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 .
Status of Claims/Response to Amendment
Claims 1-10 and 15-24 are currently pending in response to the claim amendments and remarks filed on March 19, 2026. Claims 11-14 are canceled from a non-elected Invention II under a restriction requirement mailed out by Examiner BRET CHEN on 08/19/2025. The claim amendments and remarks have introduced new issues to claims 15-24, thus overcame the 35 U.S.C. 103 rejections as presented by Examiner TAMEEM D SIDDIQUEE in the Non-Final Office Action mailed on 12/19/2025. Upon further consideration of claims 15-24, new ground(s) of rejections is made as detailed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/03/2026 was filed after the mailing date of the Non-Final Office Action on 12/19/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Applicant’s Remarks
With respect to 35 U.S.C. §103 rejections:
With respect to claims 1-10, Applicant’s remarks filed on March 19, 2026 at p.7-8 have been fully considered and are persuasive. Rayner does not appear to teach "providing static process conditions data to an analysis model based on characteristics of the structure" as cited in paragraphs [0007;0009;0018;0029]. However, upon further consideration of claims 1-10, a new ground(s) of rejections is made as detailed below.
Claim Objection
Claim 19 is objected to because of the following informality: Claim 19 depends on claim 15 and subject matter “wherein an analysis model includes a neural network” has insufficient antecedent basis (e.g., claim 19 should depend on claim 16 for antecedent basis and the limitation should amend to “wherein the analysis model includes a neural network”).
Claim 15 is objected to because of the following informality: Claim 15 recites the limitation “static process conditions data” (at lines 10-11) fails to have sufficient antecedent basis. See prior limitation “static process conditions”.
Claim 21 is objected to because of the following informality: Claim 21 recites the limitation “the static process conditions data” (at lines 12 and 14-15) fails to have sufficient antecedent basis. See prior limitation “static process conditions”.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 21 recites the step of “receive sensor data” (at line 6) and repeat the same step again “receive the sensor data” (at line 9) that is unclear and indefinite. It is unclear if the same sensor signal is being receive at two different stages in the analysis modeling or if Applicant intends to recite “receive a first sensor data” and follow by “receive a second sensor data”.
Claims 22-24 are further rejected under 35 U.S.C. 112, second paragraph, for being dependent upon a rejected base claim 21.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 15-19 and 21-23 are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by Fujita et al. (JP- 2020015940-A).
With respect to claim 1, Fujita teaches a thin-film deposition system (film forming system 1, fig.1), comprising:
one or more memories configured to store software instructions; one or more processors configured to execute the software instructions to perform a process (computer readable instructions stored on computer readable media, and / or processors provided with computer readable instructions stored on computer readable media, page 10), the process including:
depositing, with an atomic layer deposition process, a first portion of a layer on a structure on a semiconductor wafer (film forming apparatus 2 forms a film according to the control conditions, page 5; film characteristics is a characteristics of a plurality of stacked films, page 6);
generating, with a sensor, sensor data indicating one or more dynamic process conditions1 present while depositing the first portion of the layer (the state acquisition unit 33 may acquire the temperature, the humidity, and the like of the environment where the film forming apparatus 2 is installed as the state data, pages 2-3; The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6; The temperature of the substrate 10 may be measured by a radiation thermometer disposed inside or outside the deposition chamber 20…The degree of vacuum of the film forming chamber 20 may be measured by an ion gauge arranged in the film forming chamber 20. The type and amount of gas present in the deposition chamber 20 may be measured by a quadrupole mass spectrometer disposed in the deposition chamber 20, page 7; step S1 and S5, obtains control condition data indicating control conditions of the film forming apparatus 2… acquires state data indicating the state of the film forming apparatus 2. The state data is an actual measurement value of a control target when the film forming apparatus 2 operates under the control condition indicated by the control condition data, fig.4);
providing static process conditions data2 to an analysis model based on characteristics of the structure (learning processing unit 34 performs a learning process of the model 35 using the input learning data. The learning data may include control condition data from the control condition acquisition unit 31, film characteristic/property data from the film characteristic/property acquisition unit 32, and state data from the state acquisition unit 33, page 3; the characteristics of the film (for example, the film thickness, the composition (mixed crystal ratio, the laminated structure), and the like), page 5; the film property data includes the film thickness, composition, flatness, electrical properties (eg, mobility, carrier concentration, resistance, etc.) and optical properties (eg, band gap, transmittance, transmission spectrum, photo Data such as luminescence intensity and photoluminescence wavelength peak), crystallinity, surface information, and / or dislocation density. The film characteristics may be the characteristics of a single-layer film or the characteristics of a plurality of stacked films. Each characteristic may be any of a maximum value, a minimum value, an average value, and a distribution of values at a plurality of positions on the film, or may be a value at one position (for example, the center). The composition may be a composition ratio of constituent elements or a lattice constant. The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6; step S3, acquires film characteristic data of a film formed by the film forming apparatus 2 operated under the control condition indicated by the control condition data, fig.4; step S7, performs a learning process on the model 35 using the acquired control condition data, film characteristic data, and learning data including state data, fig.4);
generating, with the analysis model, first predicted layer data based on the static process conditions data and the first dynamic process conditions data (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data, page 4; the recommended control condition acquisition unit 38 acquires recommended control condition data output from the model 35…the control condition data, the state data, and the film characteristic data at the time of film formation are input to the model 35 as learning data, and the learning process of FIG. 4 is performed…the learning process of the model 35 can be advanced while manufacturing the compound semiconductor, page 9);
comparing the first predicted layer data to target layer data; if the first predicted layer data matches the target layer data, depositing a second portion of the layer by continuing the atomic layer deposition process with the dynamic process conditions; and if the first predicted layer data does not match the target layer data, generating adjusting dynamic process conditions adjustment data and depositing the second portion of the layer by adjusting the atomic layer deposition process based on the dynamic process conditions adjustment data (the correlation between the control condition relating to the raw material flux amount and the actual flux amount, and furthermore, the correlation between the control condition and the film characteristics, was determined in the previous time. If it is different from the time of the film formation, the current correlation is accurately predicted, and the film formation is performed according to the recommended control condition data. Further, due to the deposition of the raw material inside the film forming chamber 20, the correlation between the control condition relating to the substrate temperature and the actual substrate temperature, and furthermore, the correlation between the control condition and the film characteristics is changed in the previous film formation. If it is different from the time, the current correlation is accurately predicted, and the film is formed by the recommended control condition data. At the time of each film formation, the learning process of the model 35 may be performed using the control condition data, the state data, and the film characteristic data as learning data, page 9).
With respect to claim 15, Fujita teaches a thin-film deposition system (film forming system 1, fig.1), comprising:
a thin-film deposition chamber (film forming device 2 including film forming chamber 20, sub-chamber 23, and load chamber 24, figs.1-2);
a support configured to support a substrate within the thin-film deposition chamber (film forming chamber 20 includes substrate manipulator 200 that holds one or a plurality of substrates 10, fig.3 and page 4);
a fluid source configured to provide a fluid into the thin-film deposition chamber during a thin-film deposition process (film forming chamber 20 may have a gas supply port for supplying a gas (eg, oxygen, ozone, nitrogen, ammonia) into the film forming chamber 20, fig.3 and page 4);
a sensor configured to generate sensor data indicating one or more dynamic process conditions3 present while depositing a first portion of a layer on a structure on the substrate (the state acquisition unit 33 may acquire the temperature, the humidity, and the like of the environment where the film forming apparatus 2 is installed as the state data, pages 2-3; The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6; The temperature of the substrate 10 may be measured by a radiation thermometer disposed inside or outside the deposition chamber 20…The degree of vacuum of the film forming chamber 20 may be measured by an ion gauge arranged in the film forming chamber 20. The type and amount of gas present in the deposition chamber 20 may be measured by a quadrupole mass spectrometer disposed in the deposition chamber 20, page 7); and
a control system configured to receive the sensor data, receive static process conditions4 based on characteristics of the structure (learning processing unit 34 performs a learning process of the model 35 using the input learning data. The learning data may include control condition data from the control condition acquisition unit 31, film characteristic/property data from the film characteristic/property acquisition unit 32, and state data from the state acquisition unit 33, page 3; the characteristics of the film (for example, the film thickness, the composition (mixed crystal ratio, the laminated structure), and the like), page 5; the film property data includes the film thickness, composition, flatness, electrical properties (eg, mobility, carrier concentration, resistance, etc.) and optical properties (eg, band gap, transmittance, transmission spectrum, photo Data such as luminescence intensity and photoluminescence wavelength peak), crystallinity, surface information, and / or dislocation density. The film characteristics may be the characteristics of a single-layer film or the characteristics of a plurality of stacked films. Each characteristic may be any of a maximum value, a minimum value, an average value, and a distribution of values at a plurality of positions on the film, or may be a value at one position (for example, the center). The composition may be a composition ratio of constituent elements or a lattice constant. The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6), identify process conditions data for the thin-film deposition process based on a machine learning process and the sensor data and static process conditions data (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data, page 4; the recommended control condition acquisition unit 38 acquires recommended control condition data output from the model 35…the control condition data, the state data, and the film characteristic data at the time of film formation are input to the model 35 as learning data, and the learning process of FIG. 4 is performed…the learning process of the model 35 can be advanced while manufacturing the compound semiconductor, page 9) and to control the first fluid source during the thin-film deposition process in accordance with the process conditions data (the degree of vacuum of the film forming chamber 20, the type of gas present in the film forming chamber 20, the amount of gas, the temperature of the cryopanel 202, and the flow rate of liquid nitrogen in the cryopanel 202 are determined in the film forming chamber 20. The degree of vacuum and the quality of the vacuum may be controlled directly or indirectly. When the degree of vacuum and the quality of the vacuum change, the degree of mixing of impurities into the film to be formed changes, and the state of the growth surface changes. As a result, the characteristics of the film to be formed change, page 6; adjust the amount of the source gas discharged, and a cracking section 214A for decomposing molecules of the source gas discharged, page 10).
With respect to claim 21, Fujita teaches a control system for thin-film deposition (film forming system 1, fig.1), comprising:
a training module (a learning processing device 3, fig.1) configured to train an analysis model with a machine learning process to predict characteristics of thin films (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data. Therefore, more accurate control conditions for forming a film having target film characteristics can be obtained, page 4; Film formation is performed by the film forming apparatus 2 based on recommended control condition data obtained by inputting target film characteristic data to the model 35. As a result, the state of the film forming apparatus 2 (eg, the amount of raw material charged to the cell 21, the degree of contamination on the inner wall surface of the film forming chamber 20, the position of the thermocouple for temperature measurement, and the like) differs for each maintenance. If the correlation between the control condition and the film characteristic is different due to the reason, the correlation in the campaign after the current maintenance is accurately predicted, and the film is formed by the recommended control condition data, page 9); and
the analysis model, configured to:
receive target thin-film data (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data. Therefore, more accurate control conditions for forming a film having target film characteristics can be obtained, page 4; Film formation is performed by the film forming apparatus 2 based on recommended control condition data obtained by inputting target film characteristic data to the model 35, page 9);
receive sensor data indicating one or more dynamic process conditions5 present while depositing a first portion of a layer on a structure on a semiconductor wafer during a thin-film deposition process (the state acquisition unit 33 may acquire the temperature, the humidity, and the like of the environment where the film forming apparatus 2 is installed as the state data, pages 2-3; The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6; The temperature of the substrate 10 may be measured by a radiation thermometer disposed inside or outside the deposition chamber 20…The degree of vacuum of the film forming chamber 20 may be measured by an ion gauge arranged in the film forming chamber 20. The type and amount of gas present in the deposition chamber 20 may be measured by a quadrupole mass spectrometer disposed in the deposition chamber 20, page 7);
receive the sensor data (the state acquisition unit 33 may acquire the temperature, the humidity, and the like of the environment where the film forming apparatus 2 is installed as the state data, pages 2-3; a plurality of films are formed…the substrate may be a compound semiconductor such as gallium arsenide, and the film to be formed may be any of an n-type semiconductor layer, an undoped semiconductor layer, and a p-type active layer, page 4; the system 1 may perform the processing of steps S1 to S7 for each film forming operation in the film forming apparatus 2…The film characteristics may be the characteristics of a single-layer film or the characteristics of a plurality of stacked films. Each characteristic may be any of a maximum value, a minimum value, an average value, and a distribution of values at a plurality of positions on the film, page 6);
receive static process conditions6 based on characteristics of the structure (learning processing unit 34 performs a learning process of the model 35 using the input learning data. The learning data may include control condition data from the control condition acquisition unit 31, film characteristic/property data from the film characteristic/property acquisition unit 32, and state data from the state acquisition unit 33, page 3; the characteristics of the film (for example, the film thickness, the composition (mixed crystal ratio, the laminated structure), and the like), page 5; the film property data includes the film thickness, composition, flatness, electrical properties (eg, mobility, carrier concentration, resistance, etc.) and optical properties (eg, band gap, transmittance, transmission spectrum, photo Data such as luminescence intensity and photoluminescence wavelength peak), crystallinity, surface information, and / or dislocation density. The film characteristics may be the characteristics of a single-layer film or the characteristics of a plurality of stacked films. Each characteristic may be any of a maximum value, a minimum value, an average value, and a distribution of values at a plurality of positions on the film, or may be a value at one position (for example, the center). The composition may be a composition ratio of constituent elements or a lattice constant. The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6), and identify process conditions data that results in predicted thin-film data that complies with the target thin-film data, based on the sensor data and the static process conditions data (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data, page 4; the recommended control condition acquisition unit 38 acquires recommended control condition data output from the model 35…the control condition data, the state data, and the film characteristic data at the time of film formation are input to the model 35 as learning data, and the learning process of FIG. 4 is performed…the learning process of the model 35 can be advanced while manufacturing the compound semiconductor, page 9), wherein the control system is configured to adjust the thin-film deposition process on the semiconductor wafer with deposition process conditions in accordance with the static process conditions data and the sensor data (the degree of vacuum of the film forming chamber 20, the type of gas present in the film forming chamber 20, the amount of gas, the temperature of the cryopanel 202, and the flow rate of liquid nitrogen in the cryopanel 202 are determined in the film forming chamber 20. The degree of vacuum and the quality of the vacuum may be controlled directly or indirectly. When the degree of vacuum and the quality of the vacuum change, the degree of mixing of impurities into the film to be formed changes, and the state of the growth surface changes. As a result, the characteristics of the film to be formed change, page 6; adjust the amount of the source gas discharged, and a cracking section 214A for decomposing molecules of the source gas discharged, page 10).
With respect to claims 6 and 19, Fujita teaches further wherein an analysis model includes a neural network (The learning process may be performed using the state data. The model 35 is, for example, a neural network such as a recurrent type or a time delay type, page 8).
With respect to claim 7, Fujita teaches further wherein the static process conditions data includes one or more of: a deposition material (Al, Ga, In, As, Sb, Si, Te, Sn, Zn, page 4; The infrared sensor 5 includes, for example, an n + layer 51 of indium antimonide (InSb), an n + layer 52 of aluminum indium antimonide (AlInSb), and an n + layer (barrier layer) of aluminum indium antimonide (AlInSb) on a gallium arsenide substrate 10. 53, an active layer 54 of aluminum indium antimonide (AlInSb), a p + layer (barrier layer) 55 of aluminum indium antimonide (AlInSb), and a p + layer 56 of indium antimonide (InSb). The film formed by the film forming method of FIG. 6 may be any of the layers 51 to 56. The infrared sensor 5 may further include a layer of silicon dioxide (SiO .sub.2 ), a layer of silicon nitride (Si .sub.3 N .sub.4 ), and / or an electrode layer, fig.4 and page 9); features of a deposition surface (the degree of crystallization, surface shape, and film characteristics (eg, flatness, crystallinity, and the like) in the film to be formed are obtained… The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6); and an age of deposition equipment (The maintenance may be performed regularly (for example, every year as an example), may be performed according to the characteristics of the formed film, or may be performed according to the life of the used component, failure, or the like, page 6).
With respect to claim 8, Fujita teaches further wherein the first dynamic process conditions data includes one or more of: a flow rate of the deposition material; a duration of flow of the deposition material; a pressure in a deposition chamber; a temperature in the deposition chamber; and a humidity in the deposition chamber (the state acquisition unit 33 may acquire the temperature, the humidity, and the like of the environment where the film forming apparatus 2 is installed as the state data, pages 2-3; The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6; The temperature of the substrate 10 may be measured by a radiation thermometer disposed inside or outside the deposition chamber 20…The degree of vacuum of the film forming chamber 20 may be measured by an ion gauge arranged in the film forming chamber 20. The type and amount of gas present in the deposition chamber 20 may be measured by a quadrupole mass spectrometer disposed in the deposition chamber 20, page 7; step S1 and S5, obtains control condition data indicating control conditions of the film forming apparatus 2… acquires state data indicating the state of the film forming apparatus 2. The state data is an actual measurement value of a control target when the film forming apparatus 2 operates under the control condition indicated by the control condition data, fig.4).
With respect to claim 9, Fujita teaches further wherein the target thin-film data identifies a target thin-film thickness (the composition of the film to be formed and the characteristics of the film (for example, the film thickness, the composition (mixed crystal ratio, the laminated structure), and the like) change, page 5).
With respect to claim 10, Fujita teaches further wherein the target thin-film data identifies a target thin-film thickness range (The film property data, the thickness of the film formed, the composition, flatness, electrical properties, optical properties, crystallinity, surface information, and data on at least one of the dislocation density, page 14).
With respect to claim 16, Fujita teaches further wherein the control system includes an analysis model, wherein the analysis model is configured to identify the process conditions data (The model 35 outputs recommended control condition data in response to input of target film characteristic data and state data of the film forming apparatus 2, and a learning process is executed using learning data including the acquired state data, page 4; the recommended control condition acquisition unit 38 acquires recommended control condition data output from the model 35…the control condition data, the state data, and the film characteristic data at the time of film formation are input to the model 35 as learning data, and the learning process of FIG. 4 is performed…the learning process of the model 35 can be advanced while manufacturing the compound semiconductor, page 9).
With respect to claim 17, Fujita teaches further wherein the analysis model is configured to receive target thin-film data indicating target parameters of the thin film and to identify the process conditions data by generating predicted thin-film data that complies with the target thin-film data (the target film characteristic acquisition unit 36 acquires target film characteristic data indicating target film characteristics for the film to be formed, and in step S23, the target film characteristic supply unit 37 converts the target film characteristic data into a model 35. Supply. Thereby, recommended control condition data relating to the control conditions of the film forming apparatus 2 is output from the model 35, page 8; If the correlation between the control condition and the film characteristic is different due to the reason, the correlation in the campaign after the current maintenance is accurately predicted, and the film is formed by the recommended control condition data, page 9).
With respect to claim 18, Fujita teaches further wherein the analysis model is configured to receive static process conditions data and to identify the process conditions data based on the static process conditions data and the target thin-film data (learning processing unit 34 performs a learning process of the model 35 using the input learning data. The learning data may include control condition data from the control condition acquisition unit 31, film characteristic/property data from the film characteristic/property acquisition unit 32, and state data from the state acquisition unit 33, page 3; the characteristics of the film (for example, the film thickness, the composition (mixed crystal ratio, the laminated structure), and the like), page 5; the film property data includes the film thickness, composition, flatness, electrical properties (eg, mobility, carrier concentration, resistance, etc.) and optical properties (eg, band gap, transmittance, transmission spectrum, photo Data such as luminescence intensity and photoluminescence wavelength peak), crystallinity, surface information, and / or dislocation density. The film characteristics may be the characteristics of a single-layer film or the characteristics of a plurality of stacked films. Each characteristic may be any of a maximum value, a minimum value, an average value, and a distribution of values at a plurality of positions on the film, or may be a value at one position (for example, the center). The composition may be a composition ratio of constituent elements or a lattice constant. The surface information may include information obtained from an optical microscope photograph of the formed film, or may include information obtained from measurement of surface irregularities such as a step meter, an AFM, or a foreign substance inspection device, page 6).
With respect to claim 22, Fujita teaches further comprising: a storage device that stores training set data (the model 35 may be stored in a server external to the learning processing device 3, page 3; stage a process in which an operation is performed or (2) perform an operation. It may represent a section of the device to have. Certain stages and sections are implemented by dedicated circuitry, programmable circuitry provided with computer readable instructions stored on computer readable media, and / or processors provided with computer readable instructions stored on computer readable media, page 10), wherein the training module is configured to train the analysis model using the training set data (learning processing unit 34 performs a learning process of the model 35 using the input learning data. The learning data may include control condition data from the control condition acquisition unit 31, film characteristic/property data from the film characteristic/property acquisition unit 32, and state data from the state acquisition unit 33, page 3).
With respect to claim 23, Fujita teaches further wherein the training set data includes historical thin-film data identifying characteristics of previously deposited thin films, wherein the training set data includes historical process conditions data identifying historical process conditions associated with the previously deposited thin films (by inputting the target film characteristics, it is possible to generate the model 35 in which the recommended control conditions are output. Further, since the state data includes the operation history data, more accurate control conditions for forming a film having target film characteristics can be obtained. Further, since the operation history data includes data relating to at least one of the number of times and contents of maintenance, the model 35 can learn the relationship between the number of times and contents of maintenance and the film characteristics, page 8; the state data may include operation history data indicating an operation history of the film forming apparatus 2. The operation history data includes data relating to at least one of the number and contents of maintenance performed on the film forming apparatus 2 (for example, replacement or cleaning of a certain part), the number of times at least one part of the film forming apparatus 2 has been used. The data may include at least one of the following data, the weight of the raw material charged into the crucible of the cell 21 at the time of maintenance, the data on the number of times of film formation by the film forming apparatus 2, and the data on the film formed in the past. The data relating to at least one of the number of times and contents of maintenance may be data indicating a history of maintenance. The data on the number of film formations may be the number of film formations after maintenance or the total number of film formations regardless of maintenance. The data on the film formed in the past may be history data indicating the type, characteristics, etc. of the film formed in the past. These operation history data can relate to the state of adhesion of the raw material inside the film forming chamber 20, and further to the heat capacity and heat conduction state of each member, page 7).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3-4 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fujita et al. (JP- 2020015940-A) in view of Rayner (US-20120269968-A1).
With respect to claims 3-4 and 20, Fujita teaches the thin-film deposition apparatus/process (Fujita: figs. 1-3) but does not teach that the thin-film deposition process is an atomic layer deposition process and performing the thin-film deposition process includes performing a first cycle of the atomic layer deposition process. However, it is known by Rayner to teach of an atomic layer deposition apparatus and process (Rayner: figs.1-9, title and abstract) and performing the thin-film deposition process includes performing a first cycle of the atomic layer deposition process (Rayner: A complete cycle is required to obtain the desired material. Each cycle deposits a very specific amount of material onto the substrate surface and is repeated until the desired amount of material has been deposited, enabling very accurate control of film thickness. Typical growth per cycle (GPC) is approximately 1 .ANG./cycle, [0006]; an atomic layer deposition process implemented in connection with or facilitated by the apparatus 1 of the present invention, multiple precursor pulse steps (separated by purge steps) are used in implementing the atomic layer deposition process…a first precursor pulse step, precursor gas is introduced through certain of the gas injection ports 20, resulting in a mixture of precursor gas and inactive gas, as represented by arrow B [0054]; after the first pulsing step, the chamber 10 is purged through the preferably continuous injection of inactive gas through all available gas injection ports 20. In the second precursor pulse step, precursor gas is injected through the primary precursor gas injection port 42). Because Rayner’s teaching is also directed to the thin-film deposition process (Rayner: figs.1-9; Fujita: figs.1-3), it would have been obvious to POSITA before the effective filing date to incorporate the atomic layer deposition process and performing the thin-film deposition process includes performing a first cycle of the atomic layer deposition process as taught by Rayner with the thin-film deposition process as taught by Fujita for the purpose that enables surface-controlled film growth on the atomic scale with excellent uniformity (Rayner: [0005]).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Fujita et al. (JP- 2020015940-A) in view of NOH (US-2020/0140993-A1).
With respect to claim 24, Fujita does not teach wherein the control system is configured to perform a data mining process on a thin-film deposition database to obtain the training set data. However, it is known by NOH to teach of a system/method for controlling thin-film deposition process (NOH: fig.1, title, abstract) including the method to perform a data mining process on a thin-film deposition database to obtain the training set data (NOH: the thin-film measurement results which are originally quantitative data (422), and the thin-film measurement results that are quantified through the data conversion (440), may be sorted or trimmed by the one or more processors 210 (450). The data that is sorted or trimmed may be used as pre-processed measurement data 460. The pre-processed measurement data 460 may be quantitative data, [0084]; a general sorting algorithm or trimming algorithm may be used for the data-sorting and/or trimming process (450 and 470), [[0086]). Because NOH’s teaching is also directed to the thin-film deposition process (NOH: fig1; Fujita: figs.1-3), it would have been obvious to POSITA before the effective filing date to incorporate the method to perform a data mining process on a thin-film deposition database to obtain the training set data as taught by NOH with the thin-film deposition process as taught by Fujita for a well-known purpose of sifting through massive, raw datasets to discover hidden patterns, correlations, and useful insights. This process extracts and transforms unstructured information into structured, meaningful features that machine learning algorithms use to train accurate predictive models and make automated decisions.
Allowable Subject Matter
Claims 2 and 5 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record, taken alone or in combination, fails to disclose or render obvious, which makes the following claims allowable over the prior art:
With respect to claim 2, further wherein the process includes, if the first predicted thin-film data does not match the target thin-film data: generating second predicted thin-film data based on the adjusted first dynamic process conditions data; comparing second predicted thin-film data to the target thin-film data; and if the second predicted thin-film data matches the target thin-film data, performing the thin-film deposition process with process conditions based on the static process conditions data and the adjusted first dynamic process conditions data.
With respect to claim 5, wherein the process includes, after the first cycle: identifying, with the analysis model, second dynamic process conditions data; and performing a second cycle of the atomic layer deposition process based on the static process conditions data and the second dynamic process conditions data.
Conclusion
The additional prior arts made of record and have not been relied upon are considered pertinent to applicant's disclosure as follows: KR_102711644_B1.
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/HIEN D KHUU/Primary Examiner, Art Unit 2116 August 11, 2026
1 As defined by Applicant, “dynamic process conditions include temperature, pressure, humidity, and flow rate”, See Specification at page 35.
2 As defined by Applicant, “The static process conditions can include an effective plan area crystal orientation, an effective plan area roughness index, an effective sidewall area of the features on the surface of the semiconductor wafer, an exposed effective sidewall tilt angle, an exposed surface film function group, an exposed sidewall film function group, a rotation or tilt of the semiconductor wafer, process gas parameters (materials, phase of materials, and temperature of materials), a remaining amount of material fluid in the fluid sources 108 and 110, a remaining amount of fluid in the purge sources 112 and 114, a humidity within a deposition chamber, an age of an ampoule utilized in the deposition process, light absorption or reflection within the deposition chamber, the length of pipes or conduits that will provide fluids to the deposition chamber, or other conditions”, See Specification at page 34.
3 As defined by Applicant, “dynamic process conditions include temperature, pressure, humidity, and flow rate”, See Specification at page 35.
4 As defined by Applicant, “The static process conditions can include an effective plan area crystal orientation, an effective plan area roughness index, an effective sidewall area of the features on the surface of the semiconductor wafer, an exposed effective sidewall tilt angle, an exposed surface film function group, an exposed sidewall film function group, a rotation or tilt of the semiconductor wafer, process gas parameters (materials, phase of materials, and temperature of materials), a remaining amount of material fluid in the fluid sources 108 and 110, a remaining amount of fluid in the purge sources 112 and 114, a humidity within a deposition chamber, an age of an ampoule utilized in the deposition process, light absorption or reflection within the deposition chamber, the length of pipes or conduits that will provide fluids to the deposition chamber, or other conditions”, See Specification at page 34.
5 As defined by Applicant, “dynamic process conditions include temperature, pressure, humidity, and flow rate”, See Specification at page 35.
6 As defined by Applicant, “The static process conditions can include an effective plan area crystal orientation, an effective plan area roughness index, an effective sidewall area of the features on the surface of the semiconductor wafer, an exposed effective sidewall tilt angle, an exposed surface film function group, an exposed sidewall film function group, a rotation or tilt of the semiconductor wafer, process gas parameters (materials, phase of materials, and temperature of materials), a remaining amount of material fluid in the fluid sources 108 and 110, a remaining amount of fluid in the purge sources 112 and 114, a humidity within a deposition chamber, an age of an ampoule utilized in the deposition process, light absorption or reflection within the deposition chamber, the length of pipes or conduits that will provide fluids to the deposition chamber, or other conditions”, See Specification at page 34.
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