to a system which is anticipated by Liran claim 1.
Regarding claims 18, 20, and 24, claims 18, 20, and 24 recite substantially similar limitations as claim 2, 6, and 8, respectively; therefore, claims 18, 20, and 24 are rejected with the same rationale, reasoning, and motivation provided above for claims 2, 6, and 8, respectively. Claims 2, 6, and 8 are method claims while claims 18, 20, and 24 are directed to a system which is anticipated by Liran claim 1.
Conclusion
Applicant's amendment and arguments dated 05/04/2026 necessitated the updating of the 35 USC § 101 and the 35 USC § 103 rejections of the pending claims presented in the present Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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