DETAILED ACTION
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 .
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.
Claims 1-3, 8-9, 11-12, 14-19, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Phan et al., hereinafter Phan, US Publication No. 2021/0103221.
Regarding Claim 1, Phan teaches a computer-implemented method for determining a predicted future performance state of a substrate tool, comprising:
providing a current performance state for the substrate tool to a trained machine learning model (Phan paragraphs [0026] and [0028], wherein post-process measurements are a current performance state of a substrate tool and are provided as historical measurements to a neural network);
providing operating data for the substrate tool to the trained machine learning model (Phan paragraphs [0026] and [0028], wherein in-process measurements are operating data that are provided to the neural network); and
outputting the predicted future performance state of the substrate tool, the predicted future performance state being determined by the trained machine learning model based on the current performance state and the operating data (Phan paragraph [0028], wherein a predicted performance of the process as performed by the process tool is determined through the neural network and is output).
Regarding Claim 2, Phan further teaches outputting a recommended recalibration of the substrate tool determined by the trained machine learning model based on the current performance state (Phan paragraph [0038], wherein the control system in conjunction with a user determines a recommended adjustment to a process parameter which is a recalibration).
Regarding Claim 3, Phan further teaches wherein the operating data includes data generated by a sensor associated with the substrate tool or associated with an ambient environment of the substrate tool (Phan paragraph [0026], wherein operating data includes sensor measurements for voltages, currents, temperatures, and pressures).
Regarding Claim 8, Phan further teaches wherein the trained machine learning model includes a recurrent neural network (Phan paragraph [0028], wherein the neural network utilizes previous outputs in conjunction with further inputs to generate a next output and is a recurrent neural network).
Regarding Claim 9, Phan further teaches outputting the predicted future performance state as an outputted predicted future performance state (Phan paragraph [0028], wherein a naïve predicted output is generated and output);
providing as input to the trained machine learning model the outputted predicted future performance state as a subsequent performance state of the substrate tool (Phan paragraph [0028], wherein the naïve predicted output is provided as an input to the second layer of the neural network); and
receiving a subsequent predicted future performance state of the substrate tool following a subsequent future use of the substrate tool, the subsequent predicted future performance state being determined by the trained machine learning model based on the subsequent performance state of the substrate tool (Phan paragraph [0028], wherein an informed predicted output is determined based on the measurements and the naive predicted output).
Regarding Claim 11, Phan further teaches wherein the current performance state is determined by a substrate inspection tool (Phan paragraph [0026], wherein resistivity measurements use substrate inspection tools).
Regarding Claim 12, Phan further teaches wherein a recommended recalibration includes to recalibrate an identified parameter of the substrate tool before a future use of the substrate tool (Phan paragraphs [0038] and [0042], wherein adjustment of the process parameters are performed before running the process again).
Regarding Claim 14, Phan further teaches receiving a recommendation to adjust a condition of an ambient environment around the substrate tool, the recommendation being generated by the trained machine learning model based on the current performance state and the operating data (Phan paragraphs [0026] and [0038], wherein adjustments are recommended to process parameters, process parameters including temperature and pressure which are conditions of the ambient environment).
Regarding Claim 15, Phan further teaches wherein the predicted future performance state includes an indication that a performance of the substrate tool will be outside of a predefined performance specification for a future use of the substrate tool (Phan paragraph [0038], wherein the error function is an indication that the substrate tool is not meeting targets, i.e. predefined performance specifications).
Regarding Claim 16, Phan further teaches wherein the predicted future performance state includes an indication that a performance of the substrate tool will deviate from a performance of another tool by more than a predefined maximum deviation on a future use of the tool (Phan paragraphs [0029] and [0038], wherein determination of whether or not to adjust a process parameter is determined based on the error or deviation of the predicted output from a desired value, the error having a predefined acceptable differential or maximum deviation).
Regarding Claim 17, Phan teaches a method for predicting a future performance state of a substrate tool, comprising:
means for receiving (Phan paragraph [0043], see apparatus including a processor and memory), by a trained machine learning model, a current performance state for the substrate tool (Phan paragraphs [0026] and [0028], wherein post-process measurements are a current performance state of a substrate tool and are provided as historical measurements to a neural network);
means for receiving (Phan paragraph [0043], see apparatus including a processor and memory), by the trained machine learning model, operating data for the substrate tool (Phan paragraphs [0026] and [0028], wherein in-process measurements are operating data that are provided to the neural network); and
means for generating (Phan paragraph [0043], see apparatus including a processor and memory), by the trained machine learning model, a predicted future performance state of the substrate tool, the predicted future performance state being determined based on the current performance state and the operating data (Phan paragraph [0028], wherein a predicted performance of the process as performed by the process tool is determined through the neural network and is output).
Regarding Claim 18, Phan further teaches means for generating (Phan paragraph [0043], see apparatus including a processor and memory), by the trained machine learning model, a recommended recalibration of the substrate tool based on the current performance state and the operating data (Phan paragraph [0038], wherein the control system in conjunction with a user determines a recommended adjustment to a process parameter which is a recalibration).
Regarding Claim 19, Phan further teaches wherein the operating data includes data generated by one or more of:
a sensor associated with the substrate tool or associated with an ambient environment of the substrate tool (Phan paragraph [0026], wherein operating data includes sensor measurements for voltages, currents, temperatures, and pressures);
an auto-test performed by the substrate tool;
run-time data for the substrate tool, the run-time data including data associated with an alignment or an autofocus of the substrate tool;
a calibration performed by the substrate tool; and
an event, the event including a replacement of a component of the substrate tool.
Regarding Claim 21, Phan teaches a system for determining a predicted future performance state of a substrate tool, comprising:
one or more processors (Phan paragraph [0043], see apparatus including a processor); and
non-transitory computer-readable storage having stored thereon instructions (Phan paragraph [0043], see computer readable medium) which, when executed by the one or more processors, cause the system to:
provide a current performance state for the substrate tool to a trained machine learning model (Phan paragraphs [0026] and [0028], wherein post-process measurements are a current performance state of a substrate tool and are provided as historical measurements to a neural network);
provide operating data for the substrate tool to the trained machine learning model (Phan paragraphs [0026] and [0028], wherein in-process measurements are operating data that are provided to the neural network); and
output the predicted future performance state of the substrate tool, the predicted future performance state being determined by the trained machine learning model based on the current performance state and the operating data (Phan paragraph [0028], wherein a predicted performance of the process as performed by the process tool is determined through the neural network and is output).
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 4-7, 10, 20, 22 are rejected under 35 U.S.C. 103 as being unpatentable over Phan as applied to claims 1, 17, and 21 above, and further in view of Kaushal et al., hereinafter Kaushal, US Publication No. 2014/0229409.
Regarding Claim 4, Phan does not explicitly teach wherein the operating data includes data generated by an auto-test performed by the substrate tool.
Kaushal teaches wherein the operating data includes data generated by an auto-test performed by the substrate tool (Kaushal paragraphs [0122] and [0131], wherein self tests are scheduled and performed which generate parameters to produce input data to the machine learning).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 5, Phan does not explicitly teach wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool.
Kaushal teaches wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool (Kaushal paragraph [0084], wherein type and quantity of error is supplied as an input to the machine learning).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 6, Phan does not explicitly teach wherein the current performance state includes a type of the substrate tool.
Kaushal teaches wherein the current performance state includes a type of the substrate tool (Phan paragraph [0057], wherein input data includes the specific tool system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 7, Phan does not explicitly teach wherein the current performance state includes a type of a fabrication step or a type of another substrate function performed by the substrate tool.
Kaushal teaches wherein the current performance state includes a type of a fabrication step or a type of another substrate function performed by the substrate tool (Kaushal paragraph [0057], wherein input data includes recipe information which is the type of fabrication step).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 10, Phan does not explicitly teach wherein the current performance state includes a thickness of a substrate layer; and
wherein the predicted future performance state includes a predicted thickness of a substrate layer, the thickness and the predicted thickness being different.
Kaushal teaches wherein the current performance state includes a thickness of a substrate layer (Kaushal paragraph [0060], wherein data includes thickness); and
wherein the predicted future performance state includes a predicted thickness of a substrate layer, the thickness and the predicted thickness being different (Kaushal paragraphs [0115] and [0128], wherein asset targets include thickness, which are predicted by the learned functions and compared).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 20, Phan does not explicitly teach wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool.
Kaushal teaches wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool (Kaushal paragraph [0084], wherein type and quantity of error is supplied as an input to the machine learning).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Regarding Claim 22, Phan does not explicitly teach wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool.
Kaushal teaches wherein the operating data includes data generated by an occurrence of an error associated with the substrate tool (Kaushal paragraph [0084], wherein type and quantity of error is supplied as an input to the machine learning).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Kaushal to apply the known technique of scheduling test and calibration runs for a tool system to optimize a process, yielding the predictable results of improved yield.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Phan as applied to claim 12 above, and further in view of Wu et al., hereinafter Wu, US Publication No. 2022/0214621.
Regarding Claim 13, Phan does not explicitly teach wherein the identified parameter is a lamp intensity.
Wu teaches wherein the identified parameter is a lamp intensity (Wu paragraphs [0057] and [0060], wherein the intensity of the lamp may be adjusted).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Phan and Wu because the combination would allow the known technique of adjusting an edge exposure tool used in a wafer fabrication process as taught by Wu to be utilized in conjunction with the adjusting of tools in a wafer fabrication process as taught by Phan, yielding the predictable results of an improved fabrication process, thereby increasing yield.
Conclusion
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/ERIC D LEE/Primary Examiner, Art Unit 2851
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