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 .
Election/Restrictions
Claims 17-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected group, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 9/16/25.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the time-series in-situ results data" in line 12. There is insufficient antecedent basis for this limitation in the claim. Referring to claim 1, line 21 recites the limitation “the time-series in-situ results data”, however line 17 recites the limitations of “generating time-series in-situ results data”, and line 12 recites the limitation of “the time-series in-situ results data”. Therefore the recitation of “the time-series in-situ results data” in the same or subsequent claim is unclear because it is uncertain which of the time-series in-situ results data was intended (MPEP 2173.05(e)). For purposes of examination, it will be assumed to be any of these.
Referring to claim 1, limitation d, it is unclear how the configuration of the camera, in the wherein clause regarding the time-series in-situ results data, further limits the data derived from a signal provided from the camera, or how the configuration of the camera further limits the generation of processing system data.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pub. No. 2019/0286075 to Yennie, supplied by applicant.
1. A computer-implemented method of polishing substrates, comprising:
polishing a substrate using a polishing system (see "Chemical mechanical polishing (CMP)" in par. 2 and 4. See also par. 16, 48, 70), comprising:
(a) flowing a polishing fluid onto a radial position (paragraph 5, the slurry distribution) of a surface of a polishing pad, according to a polishing recipe, the polishing recipe comprising a plurality of polishing parameters and a corresponding plurality of target values (see "Chemical mechanical polishing (CMP)" in par. 2 and 4. See also par. 16, 48, 70);
(b) urging a substrate against the surface of the polishing pad according to the polishing recipe (par. 12: "Each polishing system includes a support to hold a polishing pad, a carrier to hold a substrate against the polishing pad, a motor to cause relative motion between the substrate and the polishing pad');
(c) maintaining, by adjusting a first control parameter, a first polishing parameter of the plurality of polishing parameters at or near its target value (par. 12: " ... to control at least one polishing control parameter of based on the sequence of characterizing values ... ");
(d) generating processing system data comprising the polishing recipe and time-series data of the first control parameter (par. 70: "The processing system is controlled by various control parameters. Data to set the control parameters as a function of time can be termed a "recipe." For example, in a polishing system the control parameters can be set in a recipe that indicates polishing pressure in various chambers of the carrier head, as well as other parameters such as carrier head rotation rate, platen rotation rate, slurry dispensing rate, carrier head sweep, slurry composition, etc. Any of these control parameters can be specified as a function of time."),
wherein the time-series in-situ results data comprises: data derived from a signal provided from a camera configured to monitor a distribution of the polishing fluid dispensed on the surface of the polishing pad (see "in-situ monitoring system" in par. 12, 16, 45, 52, 75; Yennie teaches in-situ results data comprises: data derived from a signal provided from a camera (see "in-situ monitoring system" in par. 12, 16, 45, 52, 75)); and
(e) concurrently with (a)-(d), generating time-series in-situ results data using measurements obtained from an in-situ substrate monitoring system (see "in-situ monitoring system" in par. 12, 16, 45, 52, 75);
repeating (a)-(e) for a plurality of substrates to obtain a corresponding plurality of training data sets, each of the training data sets comprising the processing system data and the time series in-situ results data for a polished substrate (par. 12: " ... At least one controller of at least one of the plurality of polishing system is configured to cause one or more of the plurality of polishing systems to polish a series of training substrates .... to cause the polishing system from the one or more systems to polish a series of device substrates, to receive a sequence of measurements of the device substrates from the in-situ monitoring system of the one or more systems");
receiving, at an artificial intelligence (AI) training platform, training data comprising the plurality of training data sets, wherein at least a portion of the plurality of training data sets are received sequentially in time (see "algorithm generation platform" in par. 14); and
training a machine learning AI algorithm using the training data, the trained machine learning AI algorithm determining a functional relationship between one or more polishing control parameters and the time-series in-situ results data (par. 14, 70, 72; par. 44-45, “the polishing system can use the trained model to convert a spectrum from the in-situ monitoring system to a thickness value, which can be used to detect the polishing endpoint or determine an adjustment for a pressure in a carrier head.”; paragraph 80-101); and
adjusting one or more of the plurality of polishing parameters based on the identified functional relationship (par. 14: "The implemented machine learning model is trained using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and the trained machine learning model is passed to the controller of the one or more polishing systems for control of polishing of the device substrates.", par. 99: "For example, during polishing of a substrate in the polishing system, the substrate can be monitored with an in- situ spectrographic monitoring system to generate a plurality of measured spectra of the substrate being polished. The plurality of measured spectra are passed to the trained machine learning model to generate a plurality of characterizing values, e.g., thickness measurements, and at least one processing parameter of the polishing system is controlled based on the plurality of characterizing values.").
2. The method of claim 1, wherein the target values comprise desired set points, values above a desired lower threshold, values below a desired upper threshold, and/or values between desired the lower and upper thresholds for each of the polishing parameters (par. 60, 72).
3. The method of claim 1, wherein the in-situ results data comprises data derived from a signal provided from a camera that is positioned to view and is configured to detect a variation in temperature of at least a portion of the surface of the polishing pad (par. 6, 37, 52-56, 66, 71).
4. The method of claim 3, wherein the first polishing parameter comprises a temperature of the surface of the polishing pad, and the first control parameter comprises a flow rate of a coolant delivered to the surface of the polishing pad or a flow rate of a polishing fluid delivered to the surface of the polishing pad (par. 70).
5. The method of claim 1, wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect an amount of coverage of the polishing fluid dispensed on the surface of the polishing pad from a polishing fluid delivery nozzle (par. 6, 37, 52-56, 66, 71).
6. The method of claim 5, wherein the first control parameter comprises: a flow rate of the polishing fluid delivered to the surface of the polishing pad, or the radial position of the polishing fluid delivery nozzle relative to the surface of the polishing pad (par. 66, 70).
7. The method of claim 1, wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a temperature of at least a portion of the surface of the polishing pad, and data derived from a signal provided from a sensor that is configured to detect a composition of the polishing fluid (par. 6, 37, 52-56, 66, 71).
8. The method of claim 7, wherein the first polishing parameter comprises a temperature of the surface of the polishing pad, and the first control parameter comprises a flow rate of a coolant delivered to the surface of the polishing pad or a flow rate of the polishing fluid delivered to the surface of the polishing pad (par. 66, 70).
9. The method of claim 1, wherein the in-situ results data comprises data derived from a signal provided from a camera that is positioned to detect a roughness of the surface of the polishing pad, or positioned to detect an optical property of the surface of the polishing pad, the first polishing parameter comprises a pad conditioning parameter of the surface of the polishing pad, and the first control parameter comprises a rotation speed of a conditioning disk, a downforce exerted on the conditioning disk against the polishing pad, a dwell time of the conditioning disk over one or more portions of the surface of the polishing pad, or a sweep speed of the conditioning disk across the surface of the polishing pad (par. 6, 37, 52-56, 66, 71).
10. The method of claim 1, wherein maintaining the first polishing parameter at or near its target value comprises: i determining a difference between an actual value of the first polishing parameter and its target value; ii. based on the determined difference, adjusting the first control parameter of a first control system; and iii. continuously repeating i. and ii. to provide closed-loop control over the first polishing parameter (par. 60, 72).
11. The method of claim 10, wherein the first polishing parameter comprises a temperature of the surface of the polishing pad (par. 66).
12. The method of claim 11, wherein the polishing fluid comprises a slurry composition, and the first control parameter comprises a flow rate or an amount of the slurry composition delivered to the surface of the polishing pad (par. 70).
13. The method of claim 12, wherein the first control parameter comprises a flow rate of a coolant delivered to the surface of the polishing pad (par. 70).
14. The method of claim 10, wherein the adjusting one or more of the plurality of polishing parameters based on the analysis of the received training data performed by the machine learning AI algorithm further comprises adjusting a composition of the polishing fluid disposed on the surface of the polishing pad based on the functional relationship (par. 14, 70, 72).
15. The method of claim 14, wherein adjusting the composition of the polishing fluid includes starting, stopping, or changing a flowrate of an individual polishing fluid component delivered to the surface of the polishing pad (par. 14, 70, 72).
16. The method of claim 1, wherein the training data used to train the machine learning AI algorithm further comprises one or a combination of: substrate tracking data comprising processing histories of one or more of the plurality of substrates and/or information related to devices formed thereon; facilities system data comprising information generated using one or more facilities supply systems including analytical information of polishing fluids delivered to the polishing system from a remote polishing fluid distribution system; and electrical test data comprising electrical test information generated from one or more of the plurality of substrates at a post-polishing electrical test measurement operation (par. 24, 36, 37, 38, 42, 70, 75).
Response to Arguments
Applicant's arguments filed 6/19/26 have been fully considered but they are not persuasive.
Applicant appears to argue the proposed amendments discussed during the interview on 6/17/26 were made. The examiner disagrees. The amendments filed 6/19/26 are different.
Applicant argues Yennie fails to teach the trained machine learning AI algorithm determining a functional relationship between one or more polishing control parameters and the time-series in-situ results data. The examiner disagrees. Yennie teaches the polishing system can use the trained model to convert a spectrum from the in-situ monitoring system to a thickness value, which can be used to detect the polishing endpoint or determine an adjustment for a pressure in a carrier head (par. 14, 70, 72; par. 44-45, “the polishing system can use the trained model to convert a spectrum from the in-situ monitoring system to a thickness value, which can be used to detect the polishing endpoint or determine an adjustment for a pressure in a carrier head.”; paragraph 80-101), which reads on training a machine learning AI algorithm using the training data, the trained machine learning AI algorithm determining a functional relationship between one or more polishing control parameters and the time-series in-situ results data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN P SHECHTMAN whose telephone number is (571)272-3754. The examiner can normally be reached 9:30am-6:00pm, M-F.
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/Sean Shechtman/ Primary Examiner, Art Unit 2896
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