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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
This Office Action is responsive to communication filed on 1/19/2026.
Claims 1, 7-8 and 10-12 are amended.
Claims 1-12 are pending and presented for examination.
Response to Arguments/Amendment
Regarding claim interpretation under 35 U.S.C. 112(f)
Applicant Argues
Claim 7 as amended does not invoke 35 U.S.C. 112(f).
Examiner Responds
Applicant’s amendment to claim 7 recite sufficient structure to perform the claimed function. As such, the claim is no longer invokes §112(f).
Regarding rejections under 35 U.S.C. 103
Applicant Argues
The cited combination does not teach or suggest claim 1 or 7.
Examiner Response
Applicant’s arguments with respect to claims 1 and 7 have been fully considered but are moot because Applicant’s arguments are over amended features.
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 1-3, 5-9 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over SUZUKI (US20200101579A1)1 in view of GUO (US20230400847) (hereinafter – “SUZUKI-GUO”).
Regarding claim 1
SUZUKI teaches:
polishing a workpiece by pressing the workpiece against a polishing surface of a polishing pad with an elastic membrane forming a pressure chamber provided in a polishing head ([0050]: polishes wafer while pressing wafer against polishing pad; [0055]: pressure chambers P1-P4 are formed of elastic pad);
controlling a pressure in the pressure chamber on the basis of a measure value of a film thickness of the workpiece while measuring the film thickness during the polishing of the workpiece ([0065]-[0066]: monitoring signal can be a signal representing the film thickness, control section determines the internal pressures of P1-P4 based on the monitoring signal);
inputting time-series pressure data representing [0111]: machine learning apparatus (end point detection system) includes state acquisition section capable of acquiring a state variable including at least one of data on the state of device making up polishing unit and data on the state of wafer; [0113]: data includes data detected by pressure controller; [0115]: pressure controller includes a pressure sensor).
SUZUKI is not relied on for inputting time-series pressure data representing a change in the pressure, wherein the time series pressure data is changed depending on a state of wear of the polishing pad. SUZUKI is also not relied on for outputting a life index of the polishing pad from the learned model.
However, GUO in analogous art teaches methods for predictive maintenance for semiconductor manufacturing equipment, comprising:
inputting time-series pressure data representing a change in the pressure during the manufacturing of workpiece into a learned model, wherein the time series pressure data is changed depending on a state of wear ([0050]: “trained machine learning model can be a neural network that takes, as inputs, data indicating operating conditions of manufacturing equipment or a component of manufacturing equipment and generates, as an output, predicted equipment health status”; [0065]-[0066]: “examples of equipment health status scores or metrics for components of a system or sub-system can include a Remaining Useful Life (RUL) of the component. For example, in some embodiments, a predictive maintenance system can determine that the component will need to be replaced at a particular time in the future […] the predictive maintenance system can identify a change to a recipe used by the manufacturing equipment (e.g. a temperature change, a pressure change, and/or any other suitable recipe change) that is likely to extend the RUL of the component”, i.e., 0066 teaches that the wear rate of the pad corresponds to changes in pressure; [0091]: once trained, equipment health status machine learning model 114 can generate estimated and/or predicted equipment health status information; [0092]: real-time data signals 116 can be received and can be a set of time series data sequences, such as a pressure data time series; [0093]: “Derived real-time data 118 can be generated using real-time data signals 116. For example, in some embodiments, derived real-time data 118 can be generated using a feature extraction model applied to real-time data signals 116”, Fig. 2B #212 second plot shows real-time data signal of chamber pressure fluctuating as a function of time of which can be used to produce pressure change/derived real time data); and
outputting a life index of the polishing pad from the learned model ([0050]: “trained machine learning model can […] generate as an output, predicted equipment health status”; [0066]: “examples of equipment health status scores or metrics for components of a system or sub-system can include a Remaining Useful Life (RUL) of the component”).
SUZUKI and GUO are analogous art to the claimed invention because they are from the same field of semiconductor manufacturing. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of GUO to the teachings of SUZUKI such that GUO’s determination of a remaining useful life could be used with SUZUKI’s control and state acquisition sections for the purposes of preventing significant equipment downtime by identifying an impending component failure before the component fails (GUO, [0002]).
Regarding claim 2
SUZUKI-GUO teaches the elements of claim 1 as outlined above.
SUZUKI also teaches in addition to the time-series pressure data, polishing data relating to the polishing of the workpiece is input to the learned model ([0126]: data input into learning section such as motor current, elapsed time, temperature of pad).
Regarding claim 3
SUZUKI-GUO teaches the elements of claim 2 as outlined above.
SUZUKI also teaches polishing data includes at least one information of the workpiece and polishing conditions for the workpiece ([0126]: data input into learning section such as thickness of film, thickness of pad)
Regarding claim 5
SUZUKI-GUO teaches the elements of claim 1 as outlined above.
SUZUKI also teaches inputting the time-series pressure data into the learned model and outputting the life index of the polishing pad from the learned model are performed after the polishing of the workpiece and before polishing of a next workpiece ([0148]: offline learning).
Regarding claim 6
SUZUKI-GUO teaches the elements of claim 1 as outlined above.
SUZUKI also teaches wherein the pressure chamber is a plurality of pressure chambers ([0055]: pressure chambers P1-P4), the step of controlling the pressure in the pressure chamber is a step of controlling pressures in the plurality of pressure chambers on the basis of the measured value of the film thickness of the workpiece while measuring the film thickness during the polishing of the workpiece ([0065]-[0066]: monitoring signal can be a signal representing the film thickness, control section determines the internal pressures of P1-P4 based on the monitoring signal), and the step of inputting the time-series pressure data respectively representing changes in the pressure in the plurality of pressure chambers during the polishing of the workpiece ([0111]: machine learning apparatus (end point detection system) includes state acquisition section capable of acquiring a state variable including at least one of data on the state of device making up polishing unit and data on the state of wafer; [0113]: pressure sensors provided in the respective fluid paths to pressure chambers P1-P4, data includes data detected by pressure controller).
Regarding claim 7
SUZUKI teaches:
a polishing head that includes a pressure chamber formed by an elastic membrane and polishes a workpiece by pressing the workpiece against a polishing surface of a polishing pad with the elastic membrane ([0050]: polishing head; [0050]: polishes wafer while pressing wafer against polishing pad; [0055]: pressure chambers P1-P4 are formed of elastic pad)
a film thickness sensor that measures a film thickness of the workpiece ([0114]: film thickness sensor);
at least one processor configured to control a pressure in the pressure chamber during the polishing of the workpiece on the basis of a measured value of the film thickness ([0065]-[0066]: monitoring signal can be a signal representing the film thickness, control section 65 determines the internal pressures of P1-P4 based on the monitoring signal; [0161]: control section 65 comprises processor); and
to input time-series pressure data representing [0111]: machine learning apparatus (end point detection system) includes state acquisition section capable of acquiring a state variable including at least one of data on the state of device making up polishing unit and data on the state of wafer; [0113]: data includes data detected by pressure controller; [0115]: pressure controller includes a pressure sensor).
SUZUKI is not relied on to input time-series pressure data representing a change in the pressure, wherein the time series pressure data is changed depending on a state of wear of the polishing pad.
However, GUO in analogous art teaches predictive maintenance for semiconductor manufacturing equipment, comprising:
to input time-series pressure data representing a change in the pressure during the manufacturing of workpiece into a learned model, wherein the time series pressure data is changed depending on a state of wear ([0050]: “trained machine learning model can be a neural network that takes, as inputs, data indicating operating conditions of manufacturing equipment or a component of manufacturing equipment and generates, as an output, predicted equipment health status”; [0065]-[0066]: “examples of equipment health status scores or metrics for components of a system or sub-system can include a Remaining Useful Life (RUL) of the component. For example, in some embodiments, a predictive maintenance system can determine that the component will need to be replaced at a particular time in the future […] the predictive maintenance system can identify a change to a recipe used by the manufacturing equipment (e.g. a temperature change, a pressure change, and/or any other suitable recipe change) that is likely to extend the RUL of the component”, i.e., 0066 teaches that the wear rate of the pad corresponds to changes in pressure; [0091]: once trained, equipment health status machine learning model 114 can generate estimated and/or predicted equipment health status information; [0092]: real-time data signals 116 can be received and can be a set of time series data sequences, such as a pressure data time series; [0093]: “Derived real-time data 118 can be generated using real-time data signals 116. For example, in some embodiments, derived real-time data 118 can be generated using a feature extraction model applied to real-time data signals 116”, Fig. 2B #212 second plot shows real-time data signal of chamber pressure fluctuating as a function of time of which can be used to produce pressure change/derived real time data).
SUZUKI and GUO are analogous art to the claimed invention because they are from the same field of semiconductor manufacturing. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of GUO to the teachings of SUZUKI such that GUO’s determination of a remaining useful life could be used with SUZUKI’s polishing device for the purposes of preventing significant equipment downtime by identifying an impending component failure before the component fails (GUO, [0002]).
Regarding claim 8
SUZUKI-GUO teaches the elements of claim 7 as outlined above.
The remaining limitations of claim 8 are substantially the same as claim 2 and are rejected as per claim 2.
Regarding claim 9
SUZUKI-GUO teaches the elements of claim 8 as outlined above.
The remaining limitations of claim 9 are substantially the same as claim 3 and are rejected as per claim 3.
Regarding claim 11
SUZUKI-GUO teaches the elements of claim 7 as outlined above.
The remaining limitations of claim 11 are substantially the same as claim 5 and are rejected as per claim 5.
Regarding claim 12
SUZUKI-GUO teaches the elements of claim 7 as outlined above.
The remaining limitations of claim 12 are substantially the same as claim 6 and are rejected as per claim 6.
Claims 4 and 10 are rejected under 35 U.S.C. as being unpatentable over SUZUKI-GUO in further view of HUANG (US20200130136A1)2.
Regarding claim 4
SUZUKI-GUO teaches the elements of claim 1 as outlined above.
SUZUKI-GUO are not relied on for inputting a cut rate of the polishing pad which has been dressed by a dresser into the learned model.
However, HUANG in analogous art teaches inputting a cut rate of the polishing pad which has been dressed by a dresser into the learned model ([0054]: predict polishing pad wear by taking into account trajectory of conditioning disk (dresser), polishing strength of conditioning disk, rotation speed of condition disk).
HUANG is analogous art to the claimed invention because they are from the same field semiconductor manufacturing. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of HUANG to the teachings of SUZUKI-GUO such that HUANG’s dressing data could be used with SUZUKI-GUO’s remaining useful life prediction for the purposes of improving the prediction accuracy.
Regarding claim 10
SUZUKI-GUO teaches the elements of claim 7 as outlined.
The remaining limitations of claim 10 are substantially the same as claim 4 and are rejected as per claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang, O., et al., (“Mathematical modeling of CMP conditioning process”, published on 1/5/2007, retrieved from https://www.sciencedirect.com/science/article/pii/S0167931706006356, retrieved on 2/24/2026) teaches Preston’s equation to correlate polishing pad thickness with pressure.
Tso, P., et al., (“Estimating chemical mechanical polishing pad wear with compressibility”, published on 3/31/2006, retrieve from https://link.springer.com/article/10.1007/s00170-005-0386-1, retrieved on 2/24/2026) teaches a method of estimating polishing pad wear.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael V Farina whose telephone number is (571)272-4982. The examiner can normally be reached Mon-Thu 8:00-6:00 EST.
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/M.V.F./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 SUZUKI is a prior art reference cited in the previous office action.
2 HUANG is a prior art reference cited in the previous office action.