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
Current application, US Application No. 18/061,876 filed on 12/05/2022, claims Priority from US Provisional Application 63/295,002, filed on 12/30/2021, and claims foreign priority to TW 110149708, filed on 12/30/2021.
Examiner acknowledges that the certified copy of foreign priority document has been received. However, the certified English translation copy of the original foreign document, which is not written in English, has not been received. There is no requirement to submit certified English translation copy at this stage according to 37 CFR 1.55(g)(3). However, should the need of certified English translated copy arise according to the cases mentioned in 37 CFR 1.55(g)(3), submission may be requested in the future.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/26/2026 has been entered.
DETAILED ACTION
This office action is responsive to the amendment filed on 02/26/2026. Claims 1-2, 4-9 and 11-15 are currently pending. Claims 3 and 10 are canceled per applicant’s request.
Response to Amendment
Applicant's amendment is entered into further examination and appreciated by the examiner.
Response to Arguments/Remarks
Regarding remarks on the rejections under 35 USC 103, applicant’s arguments using amended claims are persuasive and allowable subject matter is indicated. However, the amendments introduced ambiguities and are rejected under 35 USC 112(b). Please amend further to overcome rejections under 35 USC 112(b). Please see the new office action below.
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 1-2, 4-9 and 11-15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. As per claims 1 and 8, the limitations “wherein the measurement signal is raw data or filtered data for analyzing different performance index, wherein the measurement signal is the raw data used as a first signal source when the performance index is to analyze a roughness of the polishing pad, and wherein the measurement signal is the filtered data used as a second signal source when the performance index is to analyze a specific structure height of the polishing pad” are ambiguous because the phrase “different performance index” stands for (1) two different indexes for different analyses respectively or stands for (2) a single index which can be used for two different analyses. For the former case (1), the recited “the performance index” in the later limitations need to be renamed to be differentiated for each analysis. For the latter case (2), the claims fails to show how the single performance index can be discerned to guide which analysis can be applied.
For the sake of examination, the limitations “wherein the measurement signal is raw data or filtered data for analyzing different performance index, wherein the measurement signal is the raw data used as a first signal source when the performance index is to analyze a roughness of the polishing pad, and wherein the measurement signal is the filtered data used as a second signal source when the performance index is to analyze a specific structure height of the polishing pad” are interpreted as “wherein the measurement signal is raw data or filtered data for analyzing two different performance indexes (or indices), wherein the measurement signal is the raw data used as a first signal source when a first performance index is to analyze a roughness of the polishing pad, and wherein the measurement signal is the filtered data used as a second signal source when a second performance index is to analyze a specific structure height of the polishing pad” (see specification – two types of polishing pad, different … performance indexes [0033]).
As per claims 2, 4-7, 9, 11-15, claims are also rejected because base claims 1 and 8 are rejected.
Allowable Subject Matter
Claims 1 and 8 recite subject matter which is allowable over the prior art, and would be allowable if rewritten or amended to overcome current objections and rejections.
The following is a statement of reasons for the indication of allowable subject matter: As per claims 1 and 8, the closest prior art of record, Osterheld (US 20200298368 A1), hereinafter ‘Os’, Bartels (US5961369A), hereinafter ‘Bar’ and Gietzen (US 20220245801 A1), hereinafter ‘Gietz’, either singularly or in combination, fail to anticipate or render obvious limitation “wherein the measurement signal is the filtered data used as a second signal source when the performance index is to analyze a specific structure height of the polishing pad“ in combination with other limitations.
As per claim 1, Os discloses
An intelligent analysis system for measuring signals on a polishing pad surface, (a surface … a polishing pad, an in-situ polishing pad monitoring system [abs], system configured to detect a surface roughness of a polishing pad [0018, Fig. 1],processing system, classifier [0020, 0036, Fig. 3]) comprising:
a measurement signal capturing device configured to measure the polishing pad surface to obtain a measurement signal; (polishing pad, imager, pad monitoring system generate measurements for the surface texture, e.g., surface roughness [0019, 0032, Fig. 2])
and a measurement signal analysis device configured to receive the measurement signal from the measurement signal capturing device, (a controller … receive image from the monitoring system and generate a measure of polishing pad surface roughness based on the image [abs, 0033-0036])
wherein the measurement signal analysis device comprises an artificial intelligence model for analyzing the measurement signal, (machine-learning based image processing system, functional blocks can include … a supervised learning module [0037, Fig. 3], supervised learning module, a process control system … part of the controller [0038])
wherein the artificial intelligence model extracts a feature value from the measurement signal, (machine leaning module … a neural network, output node … generate the surface texture, e.g., surface roughness, measurement [0039], the image from the in-situ pad monitoring system 40 is fed into a trained machine-vision image processing system [0036, Fig. 3], machine-learning based image processing system, functional blocks can include … a supervised learning module [0037, Fig. 3])
However, Os is silent regarding the above allowable limitation.
Bar discloses a measurement signal capturing device configured to select a probe element corresponding to a type of the polishing pad, wherein the measurement signal capturing device is further configured to transmit a probing signal through the probe element to the surface and then receive a measurement signal returned by the surface through the probe element (apparatus 100 … comprise a probe 127 to measure surface characteristics [col 4 line 58-65], Probe assembly 129 is suitably selected such that emitter 312 is capable of applying the desired light signal to the surface 322 of polishing pad 126 [col 5 line 16-21], any suitable probe assembly 129 capable of producing interrogation signal 314, receiving reflected signal 316, and producing control signal 320 [col 5 line 23-30], probe 129, emitter 312 in particular, is positioned … toward polishing pad 126 at an angle [col 9 line 59 – col 10 line 10]), but is silent regarding the above allowable limitations.
Gietz discloses determining and classifying the measurement signal as a normal signal or an abnormal signal after training a classifier or an artificial model which extracts a feature (Trained CNN can classify a section image as normal or depicting failure [abs], neural networks, feature engineering technique [0035], feature for training a classifier [0044]r, trained models, normal images, abnormal ‘or bad or failed’ images [0047]), but is silent regarding the above allowable limitations.
A newly found reference, Suh (CN 115958524 A), recites thickness of structure in a polishing pad and index of the performance of the polishing layer (a polishing pad, which is subdivided in the thickness direction of the structure design [pg. 4 line 2-27, pg. 6 line 10-16], polishing variability index is the index of the variable performance of the polishing variable layer [pg. 42 line 28-37]), but is silent regarding the above allowable limitation.
As per claims 2, 4-7, 9, 11-15, claims would be allowable because base claims 1 and 8 would be allowable.
Notes with regard to Prior Art
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure.
Choo discloses an analysis system and method for polishing pad surface during CMP using artificial intelligence techniques (a system … for monitoring and controlling a … fabrication process, a cost benefit analysis [abs, 0001], CMP … surface is processed by a polishing pad [0032], modeling and/or artificial intelligence technique [0056], sensors … signals … measured parameters, analyze the conditions indicated by the received signals, signals received from the measurement system [0066], training … modeling and/or artificial intelligence techniques [0067, Fig. 1, 4-5], proceed as normal, [Fig. 10]).
Chen (US 20220147841 A1) discloses measuring the draw data set from sensors and predict the anomaly of the data using a trained model (receive a plurality of real-time datasets from one or more sensors associated with a tool to be analyzed, determine if the time slide window includes anomaly data [abs], raw datasets, training datasets [Fig. 1], extract raw data, classify data as normal/abnormal [Fig. 2 202 203], execute anomaly prediction model [Fig. 3, 4]).
Chen (US 11731232 B2) discloses a machine leaning method classifying the data using a model through training (a machine learning model, training data, a status of normal, abnormal [col 16 line 55-col 18 line 3], mechanical component being irregular relative to a normal operation motion [claims 1, 10 and 15]).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOUGLAS KAY/Primary Examiner, Art Unit 2857
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