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 .
The amendment filed on September 04, 2024 has been entered. Claims 1-11 and 13-21 are pending in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 13-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Regarding claims 1, 13 and 20, the claimed invention is directed to a process steps of a method/ a non-transient computer program carrier for obtaining a statistical description describing an expected fingerprint component of the spatial distribution and a noise component describing an expected level of measurement noise associated with the parameter of interest; obtaining metrology data related to the parameter of interest; and inferring, by a hardware computer via Bayesian inference, the spatial distribution of the parameter of interest over the at least one substrate or portion thereof, using the statistical description as a prior and the metrology data as an observation. This judicial exception is not integrated into a practical application because the generically recited computer element do not add a meaningful limitation to abstract idea as they amount to simply implementing abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it appears that the claimed steps are implemented by a generic computer processor for implementing of mental process on a computer.
Regarding dependent claims 2-11, 14-19 and 21 fail to cure the deficiency (as set forth above) and are rejected accordingly. Claims 2-11, 14-19 and 21 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to particular technological environment.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11 and 13-21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ypma et al. [US 20180253015 A1, hereafter Ypma].
As per Claims 1, 13 and 20, Ypma teaches a method for determining a spatial distribution of a parameter of interest over at least one substrate or portion thereof, the at least one substrate having been subject to a semiconductor manufacturing process (Para 106), the method comprising:
obtaining a statistical description describing an expected fingerprint component of the spatial distribution and a noise component describing an expected level of measurement noise associated with the parameter of interest (Para 119-120, the apparatus applies automated statistical techniques, and/or facilitates use of manual observation and selection);
obtaining metrology data related to the parameter of interest (Para 120-121); and
inferring, by a hardware computer via Bayesian inference, the spatial distribution of the parameter of interest over the at least one substrate or portion thereof, using the statistical description as a prior and the metrology data as an observation (Para 150, a probabilistic analysis, for example using a Bayesian network).
As per Claim 2, Ypma teaches the method as claimed in claim 1, wherein the parameter of interest is a parameter associated with the semiconductor manufacturing process (Para 9).
As per Claim 3, Ypma teaches the method as claimed in claim 1, wherein the expected fingerprint component comprises a plurality of shape components of the spatial distribution of the parameter of interest (Para 97-98).
As per Claim 4, Ypma teaches the method as claimed in claim 3, wherein the inferring comprises simultaneously fitting each shape component out of the plurality of shape components to the metrology data (Para 162).
As per Claim 5, Ypma teaches the method as claimed in claim 1, wherein the metrology data comprises a plurality of metrology datasets, each metrology dataset relating to a respective substrate of a plurality of substrates, and the inferring comprises fitting the expected fingerprint component to each metrology dataset simultaneously (Para 112, wherein component vectors may be expected to have a fairly direct relationship with physical effects in the manufacturing process).
As per Claims 6 and 16, Ypma teaches the method as claimed in claim 3, wherein some or each shape component out of the plurality of shape components are expected to be smooth according to the statistical description (Para 198, wherein spatial and/or temporal smoothing may be applied to reduce noise in the measurements).
As per Claims 7 and 17, Ypma teaches the method as claimed in claim 3, wherein some or each shape component out of the plurality of shape component is expected to have a low bending energy, divergence or curl (Para 116, wherein the drawing indicates a Gaussian distribution curve that has been fitted to the data).
As per Claims 8 and 18, Ypma teaches the method as claimed in claim 3, wherein the inferring comprises fitting one or more shape components out of the plurality of shape components to the metrology data so as to minimize bending energy, divergence or curl for each shape component expected to be smooth (Para 116, wherein the drawing indicates a Gaussian distribution curve that has been fitted to the data).
As per Claims 9 and 19, Ypma teaches the method according to claim 3, wherein the inferring comprises fitting one or more shape components out of the plurality of shape components to the metrology data such that the expected level of measurement noise is minimized (Para 116).
As per Claim 10, Ypma teaches the method as claimed in claim 3, wherein the semiconductor manufacturing process is a lithographic process and the plurality of shape components comprise one or more selected from: an interfield shape, an intrafield shape, a slit fingerprint, a scan-up scan-down shape, a step-left step-right shape, an edge roll-off shape that depends only on radius and/or a shape per exposure field (Para 113).
As per Claim 11, Ypma teaches the method as claimed in claim 1, wherein the metrology data comprises a set of measurements of a parameter other than the parameter of interest and/or relating to a domain other than that of the parameter of interest (Para 83).
As per Claims 14 and 15, Ypma teaches a processing arrangement comprising: the computer program carrier of claim 13; and a processor operable to run the computer program.
As per Claim 21, Ypma teaches a non-transient computer program carrier comprising a computer program therein, the computer program, when executed by a computer system, configured to cause the computer system to at least perform the method of claim 20 (Para 210).
Conclusion
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/MESFIN T ASFAW/ Primary Examiner, Art Unit 2882