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 .
Non-Final Office Action
DETAILED ACTION
Examiner’s Notes
(a) Claim date: 02/28/2023
(b) Priority date: NA
(c) Field: Generating a machine-learning model for a process chamber by using earlier machine-learning model data and metrology data.
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.Claims 1-20, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the prior art of record “Hong” <US 20230222394 A1>.(As to claim 1, 8, 15, Hong discloses):1. A method, comprising:
identifying a first machine-learning model trained to generate first predictive data for a first process chamber [0007: “Training the MLM may include receiving training data that includes first sensor data indicating a first state of an environment of a first process chamber”];
obtaining metrology data associated with a substrate produced by a second process chamber [0027: “The training data further includes metrology data including process result measurements and location data indicating first locations across a surface of the substrate…”; “…second process chamber processing a second substrate”]; and
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training a second machine-learning model based on the first machine-learning model and the metrology data [Fig. 8. The second ML model (814) originates from the first ML model (802) and metrology data (806) ][ 0086: “using the first set of features in the training set (e.g., CD bias data 202) and to generate a second trained machine learning model (e.g. regression model 208) using the second set of features in the training set (e.g., process tool data 216)”; Note: it is unclear whether the “metrology data” is coming from the first or the second process chamber.],
wherein the second machine-learning model is trained to generate second predictive data associated with the second process chamber [0007: “Training the MLM may further include causing a regression to be performed using the encoded training data. The method may further include receiving second sensor data indicating a second state of an environment of a second process chamber processing a second substrate”] [Fig. 8 also has identical disclosure].
(As to claim 2, 9, 16, Hong discloses):2. The method of claim 1, wherein the second predictive data comprises at least one of predictive metrology data or predictive process control variables data [Fig. 9, 916, 906].
(As to claim 3, 10, 17, Hong discloses):3. The method of claim 1, wherein training the second machine-learning model is further based on one or more process sensitivity values obtained from the first machine- learning model [Fig. 5B, ML model 520, accommodates process values (560)].
(As to claim 4, 11 Hong discloses):4. The method of claim 1, further comprising: performing a corrective action based on the second predictive data [Fig. 5B, corrective action (506) is performed based on second predictive data (508)].
(As to claim 5, 12, 19, Hong discloses):5. The method of claim 4, wherein the corrective action comprises applying an adjustment value to a knob associated with the second process chamber [0021: “processing chambers and processing may adjust over time”; note: here, adjust is functionally equivalent to “knob”].
(As to claim 6, 13, 20, Hong discloses):6. The method of claim 1, wherein the second process chamber comprises the first process chamber in a different state [0027: “The training data further includes metrology data including process result measurements and location data indicating first locations across a surface of the substrate…”; “…second process chamber processing a second substrate”] [Note: unclear limitation. Not sure how it is possible that one physical chamber can accommodate another identical physical chamber.].
(As to claim 7, 14, Hong discloses):7. The method of claim 1, wherein training the second machine-learning model comprises performing a transfer learning operation associated with the first machine- learning model [Fig. 8, depicts how the first machine learning model is being transferred is many steps of the flow chart (i.e. gradually modified)].
(As to claim 18, Hong discloses):18. The method of claim 15, further comprising: performing a corrective action based on the predictive dimension [Fig. 9, 914].
Conclusion
The prior art made of record in the form PTO-892 are not relied upon is considered pertinent to applicant's disclosure.Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.Contact information:Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED ALAM whose telephone number is (571) 270-1507, email address: [mohammed.alam@uspto.gov] and fax number (571) 270-2507. The examiner can normally be reached on 10AM to 4PM (EST), Monday to Friday. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's Supervisor, JACK CHIANG can be reached on (571) 272-7483. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300./Mohammed Alam/Primary Examiner, Art Unit 2851