CTNF 18/791,686 CTNF 79569 DETAILED ACTION DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim (s) 1-9, 12-15 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by U.S. Patent Publication 2017/0109646 to David . David shows techniques for measuring and/or compensating for process variations in a semiconductor manufacturing process. Machine learning algorithms are used on extensive sets of input data, including upstream data, to organize and pre-process the input data, and to correlate the input data to specific features of interest (variations of optical characteristics based on inputs like temperature/pressure). The correlations can then be used to make process adjustments. Referring to claims 1, 13, and 15, David shows an information processing apparatus configured to predict a variation amount in an optical characteristic of an optical system provided in an exposure apparatus by inputting a target temperature of the optical system to a learning model ([0078] temperature sensors as model inputs) or predict a target temperature of an optical system by inputting a variation amount in an optical system ([0073], [0075], [0080] optical/spectral data as model inputs and outputs), wherein the learning model is a learning model obtained by machine learning ([0029] “Data from prior production runs can be used to create a model for a target parameter, and data from a current production run can be input to the model to generate a prediction for the target parameter…”, [0037] “Machine learning algorithms can be used to create new approaches to data analysis…”, [0046] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets.”). It is unclear what the distinction is between claims 2 and 3. David shows both training of the model with inputs and known outputs and then using that information to execute process control by predicting outputs based on the inputs. Referring to claim 2, David shows wherein the information processing apparatus is configured to predict the variation amount in the optical characteristic of the optical system by further inputting at least one of a measurement result of a temperature of the optical system ([0078] “Process equipment measurements or metrics can also be used as inputs to the algorithm, such as gas flow sensors, power sensors, pressure sensors, temperature sensors, current sensors, voltage sensors, etc.”), a measurement result of a pressure of the optical system, a target pressure of the optical system, a measurement result of a temperature in a chamber in which an exposure light source, an original stage, the optical system and a substrate stage in the exposure apparatus are accommodated, a measurement result of a humidity in the chamber, a measurement result of a pressure in the chamber, a target temperature in the chamber, a target humidity in the chamber, a target pressure in the chamber, a target position of the original stage, a target position of the optical system, a target position of the substrate stage, a configuration of the optical system, a target illuminance of the exposure light source when an exposure is performed, a type of an original used when the exposure is performed, and a movement speed of the substrate stage when the exposure is performed, to the learning model ([0089]–[0090] “For each input vector … a score will be generated… The score is a prediction of the target made by the model… If the target was a parametric test value, then the score will be a prediction of that parametric test value.”) Referring to claim 3, David shows wherein input data of the learning model is created from at least one of the target temperature of the optical system, a measurement result of a temperature of the optical system ([0078] “Process equipment measurements or metrics can also be used as inputs to the algorithm, such as gas flow sensors, power sensors, pressure sensors, temperature sensors, current sensors, voltage sensors, etc.”), a measurement result of a pressure of the optical system, a target pressure of the optical system, a measurement result of a temperature in a chamber in which an exposure light source, an original stage, the optical system and a substrate stage in the exposure apparatus are accommodated, a measurement result of a humidity in the chamber, a measurement result of a pressure in the chamber, a target temperature in the chamber, a target humidity in the chamber, a target pressure in the chamber, a target position of the original stage, a target position of the optical system, a target position of the substrate stage, a configuration of the optical system, a target illuminance of the exposure light source when an exposure is performed, a type of an original used when the exposure is performed, and a movement speed of the substrate stage when the exposure is performed. Referring to claim 4, David shows wherein correct answer data of the learning model is created from a measurement result of the variation amount in the optical characteristic of the optical system ([0046] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets.”). Referring to claim 5, see citations for claims 3 and 4. Referring to claim 6, David shows wherein the information processing apparatus is configured to update the learning model when a difference between the variation amount in the optical characteristic of the optical system predicted from the learning model and a measurement result of the variation amount in the optical characteristic of the optical system is larger than a predetermined threshold ([0093] “As new input data and corresponding target data is generated, the algorithm can be retrained so as to produce a better model that will give better scores.”, [0126] “If instead the model is not predicting within the defined limits as compared to actual measured overlay error, then all data collected up to that point is used to retrain the model…”). Referring to claim 7, David shows wherein the information processing apparatus is configured to: create the input data and the correct answer data in a plurality of exposure apparatuses ([0088] “Input data may also be collected from the process equipment 720 during steps for etch, CMP, gap fill, blanket, RTP, etc., and may include process variables such as process duration, temperature, pressure, RF frequency, etc. Input data may also include metrology data 730 such a CD, wafer shape, film thickness, film resistivity, inline or in-situ measurements, etc.” shows that the model may use data from different apparatus types, [0111] “…all available wafer geometry parameters… scanner parameters… CD measurements… film property parameters… parameters of other overlay measurements such as DBO and IBO… are used as inputs to the training model, with the corresponding actual overlay error as the target.” shows a model built from measurements across several different tools / apparatuses; and create the learning model by performing machine learning based on the created input data and the created correct answer data ([0046] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets.”). Referring to claim 8, David shows wherein the optical characteristic of the optical system includes at least one of a focus and a distortion of the optical system ([0092] “…a model whose target is a lithography apparatus control parameter, such as focus , power, or x-y direction control.”) Referring to claim 9, David shows an exposure apparatus configured to expose a substrate so as to transfer a pattern formed on an original onto the substrate, comprising: a projecting optical system configured to guide exposure light passing through the original to the substrate ([0052] “A lithographic apparatus is a machine that applies a desired pattern onto a substrate… A circuit pattern … is generated by a patterning device, usually referred to as a mask or a reticle, which transfers the pattern onto a target.”); a projecting optical system adjustment unit configured to control a temperature of the projecting optical system ([0078] “Process equipment measurements or metrics can also be used as inputs to the algorithm, such as gas flow sensors, power sensors, pressure sensors, temperature sensors, current sensors, voltage sensors, etc.”); the information processing apparatus according to claim 1 configured to predict a variation amount in an optical characteristic of the projecting optical system )[0090] “The score is a prediction of the target made by the model, given the input data.”); and a controller configured to control a position of the projecting optical system based on the variation amount in the optical characteristic of the projecting optical system predicted by the information processing apparatus ([0092] “…the target is a lithography apparatus control parameter, such as focus, power, or x-y direction control.”). Referring to claim 12, David shows exposing the substrate by using the exposure apparatus according to claim 9 ([0052] “A lithographic apparatus is a machine that applies a desired pattern onto a substrate… The circuit pattern … is generated by a patterning device, usually referred to as a mask or a reticle, which transfers the pattern onto a target.”); developing the exposed substrate ([0031] “In between these steps, lithographic processing allows areas of wafer to be patterned with an image, then a mask is used to expose photoresist that has been applied across the wafer, and the exposed photoresist is developed.”); and manufacturing an article from the developed substrate ([0031] “The pattern is then etched to remove selected portions of the developed photoresist, and these steps are repeated to create multiple layers. Finally, metallization is a specialized deposition process that forms electrical interconnections between various devices/circuits formed on the wafer.”). Referring to claim 14, David shows recording a program for causing a computer to perform an information process, the program causing the computer to execute the information processing method according to claim 13 ([0196] “The models being sent may take one of many forms. The model can be expressed as either source code, compiled binary code, or a parameter list to allow the model to be recreated on the client side.”) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication 2017/0109646 to David as shown above in view of U.S. Patent Publication 2009/0153813 to Shiraishi . With respect to what was shown above, David shows a lithography and exposure apparatus with an optical system for controlling optical characteristics as well as other inputs via a machine learning algorithm. David does not show the full details of an exposure system as claimed below. Shiraishi shows an exposure light source configured to emit the exposure light ([0037]: “The illumination optical system IL includes an exposure light source…”); an illuminating optical system configured to guide the exposure light from the exposure light source to the original ([0031]: “an illumination optical system IL for illuminating exposure light EL on the mask M…” [0037] describes its components.); an original stage on which the original is placed ([0031, 0039]: “a mask stage MST for holding and moving a mask M”); a substrate stage on which the substrate is placed ([0031], [0042]-[0045]: “a substrate stage PST having a substrate holder PH for holding a substrate P”); a chamber in which the exposure light source, the illuminating optical system, the original stage, the projecting optical system, and the substrate stage are accommodated ([0035]: “the exposure apparatus EX is accommodated in a chamber apparatus CH.” and “the chamber apparatus CH shown in FIG. 1 is configured to accommodate the exposure apparatus EX in its entirety…”); the projecting optical system adjustment unit configured to control a pressure of the projecting optical system ([0041]: “the imaging characteristic adjustment unit LC… may include a pressure regulating mechanism for regulating the pressure of a gas present in a space between some of the optical elements held within the lens barrel PK.” ); a chamber adjustment unit configured to control a temperature, a humidity, and a pressure in the chamber ([0035]: “The internal space of the chamber apparatus CH… is air-conditioned by means of an air conditioning system 300.” It “serves to keep the environment… including the temperature, the humidity and the pressure in a desired state.”); the controller configured to control a position of the illuminating optical system, a position of the original stage, a position of the substrate stage, and an illuminance of the exposure light source ([0031]: control unit CONT generally controls overall operations. [0039]: control unit controls mask stage position. [0045]: control unit controls substrate stage position); and a measurement unit configured to measure the variation amount in the optical characteristic of the projecting optical system ([0113], [0128] measuring projection state, aberration, wavefront aberration, and temperature-driven changes). It would have been obvious to one of ordinary skill in the art before the effecting filing date of the claimed invention to use the machine learning concepts presented in David to control the lithograph/optical apparatus shown in Shiraishi because they serve the same purpose and are from the same field of invention (lithograph, optical wafer processing). A person of ordinary skill in the art would have been motivated to combine Shiraishi with David because Shiraishi identifies a need to determine exposure conditions from numerous interacting operating variables affecting projection state, and teaches storing and applying correction information derived from measured process behavior. Given that machine learning is an established technique for deriving predictive control relationships from measured multivariable data, its use here would have been no more than the application of a known control technique to improve the accuracy and adaptability of Shiraishi’s known lithographic process control scheme, yielding predictable benefits such as better aberration compensation and improved imaging consistency. Referring to claim 11, David shows a storing unit configured to record at least one of a target temperature of the projecting optical system, a measurement result of a temperature of the projecting optical system ([0078] “Process equipment measurements or metrics can also be used as inputs to the algorithm, such as gas flow sensors, power sensors, pressure sensors, temperature sensors, current sensors, voltage sensors, etc.”), a measurement result of a pressure of the projecting optical system, a target pressure of the projecting optical system, a measurement result of a temperature in the chamber, a measurement result of a humidity in the chamber, a measurement result of a pressure in the chamber, a target temperature in the chamber, a target humidity in the chamber, a target pressure in the chamber, a target position of the illuminating optical system, a target position of the original stage, a target position of the projecting optical system, a target position of the substrate stage, a configuration of the illuminating optical system, a target illuminance of the exposure light source when the exposure is performed, a type of the original, and a movement speed of the substrate stage when the exposure is performed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL D MASINICK whose telephone number is (571)272-3746. The examiner can normally be reached M-Th, 8-6, F, 8-12. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Brooks can be reached at (571) 272-8112. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHAEL D MASINICK/Primary Examiner, Art Unit 6221 Application/Control Number: 18/791,686 Page 2 Art Unit: 6221 Application/Control Number: 18/791,686 Page 3 Art Unit: 6221 Application/Control Number: 18/791,686 Page 4 Art Unit: 6221 Application/Control Number: 18/791,686 Page 5 Art Unit: 6221 Application/Control Number: 18/791,686 Page 6 Art Unit: 6221 Application/Control Number: 18/791,686 Page 7 Art Unit: 6221 Application/Control Number: 18/791,686 Page 8 Art Unit: 6221 Application/Control Number: 18/791,686 Page 9 Art Unit: 6221 Application/Control Number: 18/791,686 Page 10 Art Unit: 6221
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