DETAILED ACTION
Drawings
Previous objection is withdrawn in view of the Applicant’s amendment filed on 01/15/2026.
Specification
Previous objection is withdrawn in view of the Applicant’s amendment filed on 01/15/2026.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6, 8-11, 13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Budach et al., US-PGPUB 2021/0132594 (hereinafter Budach) in view of Yamada et al., US-PGPUB 2011/0057114 (hereinafter Yamada)
Regarding Claims 1, 8 and 15. Budach discloses a drift compensation in a digital photolithography tool (Abstract, drift), comprising:
receiving training sensor data (Fig. 12, 1220, measurement data; Paragraph [0057]; [0104]); receiving training drift data correlated to the training sensor data (Paragraph [0101], displacement data); generating a trained machine learning model by training a machine learning model using the training sensor data and the training drift data (Paragraphs [0101]-[0102], displacement data as input to the machine learning model; Fig. 4, Paragraphs [0091]-[0094], Figs. 3-6; Paragraph [0104], sensor data such as temperature, pressure, humidity, as inputs to the machine learning model),
processing a substrate using a digital lithography tool, comprising a sensor configured to produce sensor data, and an eye module (Figs. 1, 11; Paragraph [0075]); measuring drift of the digital lithography tool by measuring locations of at least two alignment marks on the substrate using the eye module (Paragraphs [0101]-[0102], Paragraphs [0091]-[0094]); providing the sensor data and the measured drift to the trained machine learning model (Paragraphs [0101]-[0102], displacement data as input to the machine learning model; Paragraph [0104], sensor data such as temperature, pressure, humidity, as inputs to the machine learning model): receiving, from the trained machine learning model, predicted tool drift, wherein the machine learning model comprises at least an ML trend model, an ML increment model and an ML remaining model, each configured to predict a respective component of drift (Fig. 9, showing the displacements, difference in displacements and the rate of change of the measured drift, as the amount of drift is shown in time; Paragraphs [0105]-[0116], Fig. 3; Paragraph [0090]; Paragraph [0011], predict drift) (Note: while the Applicant can be their own lexicographer, the limitations “ML trend model, ML increment model and ML remaining model” are merely labelings, as many different techniques are discussed under their respective labelings in the Applicant’s specification); and adjusting the eye module based on the predicted tool drift (Paragraph [0006], displacing the electron beam for offset correction)
Budah discloses lithography using electron beam, but does not disclose the eye module being an optical projection module configured to project an exposure patten onto the substrate.
Yamada discloses electron beam lithography in which the electron beam is defected to project an image of the pattern of the exposure mask on the substrate (Paragraphs [0042], [0003], [0013]-[0014])
At the time of the invention, it would have been obvious to combine the teachings of Budah and Yamada and adjust the eye module on the predicted tool drift on a electron beam lithography which includes the eye module being an optical projection module configured to project an exposure patten onto the substrate, so as to perform highly accurate exposure.
Regarding Claims 2, 9 and 16. Budach discloses the sensor data comprises one of a temperature, humidity, and pressure (Paragraph [0104], sensor data such as temperature, pressure, humidity, as inputs to the machine learning model)
Regarding Claims 3, 10 and 17. Budach discloses measuring the drift comprises measuring locations of a first alignment mark and a second alignment mark on the substrate (Paragraphs [0040], [0101]-[0102], Paragraphs [0091]-[0094])
Regarding Claims 4, 11 and 18. Budach discloses measuring the locations of the first alignment mark and the second alignment mark is performed using one of the eye module and a second eye module (Figs. 1, 10)
Regarding Claims 6, 13 and 20. Budach discloses the machine learning model comprises: an ML trend model receiving measured drift and configured to generate a trend prediction of future drift, an ML increment model receiving rate of change of measured drift and configured generate an increment prediction of future drift, and an ML remaining model receiving a difference between the measured drift and the trend prediction of future drift and the increment prediction of future drift, and configured to generate a predicted remaining value comprising a predicted difference between future drift, the trend prediction of future drift, and the increment prediction prediction of future drift (Fig. 9, showing the displacements, difference in displacements and the rate of change of the measured drift, Paragraphs [0105]-[0116]; Fig. 3, Paragraph [0090])
6. Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Budach et al., US-PGPUB 2021/0132594 in view of Yamada, US-PGPUB 2011/0057114 as applied to Claims 1, 8 and 15, and further in view of Raquel et al., US-PGPUB 20140358480 (hereinafter Raquel)
Regarding Claims 5, 12 and 19. Budach discloses predicting tool drift and altering operations of the digital lithography tool (Fig. 11)
The modified Budach does not explicitly disclose providing a warning based on the predicted tool drift, the warning comprising updating a user display and altering operation of the digital lithography tool.
Raquel discloses providing an alert on a display and adjusting the operation of the tool associated with lithography (Paragraphs [0006], [0016]; Paragraph [0003]; Figs. 1-3);
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Raquel in the modified Budach and provide a warning based on the predicted tool drift, the warning comprising updating a user display and altering operation of the digital lithography tool, so as to efficiently correct the tool drifting.
7. Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Budach, US-PGPUB 2021/0132594 in view of Yamada, US-PGPUB 2011/0057114 as applied Claims 6 and 13 and further in view of David et al., US-PGPUB 20190064253 (hereinafter David)
Regarding Claims 7 and 14. The modified Budach does not disclose one of the ML trend model, the ML increment model, and the ML remaining model comprises a linear regression model.
David discloses machine learning that includes linear regression (Paragraphs [0028]-[0029]) for predicting various aspects of the semiconductor process, that includes drifts in data (Paragraph [0036]; Abstract; Paragraphs [0020]-[0026])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of David in the modified Budach and have one of the ML trend model, the ML increment model, and the ML remaining model comprises a linear regression model, so as to efficiently address drifting problem in lithography process.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot in view of new grounds of rejection and updated rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYUN D PARK whose telephone number is (571)270-7922. The examiner can normally be reached 11-4.
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/HYUN D PARK/Primary Examiner, Art Unit 2857
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