DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6 and 14 are objected to because of the following informalities:
Claim 6 recites the limitation "the delta" in line 6. Although this appears to be referencing the “difference” recited in line 5, there is insufficient antecedent basis for this limitation in the claim.
Claim 14 is objected to for the same reason.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-13, and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pal et al. US 2009/0280579 (“Pal”).
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Regarding claims 1-5 and 8, Pal discloses:
1. A method, comprising:
performing a first substrate manufacturing process on a substrate according to a plurality of steps of a process recipe, wherein the process recipe comprises a plurality of setting parameters (e.g., [0026]: “nitride deposition recipe”, [0041]-[0042]: “deposited layer”);
obtaining a respective set of metrology data for each corresponding step of the plurality of steps, wherein the metrology data obtained by the metrology equipment comprises dimension data associated with completing each of the corresponding steps (e.g., [0041], [0042]: “an actual thickness of the deposited layer is measured using, for example, scatterometry metrology”);
generating a correction profile based on the metrology data (e.g., [0042]: “any errors or deviations in the measured thickness as compared to the intended thickness…”);
generating an updated process recipe by applying the correction profile to the process recipe to adjust one more setting parameters of the plurality of setting parameters (e.g., [0042]: “…will be accounted for by applying feedforward control”); and
causing a second substrate manufacturing process to be performed on the substrate according to the updated process recipe (e.g., [0042]: “Thus, for example, the feed-forward control can be used to account for and correct thickness errors with modification in the spacer etch process, for example, by modifying the spacer etch trim recipe”).
2. The method of claim 1, wherein the metrology data is indicative of one or more thickness of the film layers (e.g., [0041], [0042]: “an actual thickness of the deposited layer is measured using, for example, scatterometry metrology”).
3. The method of claim 1, wherein the correction profile comprises one or more corrective actions to be applied to the plurality of setting parameters of the process recipe (e.g., [0042]: “any errors or deviations in the measured thickness as compared to the intended thickness will be accounted for by applying feedforward control”).
4. The method of claim 1, wherein generating the correction profile comprises:
determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology equipment (e.g., [0042]: “any errors or deviations in the measured thickness as compared to the intended thickness…”); and
applying an algorithm to the delta (e.g., [0042]: “…will be accounted for by applying feedforward control”).
5. The method of claim 1, wherein generating the correction profile comprises:
determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology equipment (e.g., [0037], [0042]: “any errors or deviations in the measured thickness as compared to the intended thickness…”); and
performing a look up of the delta in a library of known faults, wherein the library comprises a list of delta values correlating to specific correction profiles (e.g., [0037]: “determining any element of the pre-clean recipe in order to achieve the proximity change (.DELTA.P) value needed to achieve the target proximity (P.sub.T) value using known effects of varying elements of the pre-clean recipe on the oxide undercut and etch and/or spacer etch”).
8. The method of claim 1, further comprising:
causing a chamber matching operation to be performed, on another process chamber, based on the correction profile (e.g., [0041]: “This APC technique can also account for deposition chamber differences by shifting target nitride layer thickness along with utilizing, dispatching, and scheduling elements. For instance, if a given etcher (referred to herein as “Etcher Number One”) is known through feedback to produce a low value for final gate proximity due to a higher lateral etch rate (i.e., as compared to other etchers using the same processes on the same devices), then a nitride layer having a greater thickness can be intentionally deposited during step 403”).
Claims 9-13 and 16-19 recite features analogous to claims 1-5 and 8, and thus are rejected under the same rationale.
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.
Claims 6-7, 14-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pal in view of Ortleb et al. US 2009/0000950 (“Ortleb”).
Pal does not explicitly disclose the features of claims 6-7, 14-15 and 20-21.
Ortleb (in combination with Pal) discloses:
6. The method of claim 1, wherein generating the correction profile comprises:
inputting the metrology data into a trained machine-learning model (e.g., [0043]: “the respective measurement data are fed to the APC, which have implemented models generally describing the sensitivity factor S(r)”, [0056]);
obtaining an output value of the trained machine-learning model, the output value being indicative of a difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate (e.g., [0021], [0044]: “the APC calculates process models based on measurement data”, [0047]: “If the APC detects that the actual copper profile is different from the profile target, the APC calculates a correction”, [0056]); and
performing a look up of the delta in a library of known faults, wherein the library comprises a list of delta values correlating to specific correction profiles (e.g., [0044]: “Thus, the APC may provide appropriate corrections for adjusting anode currents”, “the APC calculates a correction of the tool settings”, [0056]: “there already exists a database for a plurality of process situations. The database may be incorporated in the form of an expert system containing a set of stored data”).
7. The method of claim 1, wherein generating the correction profile comprises:
inputting the metrology data into a trained machine-learning model (e.g., [0043]: “the respective measurement data are fed to the APC, which have implemented models generally describing the sensitivity factor S(r)”, [0056]);
obtaining an output value of the trained machine-learning model, the output value being indicative of a correction profile (e.g., [0021], [0044]: “the APC calculates process models based on measurement data”, [0047]: “If the APC detects that the actual copper profile is different from the profile target, the APC calculates a correction”, [0056]).
14. The electronic device manufacturing system of claim 9, wherein generating the correction profile comprises:
inputting the metrology data into a trained machine-learning model (e.g., [0043]: “the respective measurement data are fed to the APC, which have implemented models generally describing the sensitivity factor S(r)”, [0056]);
obtaining an output value of the trained machine-learning model, the output value being indicative of a difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate (e.g., [0021], [0044]: “the APC calculates process models based on measurement data”, [0047]: “If the APC detects that the actual copper profile is different from the profile target, the APC calculates a correction”, [0056]); and
performing a look up of the delta in a library of known faults, wherein the library comprises a list of delta values correlating to specific correction profiles (e.g., [0044]: “Thus, the APC may provide appropriate corrections for adjusting anode currents”, “the APC calculates a correction of the tool settings”, [0056]: “there already exists a database for a plurality of process situations. The database may be incorporated in the form of an expert system containing a set of stored data”).
15. The electronic device manufacturing system of claim 9, wherein generating the correction profile comprises:
inputting the metrology data into a trained machine-learning model (e.g., [0043]: “the respective measurement data are fed to the APC, which have implemented models generally describing the sensitivity factor S(r)”, [0056]);
obtaining an output value of the trained machine-learning model, the output value being indicative of a correction profile (e.g., [0021], [0044]: “the APC calculates process models based on measurement data”, [0047]: “If the APC detects that the actual copper profile is different from the profile target, the APC calculates a correction”, [0056]).
20. The non-transitory computer-readable storage medium of claim 17, wherein generating the correction profile comprises:
inputting the metrology data into a trained machine-learning model (e.g., [0043]: “the respective measurement data are fed to the APC, which have implemented models generally describing the sensitivity factor S(r)”, [0056]);
obtaining an output value of the trained machine-learning model, the output value being indicative of a correction profile (e.g., [0021], [0044]: “the APC calculates process models based on measurement data”, [0047]: “If the APC detects that the actual copper profile is different from the profile target, the APC calculates a correction”, [0056]).
Pal and Ortleb are analogous art since both pertain to feedback and feedforward control systems as it relates to semiconductor deposition and etching machines.
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Pal with Ortleb since Ortleb as detailed above since teaches that such modifications improve yield and process efficiency, thus minimizing productions costs. This is done by ensuring that each individual process step reliably has the desired result (e.g., [0012]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Funk et al. US 7,292,906 discloses a processing method of processing a substrate is presented that includes: receiving pre-process data, wherein the pre-process data comprises a desired process result and actual measured data for the substrate; determining a required process result, wherein the required process result comprises the difference between the desired process result and the actual measured data; creating a new process recipe by modifying a nominal recipe obtained from a processing tool using at least one of a static recipe and a formula model, wherein the new process recipe provides a new process result that is approximately equal to the required process result; and sending the new process recipe to the processing tool and the substrate.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
09/29/26
