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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 13, 2026 has been entered.
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 1-9, 21, 23-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “learn a nonlinear correlation”. This judicial exception is not integrated into a practical application because using a computer to perform an abstract idea does not preclude the steps from being considered an abstract idea. See MPEP 2106.06(a)(2). The generically recited “control device” does not add a meaningful limitation to the abstract idea because it amounts to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed “learn a nonlinear correlation” is equivalent, under BRI, to a non-linear mathematical regression as evidenced by the below cited prior art (“fitting curve”; 111; Figure 6; [0045]) is a well-understood, routine, conventional computer function as recognized by the court decisions listed in MPEP § 2106.05(d).
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the “deposition device” must be shown or the features canceled from the claims. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: See drawing objection.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-5, and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Matsui; Miyako et al. (US 20190237337 A1). Matsui teaches An automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied, comprising: a chamber (21; Figure 4); a substrate holder (22; Figure 4) which is disposed in the chamber (21; Figure 4) and configured to support a substrate; a deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants) configured to deposit (S1; Figure 1) a thin film on the substrate; a temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]) configured to control a temperature of the substrate placed on the substrate holder (22; Figure 4); a raw material supply device (24+36; Figure 4; [0037]) configured to supply a raw material for deposition of the thin film to the substrate to the chamber (21; Figure 4) placed on the substrate holder (22; Figure 4); an analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) configured to analyze a property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film deposited on the substrate; a removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) configured to physically remove the thin film from the substrate so as to return the substrate to an initial state (S4; Figure 1; [0033]) prior to thin film deposition (S1; Figure 1; [0033]); and a control device (36; Figure 4-[0036]) which is connected to the deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants), the temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]), the raw material supply device (24+36; Figure 4; [0037]), the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1), and the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching), wherein the control device (36; Figure 4-[0036]) comprises at least one processor (CPU; [0036]) and a memory (“storage unit”; [0036]) storing instructions (“program”; [0036]) that, when executed by the processor (CPU; [0036]), cause the control device (36; Figure 4-[0036]) to: acquire, while the substrate remains disposed within the chamber (21; Figure 4), measurement data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) from the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) corresponding to the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, execute a machine correlation model (“fitting curve”; 111; Figure 6; [0045]-[0047]) while the substrate remains in the chamber (21; Figure 4) to learn a nonlinear correlation (“fitting curve”; 111; Figure 6; [0045]-[0047]) between a plurality of process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) used for the thin film deposition and properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, determine, based on the analyzed property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1), that the thin film fails to satisfy a predetermined quality criterion (r<ro; r>ro; S3; Figure 1), in response to the determination that the thin film fails to satisfy the predetermined quality criterion (r<ro; r>ro; S3; Figure 1), generate one or more control signals and transmit the one or more control signals to the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) to cause the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) to physically remove the thin film from the substrate as a discarded thin film after the thin film is physically removed, generate one or more control signals corresponding to modified process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) derived from the learned nonlinear correlation (“fitting curve”; 111; Figure 6; [0045]-[0047]), and deposit, by transmitting the one or more control signals to at least one of the deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants), the temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]), or the raw material supply device (24+36; Figure 4; [0037]), a new thin film on the substrate by directly applying modified process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) based on the correlation learned through machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]), and repeat the acquire, determine, remove, and deposit steps in a closed-loop manner (Figure 1) while the substrate remains in the chamber (21; Figure 4) until the analyzed property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the newly deposited thin film satisfies the predetermined quality criterion (r<ro; r>ro; S3; Figure 1), wherein the process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) include at least temperature, pressure, gas composition, gas flow rate, bias voltage, and deposition time and the properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film include at least thickness (S3; Figure 1), uniformity, crystallinity, and electrical characteristics, as claimed by claim 1
Matsui further teaches:
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the thin film includes a two-dimensional material, as claimed by claim 2. Applicant’s above and below italicized claim text is considered intended use claim requirements for the pending apparatus claims. Applicant has not provided sufficient distinguishing structural characteristics of Applicant's claimed invention to contrast the Examiner's cited prior art. When the structure recited in the reference is substantially identical to that of the claims, claimed properties or functions are presumed to be inherent. The Examiner notes MPEP 2112 which states the express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995) (affirmed a 35 U.S.C. 103 rejection based in part on inherent disclosure in one of the references). See also In re Grasselli, 713 F.2d 731, 739, 218 USPQ 769, 775 (Fed. Cir. 1983).
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the removal device (25+22+36,36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) is configured to generate at least one of plasma, laser, ion beam, and etching gas, and to remove the thin film from the substrate disposed in the chamber (21; Figure 4) using at least one of the plasma (plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching), laser, ion beam, and etching gas, as claimed by claim 3
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the raw material supply device (24+36; Figure 4; [0037]) is provided in plurality, as claimed by claim 4
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) comprises: an analysis source generating unit (26,29,102; Figure 4; [0036], [0042]-”coherent light”-Applicant’s 51; Figure 1; [0044]) generating an analysis source (104; Figure 4; [0041]) for analyzing the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film; and an analysis data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) acquisition unit (110+26; Figure 4; [0036]-Applicant’s 52; Figure 1; [0044]) for acquiring data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) corresponding to the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, as claimed by claim 5
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the control device (36; Figure 4-[0036]) is configured to terminate (“Finish”; Figure 1) the deposition of the thin film when the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film analyzed by the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is equal to or greater than the reference level (S3,S4; Figure 2; [0033]), as claimed by claim 8
Claim Rejections - 35 USC § 102/103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 7 and 27 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Matsui; Miyako et al. (US 20190237337 A1). Matsui is discussed above. Matsui is believed to teach:
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 1, wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is configured to analyze the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film by using at least one of scanning electron microscopy (SEM), transmission electron microscopy (TEM), reflection high-energy electron diffraction (RHEED), low-energy electron diffraction (LEED), ellipsometry ([0010]), and X-ray diffraction (XRD), as claimed by claim 7
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 21, wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is configured to analyze the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film by using at least one of scanning electron microscopy (SEM), transmission electron microscopy (TEM), reflection high-energy electron diffraction (RHEED), low-energy electron diffraction (LEED), ellipsometry ([0010]), and X-ray diffraction (XRD), as claimed by claim 27
In the event that Matsui’s discussion in [0010] for using ellipsometry in Matsui’s analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is not accepted, then, it would have been obvious to one of ordinary skill in the art at the time the invention was made for Matsui to use ellipsometry in Matsui’s analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1).
Motivation for Matsui to use ellipsometry in Matsui’s analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is for conventional wafer surface analysis means as taught by Matsui ([0010]).
Claim Rejections - 35 USC § 103
Claims 6 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Matsui; Miyako et al. (US 20190237337 A1) in view of Miya; Go et al. (US 20090223633 A1). Matsui is discussed above. Matsui further teaches:
wherein the analysis data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) acquisition unit (110+26; Figure 4; [0036]-Applicant’s 52; Figure 1; [0044]) is configured to include at least one of a fluorescent screen, a camera (6; Figure 4; [0036]), and an X-ray detector – claim 6, 26.
Matsui does not teach:
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 5, wherein the analysis source generating unit (26,29,102; Figure 4; [0036], [0042]-”coherent light”-Applicant’s 51; Figure 1; [0044]) is configured to generate at least one of an electron beam, an X-ray, and a laser as the analysis source – claim 6
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 25, wherein the analysis source generating unit (26,29,102; Figure 4; [0036], [0042]-”coherent light”-Applicant’s 51; Figure 1; [0044]) is configured to generate at least one of an electron beam, an X-ray, and a laser as the analysis source – claim 26
Miya also teaches a wafer processing apparatus (Figure 9) including a wafer surface measurement instrument (70; Figure 9) as a SEM electron beam irradiation ([0080]).
It would have been obvious to one of ordinary skill in the art at the time the invention was claimed for Matsui to add Miya’s wafer surface measurement instrument (70; Figure 9) to Matsui’s surface analysis hardware.
Motivation for Matsui to add Miya’s wafer surface measurement instrument (70; Figure 9) to Matsui’s surface analysis hardware is for “acquires information on projections and depressions on the surface of the object to be processed” as taught by Miya ([0080]).
Claims 9, 21, 23-25, 28, 29 are rejected under 35 U.S.C. 103 as being unpatentable over Matsui; Miyako et al. (US 20190237337 A1) in view of Hung; Shih-Wei et al. (US 20170194176 A1). Matsui is discussed above. Matsui further teaches:
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]) is applied of claim 1, wherein the thin film deposition system is configured to deposit a thin film by using any one of evaporation deposition, metal-organic chemical vapor deposition (MOCVD), molecular beam epitaxy (MBE), pulsed laser deposition (PLD), and sputtering deposition, as claimed by claim 9
An automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied, comprising: a chamber (21; Figure 4); a substrate holder (22; Figure 4) which is disposed in the chamber (21; Figure 4) and configured to support a substrate; a deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants) configured to deposit (S1; Figure 1) a thin film on the substrate; a temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]) configured to control a temperature of the substrate placed on the substrate holder (22; Figure 4); a raw material supply device (24+36; Figure 4; [0037]) configured to supply a raw material for deposition of the thin film to the substrate to the chamber (21; Figure 4) placed on the substrate holder (22; Figure 4); an analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) configured to analyze a property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film deposited on the substrate; a removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) configured to physically remove the thin film from the substrate; and a control device (36; Figure 4-[0036]) which is connected to the deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants), the temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]), the raw material supply device (24+36; Figure 4; [0037]), the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1), and the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching), wherein the control device (36; Figure 4-[0036]) comprises at least one processor (CPU; [0036]) and a memory (“storage unit”; [0036]) storing instructions (“program”; [0036]) that, when executed by the processor (CPU; [0036]), cause the control device (36; Figure 4-[0036]) to: acquire, while the substrate remains disposed within the chamber (21; Figure 4), measurement data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) from the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) corresponding to the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, execute a machine correlation model (“fitting curve”; 111; Figure 6; [0045]-[0047]) while the substrate remains in the chamber (21; Figure 4) to learn a nonlinear correlation (“fitting curve”; 111; Figure 6; [0045]-[0047]) between a plurality of process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) used for thin film deposition and properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, determine, based on the analyzed property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1), that the thin film fails to satisfy a predetermined quality criterion (r<ro; r>ro; S3; Figure 1), in response to the determination that the thin film fails to satisfy the predetermined quality criterion (r<ro; r>ro; S3; Figure 1), generate one or more control signals and transmit the one or more control signals to the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) to cause the removal device (25+22+36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) to physically remove the thin film from the substrate as a discarded thin film, and after the thin film is physically removed, generate one or more control signals corresponding to modified process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) derived from the learned nonlinear correlation (“fitting curve”; 111; Figure 6; [0045]-[0047]), and deposit, by transmitting the one or more control signals to at least one of the deposition device (24+36; Figure 4; S1; Figure 1-Not shown by Applicants), the temperature adjust device (not shown; “by means for adjusting...and the temperature for example”; [0056]), or the raw material supply device (24+36; Figure 4; [0037]), a new thin film (S3, r<ro; Figure 1) on the substrate by applying modified process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) derived from the learned nonlinear correlation (“fitting curve”; 111; Figure 6; [0045]-[0047]) and repeat (Figure 1) the acquire, determine, remove, and deposit steps in a closed-loop manner (Figure 1) while the substrate remains in the chamber (21; Figure 4) until the analyzed property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the newly deposited thin film satisfies the predetermined quality criterion (r<ro; r>ro; S3; Figure 1), wherein the process parameters (“time of etching”, “wafer bias voltage”, “wafer temperature”; [0056]; “signal intensity (I)”, time; Figure 6; [0045]) include at least temperature, pressure, gas composition, gas flow rate, bias voltage, and deposition time and the properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film include at least thickness (S3; Figure 1), uniformity, crystallinity, and electrical characteristics, wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is disposed outside (not claimed) the chamber (21; Figure 4)** and analyzes the properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film deposited on the substrate during the deposition process or at the time of completion of the deposition process, and wherein the thin film includes a two-dimensional material - claim 21
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 21, wherein the removal device (25+22+36,36; Figure 4-plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching) is configured to generate at least one of plasma (plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching), laser, ion beam, and etching gas, and to remove the thin film from the substrate disposed in the chamber (21; Figure 4) using at least one of the plasma (plasma etching; [0027]-Applicant’s 60; Figure 1; [0048]-plasma etching), laser, ion beam, and etching gas, as claimed by claim 23
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 21, wherein the raw material supply device (24+36; Figure 4; [0037]) is provided in plurality, as claimed by claim 24
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 21, wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) comprises: an analysis source generating unit (26,29,102; Figure 4; [0036], [0042]-”coherent light”-Applicant’s 51; Figure 1; [0044]) generating an analysis source (104; Figure 4; [0041]) for analyzing the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film; and an analysis data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) acquisition unit (110+26; Figure 4; [0036]-Applicant’s 52; Figure 1; [0044]) for acquiring data (“signal intensity (I)”, time; Figure 6; [0042],[0045]) corresponding to the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film, as claimed by claim 25
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]-[0047]) is applied of claim 21, wherein the control device (36; Figure 4-[0036]) is configured to terminate (“Finish”; Figure 1) the deposition of the thin film when the property (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film analyzed by the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is equal to or greater than the reference level (S3,S4; Figure 2; [0033]), as claimed by claim 28
Matsui does not teach:
wherein the analysis device (26,28; Figure 4; [0042],[0052]-Applicant’s 50-52; Figure 1) is disposed outside the chamber (21; Figure 4) – claim 21
The automated thin film deposition system to which machine learning (“fitting curve”; 111; Figure 6; [0045]) is applied of claim 21, wherein the thin film deposition system is configured to deposit a thin film by using any one of evaporation deposition, metal-organic chemical vapor deposition (MOCVD), molecular beam epitaxy (MBE), pulsed laser deposition (PLD), and sputtering deposition, as claimed by claim 29
Hung also teaches a wafer processing apparatus (Figure 3D) including wherein the analysis device (410,440; Figure 3C-Applicant’s 50-52; Figure 1) is within the chamber (1610; Figure 3C) – claim 21. Hung also “suitable deposition process” as an MOCVD film among other convention means ([0017]) – claim 29
It would have been obvious to one of ordinary skill in the art at the time the invention was made for Matsui to add MOCVD precursors as taught by Hung and for Matsui to add Hung’s analysis device (410,440; Figure 3C-Applicant’s 50-52; Figure 1) within Matsui’s chamber (21; Figure 4).
Motivation for Matsui to add MOCVD precursors as taught by Hung is for forming desirable films as taught by Hung ([0017]).
Motivation for Matsui to add Hung’s analysis device (410,440; Figure 3C-Applicant’s 50-52; Figure 1) within the chamber (21; Figure 4) is for “…the thickness of the epitaxy 150 can be accurately measured without destructing the structure of the epitaxy 150.” as taught by Hung ([0046]) and for “…simple, low-cost, and rapid methodology for monitoring the control wafer, which applies the polarized light to measure the thickness of the epitaxy 150 in the recess 140 of the control wafer 100.” as taught by Hung ([0047]).
Response to Arguments
Applicant's arguments filed September 15, 2025 have been fully considered but they are not persuasive.
Applicant states:
“
Applicant respectfully traverses the rejection. As amended, independent claims 1 and 21 are directed to a specific automated thin film deposition system that integrates machine learning into the real-time control of physical deposition hardware within a chamber to repeatedly remove and redeposit thin films until a predetermined quality criterion is satisfied. The claims are not directed to a mathematical concept in isolation, but rather to an industrial manufacturing control system that physically transforms material within a deposition chamber through closed-loop feedback.
“
In response, the Examiner disagrees and directs Applicant to the Examiner’s rationale applied using the MPEP 2106 guidance of the Alice/Mayo analysis as noted in the 09-February-2026 interview summary.
Applicant states:
“
The Examiner previously characterized the claims as being directed to "learning a nonlinear correlation" and treated the machine learning functionality as a mathematical concept performed on a generic processor. The present amendments overcome that characterization. The amended claims now expressly require:
“
And…
“
These new limitations materially change the character of the claims. First, the execution of the machine correlation model is expressly tied to in-chamber operation during the deposition process. This eliminates any interpretation that the model is merely performing abstract data analysis separate from the physical system. Second, the claims now require the generation and transmission of control signals that directly actuate specific physical hardware components. The output is not merely displayed or stored; it is used to control deposition, temperature, and raw material supply devices. Third, the claims require physical removal of deposited thin film material and redeposition of new thin film within the chamber. This constitutes a physical transformation of matter. Fourth, the closed-loop repetition limitation ensures that the claimed system continuously modifies chamber conditions and physically redeposits material until a target quality threshold is achieved.
Accordingly, the focus of the amended claims is not a mathematical correlation itself, but rather an automated thin film manufacturing control system that integrates machine learning into the operation of physical deposition hardware to repeatedly transform material within a chamber. Any alleged mathematical concept is inseparably integrated into this practical technological application. Under Step 2A, Prong One, the amended claims therefore are not directed to a judicial exception.
“
In response, the Examiner notes that the claims still require a “control device” to “learn a nonlinear correlation…”. The Examiner’s rationale applied using the MPEP 2106 guidance of the Alice/Mayo analysis, as noted in the 09-February-2026 interview summary, remains conclusively in support of the Examiner’s rejection under §101. Further, the Examiner’s proposed/suggested claim amendments discussed in the 05-March-2026 interview summary were only partially adopted.
Applicant states:
“
Even if the Examiner maintains that the claims recite a mathematical concept, the newly added limitations amount to significantly more. The amendments add meaningful limitations that were not previously emphasized:….
These limitations confine the alleged abstract idea to a specific industrial environment and require concrete physical actions that alter chamber conditions and deposited material. The claimed system does not merely calculate improved parameters; it physically implements them through hardware actuation and repeatedly modifies deposited matter. The combination of in-situ analysis, hardware control, physical transformation of thin film material, and closed-loop manufacturing correction goes well beyond generic computer implementation of a mathematical formula. The amended claims therefore include an inventive application of machine learning in a specific industrial process. Therefore, under Step 2B, the claims amount to significantly more than any alleged abstract idea.
“
In response, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed “learn a nonlinear correlation” is equivalent, under BRI, to a non-linear mathematical regression as evidenced by the below cited prior art (“fitting curve”; 111; Figure 6; [0045]) is a well-understood, routine, conventional computer function as recognized by the court decisions listed in MPEP § 2106.05(d).
Applicant states:
“
I. Matsui Does Not Disclose Removal of a Defective Thin Film Followed by Deposition of a New Thin Film Independent claims 1 and 21 require that, after evaluation of thin film properties, a thin film that fails to meet a predetermined reference level is removed from the substrate, and a new thin film is subsequently deposited under modified process parameters derived from a learned correlation.
“
In response, the Examiner disagrees and emphasizes the above, identical process, in Matsui’s Figure 1 and the corresponding logic discussed by Matsui in at least [0030]-[0038]. Further, the Examiner has taken pains for precisely and consistently noting where Applicant’s amended features are taught by Matsui in all of the above new grounds of rejections as necessitated by amendment.
Applicant states:
“
The Examiner relies primarily on paragraphs [0034] and [0040] of Matsui, which describe monitoring film thickness and performing cyclic etching during deposition. However, these disclosures do not correspond to the claimed removal-and- regrowth sequence. In Matsui, etching is employed during the deposition process itself as a feedback-based thickness adjustment technique. The etching is applied incrementally to the same growing film to fine-tune its thickness and maintain it within a target range. At no point does Matsui disclose or suggest that a deposited thin film is evaluated as defective, discarded, and then replaced with a newly deposited thin film.
By contrast, the claimed invention requires a post- evaluation removal step that is triggered by a determination that the thin film fails to satisfy a quality criterion. Removal in the present claims is therefore not a partial or corrective etch, but a resetting operation that restores the substrate to an initial or corresponding state so that a new thin film may be formed under different conditions. This functional and temporal distinction is critical. Matsui's etching is part of a continuous control loop for thickness convergence, whereas the present claims require (i) quality evaluation, (ii) removal of an unacceptable film, and (iii) regrowth of a new film. These are fundamentally different process architectures serving different technical purposes.
“
In response, Applicant is incorrect. Matsui’s removal-and-regrowth sequence is identically shown in Matsui’s Figure 1, and supporting specification, in at least because said removal is accomplished at “S2” and regrowth is accomplished at “S1” while the stated monitoring and repeating is accomplished at “S3” and “S4”.
Applicant sates:
“
In claims 1 and 21, a "new thin film" is deposited after removal of a previously deposited thin film that was determined to be defective. The term "new" thus denotes a film that is distinct in formation history from the removed film and is grown under modified process parameters selected in response to prior evaluation results. Matsui does not disclose any such regrowth scenario. The repeated deposition steps in Matsui merely extend or continue growth of the same thin film while intermittently adjusting thickness via etching. There is no disclosure, explicit or implicit, of discarding a film and restarting deposition anew.
“
In response, Matsui’s Figure 1 step at “S1” is a “new” film that is added to the existing film once steps “S3” and “S4” are evaluated by Matsui’s CPU and “curve fitting”.
Applicant states:
“
As described in Matsui, the fitting curve is generated by retrospectively approximating the relationship between signal intensity and time, and is then used as a fixed reference for monitoring deposition or etching progress. Matsui does not disclose updating, retraining, or adapting this curve as additional data are acquired.
In contrast, the present invention expressly learns correlations between process parameters and thin film properties through machine learning, and uses the learned correlations to derive new process parameters for subsequent deposition. The learning process is iterative and data-driven, not merely a curve-fitting or threshold comparison operation. Thus, Matsui's fitting curve is a static reference model, not a learned correlation in the sense of claims 1 and 21.
“
In response, Matsui’s “fitting curve” (111; Figure 6; [0045]), as analogous to Applicant’s “learn a nonlinear correlation”, is a well-understood, routine, conventional mathematical procedure, automated by a computer in both Matsui and Applicant’s controller, and may be supported by the court decision of Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 67, 101 USPQ2d 1961, 1964 (2010) (listed in MPEP § 2106.05(d). Further, as noted in the Examiner’s 09-February-2026 interview summary, the cited expectation simply performs the abstract idea on Applicant’s claimed controller/computer and thus does not add “significantly more to the judicial exception” per Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). As a result, it is believed that the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.
Applicant states:
“
Even assuming arguendo that Matsui's fitting curve could be characterized as a correlation, Matsui still does not disclose deriving and applying process parameters based on such a learned correlation. Matsui's control logic operates by comparing a measured signal curve to a reference curve, determining whether a deviation exceeds a threshold, and then increasing or decreasing deposition time or etching time according to predefined rules. This is a classic rule-based feedback control scheme.
By contrast, the claims require that modified process parameters are derived from a learned correlation between process parameters and thin film properties. The control decision is therefore model-driven rather than rule-driven. Matsui contains no disclosure of selecting or optimizing process parameters based on outputs of a machine-learned model.
“
In response, the Examiner argues, under a BRI, that Matsui's well-known fitting curve should be interpreted as machine “learning” as such a curve is established by a non-linear regression procedure whereby prior process runs collect data and actively change/learn the associated proper weights/coefficients to map/learn a non-linear curve used for future/present process conditions.
Applicant states:
“
In Matsui, the variables discussed in paragraph [0045] and Fig. 6 are limited to signal intensity and time. Signal intensity is not a process parameter, but rather a measurement output of an analysis device. Matsui does not disclose learning correlations among multiple deposition process parameters, nor modifying such parameters based on learned relationships.
“
In response, Matsui discusses many process parameters. Some process parameters are dependent like Matsui’s “signal intensity”, “film thickness”, while other of Matsui’s process parameters such as “time of etching”, “wafer bias voltage”, “wafer temperature”; are independent.
Applicant states:
“
Matsui addresses only indices related to thickness change or etching amount. There is no disclosure of evaluating film uniformity, crystallinity, electrical characteristics, or other material quality metrics. Nor does Matsui disclose making deposition decisions based on such properties.
“
In response, the pending independently claimed invention requires film “thickness” as “including at least”. As previously noted, Matsui indeed teaches Matsui’s measured properties (“change amount of film thickness..monitored in real-time”, “indicator of...film thickness, etching amount are calculated”; [0034],[0040]; S3; Figure 1) of the thin film include at least thickness (S3; Figure 1), uniformity, crystallinity, and electrical characteristics.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Process control in the wafer processing art is well established as noted at least by:
US 20070095799 A1
US 20150227139 A1
US 20160130696 A1
US 20160351405 A1
US 20220284342 A1
US 20030003607 A1
US 20070218683 A1
US 20200083080 A1
US 20040035529 A1
US 20050115824 A1
US 20200006100 A1
US 20240096713 A1
US 7402257 B1
US 6521080 B2
US 6835275 B1
US 6556303 B1
US 6113733 A
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 Examiner Rudy Zervigon whose telephone number is (571) 272- 1442. The examiner can normally be reached on a Monday through Thursday schedule from 8am through 6pm EST. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Any Inquiry of a general nature or relating to the status of this application or proceeding should be directed to the Chemical and Materials Engineering art unit receptionist at (571) 272-1700. If the examiner cannot be reached please contact the examiner's supervisor, Parviz Hassanzadeh, at (571) 272- 1435.
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:/Awww.uspto.gov/interviewpractice.
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.
/Rudy Zervigon/ Primary Examiner, Art Unit 1716
Prosecution Insights