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Claims 17-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected group, there being no allowable generic or linking claim.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.
Claim1-16 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 in-situ results data" in line 13-14. There is insufficient antecedent basis for this limitation in the claim. Assumed to be “time-series in-situ results data” and assumed that claim 1, limitation e recites “generating the time-series in-situ results data” such that it is clear that “the in-situ results data” recited in line 23 is referencing the same time-series in-situ results data recited in the previous two limitations.
Referring to claim 1, limitation d, it is unclear how the wherein clause regarding in-situ results data further limits the generation of processing system data or how the positioning of the camera further limits the generation of processing system data or how the detection of a camera that is not the signal used to derive in-situ data further limits 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 in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a position at which the polishing fluid is dispensed on the surface of the polishing pad (see "in-situ monitoring system" in par. 12, 16, 45, 52, 75; Applicant argues Yennie fails to teach wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a position at which the polishing fluid is dispensed on the surface of the polishing pad. The examiner disagrees. While the examiner submits it is not clear how the wherein clause further limits the scope of the generation of processing system data in limitation d, 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 the examiner submits the positioning of the camera or the positioning of the camera to detect a radial position does not further limit the scope of the generation of processing system data in limitation d, and does not further limit the scope of the in-situ data or the data derived from the signal from the camera, and therefore reads on wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a position at which the polishing fluid is dispensed on the surface of the polishing pad.); 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 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
changing one or more of the plurality of polishing parameters based on an analysis of the received training data performed by a machine learning AI algorithm (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, changing 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 changing 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 training a machine learning AI algorithm using the training data, and wherein the trained machine learning AI algorithm identifies a functional relationship between the time-series in-situ results data and the time-series data for the first control parameter, and changing one or more of the plurality of polishing parameters includes changing 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 changing 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 2/2/26 have been fully considered but they are not persuasive.
Applicant argues Yennie fails to teach wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a position at which the polishing fluid is dispensed on the surface of the polishing pad. The examiner disagrees. While the examiner submits it is not clear how the wherein clause further limits the scope of the generation of processing system data in limitation d, 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 the examiner submits the positioning of the camera or the positioning of the camera to detect a radial position does not further limit the scope of the generation of processing system data in limitation d, and does not further limit the scope of the in-situ data or the data derived from the signal from the camera, and therefore reads on wherein the in-situ results data comprises: data derived from a signal provided from a camera that is positioned to detect a position at which the polishing fluid is dispensed on the surface of the polishing pad.
Conclusion
Applicant's amendment necessitated any new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Kraig can be reached at 571-272-8660. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sean Shechtman/ Primary Examiner, Art Unit 2896