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 .
DETAILED ACTION
DETAILED ACTION
Claims 1 - 20 are pending in the application.
Claims 1, 11, and 16 are independent.
This action is non-final in this Request for Continued Examination application filed on 03/16/2026.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 112(b) for indefinite, unclear and vague language in the claim limitations. Independent claims 1, 11, and 20 claim a method or system of: “maintaining one or more of the individual tools of the fleet of measurement tools, repairing one or more of the individual tools of the fleet of measurement tools, or both.” The specification and claims do not define or explain how a system or method repairs or maintains a measurement tool, and one of ordinary skill in the art would not understand the scope and meets and bounds of the claims so as to avoid infringement. The dependent claims depend from the independent claims and therefore are also rejected under 35 U.S.C. 112(b). Appropriate action is required.
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 1, 6, 7, 10, 11, 16 are rejected under 35 U.S.C. as being unpatentable over Cao et al. (U.S. PG Pub. No. 20170023491 ), herein "Cao,” in view of Chinese patent document Guo et al. (CN 114896435 A), herein “Guo.”
Regarding claim 1,
Cao teaches a method comprising: estimating values of one or more individual tool productivity metrics characterizing a performance of each individual tool of a fleet of measurement tools operating in a semiconductor fabrication facility; (Par. 0024: “Methods and systems for evaluating and ranking the measurement efficacy of multiple sets of measurement system combinations and recipes for a particular metrology application are presented herein. The metrology application includes the measurement of structural and material characteristics ( e.g., material composition, dimensional characteristics of structures and films, etc.) associated with different semiconductor fabrication processes. Measurement efficacy is based on estimates of measurement precision, measurement accuracy, correlation to a reference measurement, measurement time, or any combination thereof.”)
estimating values of one or more fleet productivity metrics characterizing a performance of the fleet of measurement tools operating in the semiconductor fabrication facility; (Par. 0007: “Microscope (TEM) data to determine measurement efficacy. In some other examples, a user performs model-based analysis to estimate measurement sensitivity to modelled parameters of interest, measurement precision of the modelled parameters of interest, and parameter correlation among different measurement subsystems. These results guide the user in the final determination of the measurement techniques and associated recipes to be used in a particular measurement application.” See also par. 0011, 0013, 0014, 0015, 0044, 0045, 0049, 0054, and 0058 – 0062.)
Cao may implicitly teach the elements of determining metrics of one or more individual tool metrics. However Guo explicitly teaches determining values of one or more combined productivity metrics associated with each of the individual tools (measuring machine) of the fleet of measurement tools, wherein the determined values are based on the values of the one or more individual tool productivity metrics associated with each individual tool and the values of the one or more fleet productivity metrics; (Page 11, Par. 4: “each measuring machine respectively under each measuring model to measure each test pattern, each measuring machine table obtain for measuring the measuring critical dimension of the test pattern measured under each measuring model wherein the test pattern belonging to the same type has a plurality of measurement critical dimensions can be the average value of a plurality of measurement values measured by the measuring machine on the same type of test pattern under a certain measurement model can be used for judging the proximity degree of the target critical dimension of the measuring critical dimension and the test pattern of the type, so as to evaluate the measuring performance of the measuring platform measuring test pattern under each measuring model based on the measuring performance of each measuring machine, classifying the plurality of measuring machines.” See also page 10, last paragraph – page 11, first paragraph. Guo also teaches plurality of measuring machines and different types of measuring machines.)
maintaining one or more of the individual tools of the fleet of measurement tools, repairing one or more of the individual tools of the fleet of measurement tools, or both, based on the values of the one or more combined productivity metrics. (Page 17, Par. 4: “In one exemplary embodiment, when selecting the distribution machine in the pre-distribution machine, it also can consider the real-time working time and the preset working time of the pre-distribution machine table, preferably judging the real-time working time and the preset working time of each pre-distribution machine, selecting the machine with real-time working time less than the preset working time from the distribution machine as the target machine, so as to realize the precise scheduling of the measuring machine. wherein the real-time working time can be the actual working time of the measuring machine after leaving the factory, the preset working time can be the maintenance time set by the measuring machine when leaving factory, it can select the actual working time in the maintenance time range of the measuring machine to measure, so as to reduce the error rate of the measuring machine.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined a system and method of determine or estimating a measurement efficacy of a measurement system (tool) based on metrics such as precision, measurement accuracy, and angle of incidence of the plurality of measurement tools (subsystems) and determining the best measurement tool or subsystem depending on the scenario in Cao with evaluating the performance of the measuring machines and maintaining or giving a maintenance time frame of the measuring machine as in Guo in order to maintain product stability and expand production and reduce error rate. (Guo Page 8, Par. 3 and Page 17, Par. 4)
Regarding claim 6,
The previously cited reference(s) teach the limitations of claim 1 which claim 6 depends. Cao also teaches that at least one of the one or more individual tool productivity metrics characterizing the performance of each individual tool of the fleet of measurement tools is a statistically based metric. (Par. 0052: “In another example, model reliability evaluation module 151 evaluates statistics (e.g., three-sigma values, four-sigma values, etc.) associated with the first derivative of measurement signal values predicted by the measurement model with respect to each parameter of interest for a range of values of each parameter of interest. If the statistical measures indicate broad variance, then the corresponding range of parameter values is excluded from further consideration by model reliability evaluation module 151.” See also Par. 0098. See also Ghu Page 11, Par. 4 that teaches an average of the measuring performance of the measuring machine.)
Regarding claim 7,
The previously cited reference(s) teach the limitations of claim 1 which claim 7 depends. Cao also teaches that at least one of the one or more individual tool productivity metrics characterizing the performance of each individual tool of the fleet of measurement tools is a parameter of an analytical or machine-learning based model. (Par. 0007: “Traditionally, the selection of measurement technique and the associated measurement recipe is performed on a trial-and-error basis. An experienced user manually selects various measurement techniques and recipes and performs an offline analysis to evaluate measurement efficacy. In some examples, measurement results are compared with reference measurement data, e.g., Tunneling Electron Microscope (TEM) data to determine measurement efficacy. In some other examples, a user performs model-based analysis to estimate measurement sensitivity to modelled parameters of interest, measurement precision of the modelled parameters of interest, and parameter correlation among different measurement subsystems. These results guide the user in the final determination of the measurement techniques and associated recipes to be used in a particular measurement application.” See also Par. 0008 and many other paragraphs that teach a model used for tool or measurement system analysis. See also Guo that teaches in 181 instances a measuring “model” developed using multiple measuring machines. )
Regarding claim 10,
The previously cited reference(s) teach the limitations of claim 1 which claim 10 depends. Cao also teaches that each individual tool of the fleet of measurement tools is any of a spectroscopic ellipsometer, a spectroscopic reflectometer, a soft X-ray reflectometer, a small-angle x-ray scatterometer, an imaging system, a hyperspectral imaging system, and a scatterometry overlay metrology system. (Par. 0025: “FIG. 1 illustrates a system 100 for measuring characteristics of a semiconductor wafer. As shown in FIG. 1, the system 100 may be used to perform spectroscopic ellipsometry measurements of one or more structures 114 of a semiconductor wafer 112 disposed on a wafer positioning system 110. In this aspect, the system 100 may include a spectroscopic ellipsometer 101 equipped with an illuminator 102 and a spectrometer 104.”)
Regarding claim 11 it is directed to a system or apparatuses to implement the method of steps set forth in claim 1. Cao teaches the claimed method of steps in claim 1. Cao also teaches the element of: an illumination source configured to provide an amount of illumination radiation to one or more structures disposed on a semiconductor wafer; a detector configured to receive an amount of collected radiation from the one or more structures in response to the amount of illumination radiation and generate measurement signals indicative of the collected radiation; (Par. 0088: “Although the methods discussed herein are explained with reference to system 100, any optical metrology system configured to illuminate and detect light reflected, transmitted, or diffracted from a specimen may be employed to implement the exemplary methods described herein. Exemplary systems include an angle-resolved reflectometer, a scatterometer, a reflectometer, an ellipsometer, a spectroscopic reflectometer or ellipsometer, a beam profile reflectometer, a multi-wavelength, two-dimensional beam profile reflectometer, a multi-wavelength, two-dimensional beam profile ellipsometer, a rotating compensator spectroscopic ellipsometer, etc.” See also Cao claim 8.) Therefore, Cao teaches the system or apparatuses to implement the claimed method of steps in claim 11.
Regarding claim 16, it is directed to a system with a non-transitory computer readable medium storing instructions to implement the method of steps set forth in claim 1. Cao and Guo teaches the claimed method of steps in claim 1. Cao also teaches the element of: a non-transitory, computer-readable medium storing computer-readable instructions, the computer-readable instructions, when executed by one or more processors of a computing system (Cao claim 9: “An automated measurability ranking tool comprising computer-readable instructions stored on a non-transitory, computer-readable medium, the computer-readable instructions comprising: code for causing a computing system to receive a measurement model indicative of a measurement of a parameter of interest by a metrology system at a plurality of measurement scenarios…”) Therefore, Cao teaches the system or apparatuses to implement the claimed method of steps in claim 11.
Claims 2, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cao in view of Guo in further view of Chinese patent document Biagini et al. (CN 109844664 A), herein “Biagini.”
Regarding claim 2,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Cao and Guo do not teach a statistical distance for a measurement tool. However, Biagini does teach determining of the values of one or more combined productivity metrics associated with each of the individual tools of the fleet of measurement tools involves determining a statistical distance between values of an individual tool productivity metric associated an individual tool of the fleet of measurement tools and values of a fleet productivity metric associated with the fleet of measurement tools. (Page 7, Par. 6: “…the other technology for use with a production measurement is a statistical p-value techniques. in the preceding example, the statistical test (e.g., variance analysis) to analyze five measuring step to test three measuring tool for producing the average value equal to the hypothesis, the probability (or "p-value") < α (wherein α is 0.05) generally can be used as more than random chance of system effect of evidence. whether there is a detectable difference between the determined tool, but the one measurement step with maximum discharge priority and without assistance. formulation/measuring step combined with a maximum sample size with the smallest p-value, keeping all other factors constant. those formulation/measuring step even as the potential problem of combination with other measurement step will not be worse than the measuring tool changes (or possibly even better), it should be noted that these combinations. The TMU monitor disclosed in herein can avoid the statistical p-value technology and can provide robust ranking measurement system effects of changes across sample size measuring step.” See also Page 2, Par. 5: “…in a complex manufacturing environment, many variables and is constantly changing. Although the herein-mentioned semiconductor environment, but principle is connected to any manufacturing environment. For example, in a semiconductor manufacturing environment, according to a process recipe, measurement tool, the overall process health condition, the measuring tool health status and other variable parameters are in continuous change. provides a technique to monitor measures the change of the manufacturing facility to ensure that the change will not become worse over time for the manufacturer can be valuable. If a change is detected, then can quickly respond to solve the detected change.” See also Page 12, Par. 1 starting with: “the standard deviation. The analysis…” See also Page 10, Par. 2 (“evaluate the accuracy according to the type of measuring tool.” )
would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined a system and method of determine or estimating a measurement efficacy of a measurement system (tool) based on metrics such as precision, measurement accuracy, and angle of incidence of the plurality of measurement tools (subsystems) and determining the best measurement tool or subsystem depending on the scenario in Cao with evaluating the performance of the measuring machines and maintaining or giving a maintenance time frame of the measuring machine as in Guo with analyze different measurement tools that produce a statistical value that produces the most accurate results of measurement as in Biagini in order to help the semiconductor manufacturer to more efficiently solve the problem of process control. (Page 15, Par. 3)
Regarding claim 12, it is directed to a system or apparatuses to implement the method of steps set forth in claim 2. Cao, Guo, and Biagini teach the claimed method of steps in claim 2. Therefore, Cao, Guo, and Biagini teach the system or apparatuses to implement the claimed method of steps in claim 12.
Regarding claim 17, it is directed to a system with a non-transitory computer readable medium storing instructions to implement the method of steps set forth in claim 2. Cao, Guo, and Biagini teach the claimed method of steps in claim 2. Therefore, Cao, Guo, and Biagini teach the non-transitory computer readable medium storing instructions to implement the claimed method of steps in claim 17.
Claims 3 – 5, 8, 9, 13 - 15, and 18 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cao in view Guo in further view of Kaushal et al. (US PG Pub. No. 20140135970), herein “Kaushal.”
Regarding claim 3,
The previously cited reference(s) teach the limitations of claim 1 which claim 3 depends. They do not teach a selecting or scheduling of maintenance of a tool. However, Kaushal does teach selecting (scheduling) an individual tool for maintenance based on the value of the one or more combined productivity metrics associated with the individual tool. (Par. 0084: “At 1616, the N tool parameters are ranked according to the scores determined at 1614, and the M highest ranked tool parameters are identified. These M highest ranked tool parameters represent the subset of the total tool parameters determined to have the greatest impact on the tool performance metric. At 1618, a new function is generated that models the tool performance indicator as a function of the M highest ranked parameters identified at step 1616. This new function can be used to predict future tool performance behavior based on tool parameter trends, assist with scheduling preventative maintenance and identifying where maintenance efforts should be focused, or other such applications.”)
would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined a system and method of determine or estimating a measurement efficacy of a measurement system (tool) based on metrics such as precision, measurement accuracy, and angle of incidence of the plurality of measurement tools (subsystems) and determining the best measurement tool or subsystem depending on the scenario in Cao with evaluating the performance of the measuring machines and maintaining or giving a maintenance time frame of the measuring machine as in Guo with generating tool performance indicators and then scheduling maintenance based on performance behavior as in Kaushal in order to minimize downtime and maximize yields. (Par. 0003)
Regarding claim 4,
The previously cited reference(s) teach the limitations of claim 1 which claim 4 depends. They do not teach a selecting or scheduling of maintenance of a tool based on performance statistics. However, Kaushal does teach determining a difference between the values of the one or more individual tool productivity metrics associated with each individual tool and an average value of the values of the one or more individual tool productivity metrics associated with the individual tools comprising the fleet of measurement tools; (Par. 0045: “Tool parameter data 108 can comprise values measured for one or more tools during operation (e.g., pressures, temperatures, power, gas flows, etc.), operational performance statistics (e.g., part age or usage count, processing times, set-up times, load or unload times, etc.), or other such tool parameters. Tool performance data 112 can include measured characteristics of the finished semiconductor wafers which are impacted by one or more of the tool parameters (e.g., etch bias, deposition thickness, particle count, sidewall angle, etc.), performance data for the tool itself (e.g., wafer throughput, downtime, uptime, repair costs, etc.), or other such metrics indicative of the tool's operational performance.” Examiner’s Note – an average value is a performance statistic as taught by Kaushal.)
and selecting an individual tool for maintenance based on the determined difference and the values of the one or more combined productivity metrics associated with the individual tool. (Par. 0084: “At 1616, the N tool parameters are ranked according to the scores determined at 1614, and the M highest ranked tool parameters are identified. These M highest ranked tool parameters represent the subset of the total tool parameters determined to have the greatest impact on the tool performance metric. At 1618, a new function is generated that models the tool performance indicator as a function of the M highest ranked parameters identified at step 1616. This new function can be used to predict future tool performance behavior based on tool parameter trends, assist with scheduling preventative maintenance and identifying where maintenance efforts should be focused, or other such applications.”)
Regarding claim 5,
The previously cited reference(s) teach the limitations of claim 1 which claim 5 depends. They do not teach performance of individual tool as a downtime, or reset rate.. etc. However, Kaushal does teach performance of each individual tool of the fleet of measurement tools is a tool downtime rate, a duration of tool downtime, a tool reset rate, a time between scheduled resets, a time between unscheduled resets, or any combination thereof. (Par. 0045: “Tool parameter data 108 can comprise values measured for one or more tools during operation (e.g., pressures, temperatures, power, gas flows, etc.), operational performance statistics (e.g., part age or usage count, processing times, set-up times, load or unload times, etc.), or other such tool parameters. Tool performance data 112 can include measured characteristics of the finished semiconductor wafers which are impacted by one or more of the tool parameters (e.g., etch bias, deposition thickness, particle count, sidewall angle, etc.), performance data for the tool itself (e.g., wafer throughput, downtime, uptime, repair costs, etc.), or other such metrics indicative of the tool's operational performance.”)
Regarding claim 8,
The previously cited reference(s) teach the limitations of claim 1 which claim 8 depends. They do not teach estimating accuracy and confidence of a ranking. However, Kaushal does teach estimating (predicting) a value of an accuracy metric indicative of a confidence in the ranking of an individual tool among the fleet of measurement tools. (Par. 0049: “Interface component 204 can be configured to receive input from and provide output to a user of parameter impact identification system 202. For example, interface component 204 can render an input display screen to a user that prompts for user specifications, and accepts such specifications from the user via any suitable input mechanism (e.g., keyboard, touch screen, etc.). Parameter separation component 206 can be configured to generate functions that isolate the effects of each individual tool parameter to determine the impact of each tool parameter on a selected tool performance indicator. Each function attempts to predict the behavior of the selected tool performance indicator as a function of a single tool parameter. Quality scoring component 208 can be configured to score each tool parameter according to how well the parameter's function predicts the actual behavior of the tool performance indicator. Sensitivity component 210 can be configured to determine a sensitivity of the selected tool performance indicator to each tool parameter based in part on the functions generated by parameter separation component 206.” Par. 0008: “The impact of each parameter on the performance indicator can then be determined based on an analysis of the resulting functions (e.g., by calculating a derivative of each function, by determining a predictive accuracy at each function, etc.), and the tool parameters ranked according to relative impact.” Par. 0086: “A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed.” Examiner’s Note – See also Guo that teaches classifying the measuring machine (Page 3, Par. 8: “…classifying the measuring machines with the same target measurement model one type.” Page 8, last paragraph, and Page 10, last paragraph.)
Regarding claim 9,
The previously cited reference(s) teach the limitations of claim 1 which claim 9 depends. They do not teach predicting a failure (or when the tool falls out of the performance limit). However, Kaushal does teach predicting a probability of a future failure event associated with at least one individual tool of the fleet of measurement tools based on a difference between a predicted probability distribution of the failure event and an actual, observed distribution of the failure event. (Par. 0073: “Composite function 322 greatly simplifies analysis of the tool performance indicator by reducing the problem space to a relatively small set of critical tool parameters, allowing users to focus more sharply on those critical parameters. The composite function can be leveraged in a number of ways to facilitate analysis of a selected tool performance aspect with respect to the tool parameters that determine the behavior of this performance aspect. For example, new tool parameter data can be analyzed in view of composite function 322 in order to predict future values of the tool performance indicator. If one or more tool parameter values begin drifting due to part degradation, expected future values of these tool parameters can be run through composite function 322 to determine when the tool performance indicator O.sub.NEW is expected to fall outside acceptable performance limits. In this way, composite function 322 can be used as a basis for a near real-time early warning system that identifies when preventative maintenance should be performed and which tool parameters should be the focus of maintenance efforts. Composite function 322 can also be analyzed more generally to provide insight into the relationships between the critical tool parameters and the predicted tool performance indicator O.sub.NEW. Thus, parameter impact identification system 308 serves as an efficient functional modeling system that reduces the search space for performing functional relationship modeling for a semiconductor fabrication system.”)
Regarding claims 13 - 15, they are directed to a system or apparatuses to implement the method of steps set forth in claims 3, 8, and 9, respectively. Cao, Guo, and Kaushal teach the claimed method of steps in claims 3, 8, and 9. Therefore, Cao, Guo, and Kaushal teach the system or apparatuses to implement the claimed method of steps in claims 13 - 15.
Regarding claims 18 – 20, they are directed to a system with a non-transitory computer readable medium storing instructions to implement the method of steps set forth in claims 3, 8, and 9, respectively. Cao, Guo, and Kaushal teach the claimed method of steps in claims 3, 8, and 9. Therefore, Cao, Guo, and Kaushal teach the non-transitory computer readable medium storing instructions to implement the claimed method of steps in claims 18 – 20.
Response to Arguments
The previous 35 USC §112(a) rejection for new matter is rescinded based on the claims 1, 11, and 16 that deleted elements concerning the new matter. A new 35 USC §112(b) rejection is written for vague and unclear subject matter not defined by the claim. See rejection above. The “maintaining” or “repairing” is simply not defined or explained, and one of ordinary skill in the art would not understand the scope and meets and bounds of the claims so as to avoid infringement.
Examiner is not persuaded that Cao does not teach the element of: estimating values of one or more fleet productivity metrics characterizing a performance of the fleet of measurement tools operating in the semiconductor fabrication facility. Cited paragraph 0007 teaches: “model-based analysis to estimate measurement sensitivity to modelled parameters of interest, measurement precision of the modelled parameters of interest, and parameter correlation among different measurement subsystems. These results guide the user in the final determination of the measurement techniques and associated recipes to be used in a particular measurement application.” The measurement subsystems may be different types and offer different azimuth angles and angle of incidence options as stated in paragraph 0009 which clearly teach the claimed invention of productivity metrics. The Abstract of Cao specifically states: “performance metrics” of each measurement system; which may or may not be the “angles.” Paragraph 0054 also defines the measurement metrics have different angles and precision. And cited paragraphs 0007 and 0054 teaches estimating the measurement subsystems.
Applicant argues that Cao does not teach or suggest "estimating values of one or more fleet productivity metrics characterizing a performance of the fleet of measurement tools operating in the semiconductor fabrication facility.” In support of this statement, applicant states that “Cao does not teach or suggest any fleet of measurement tools.” However, Cao teaches a plurality of measurement subsystems including spectroscopic ellipsometry and spectroscopic reflectometry (Par. 0044). Cao also teaches in several paragraphs, measurement subsystems having different metrics such as precision and angles (incidence and azimuth) each having measured or requisite precision. (Par. 0054)
The new reference of Guo teaches overlapping elements and teaches those elements that Cao does not explicitly teach including determining the productivity metrics or evaluating the measuring performance of each measuring machine as rejected above. Guo also teaches the amended portion of maintaining the tool (measuring machine) as cited above (Page 17, Par. 4). Guo also teaches the original claim 14 of ranking the individual tool (classifying the plurality of measuring machines, Page 11, Par. 4).
Given the argument the 35 USC 101 rejection is rescinded; however, a 35 USC 112(b) rejection is stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cao et al. (US Patent No. 10,502,692), published before the effective filing date of the instant application also teaches the amended portion determining productivity metrics of a during a time period of production , each individual tool of the fleet of measurement tools operating in accordance with a fixed measurement recipe during the time period of production. (See Col. 3, lines 3 – 12; Col. 4, lines 14 – 17; Col. 7, lines 57 – 67; and Col. 12, lines 51 – 59)
Zhu et al. (CN 114325346 A), herein “Zhu” also teaches a plurality of measuring machines used to measure semiconductors. Zhu is a similar reference as Guo and teaches metrics such as accuracy and precision of measuring machines.
Willems et al. (WO 2016162228), cited in the previous office action, teaches multiple inspection apparatuses for a semiconductor substrate that are ranked according to one or more performance patterns.
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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