DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Non-Final office action is in response to application 18/590,646, application filed on 02/28/2024. Claims 1-20 are currently pending in this application.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 04/16/2025 and 09/16/2026, respectively, is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
4. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claim(s) 1, 5-10 and 12-17 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by McConaghy et al. (US PG Pub No. 2009/0083680).
6. With respect to independent claim 1, McConaghy teaches:
constructing a surrogate model representing an impact of a plurality of metrics to a plurality of process parameters (build a surrogate model, para 17; obtain surrogate models, para 40; budling surrogate models, para 40, 43; surrogate model associated with performance metrics and multi-parameter design, para 42-43);
performing a sweep to determine a number of samples in an optimization space including the plurality of process parameters (see sweep of design variables, para 10; process variables and sweep of design variable, para 10; see sweeping each design variable, sampling variables, para 60);
selecting a subset of sample candidates from the surrogate model (see select candidate designs based on sample candidates based on PPA optimization, para 11); and
generating, by a processing device, a PPA model based on the subset of sample candidates to output improved sample sets (see model-building optimization algorithms, para 39; optimization of set of candidate designs to obtain an optimized surrogate model based on MPD criteria for PPA, para 40-41).
7. With respect to independent claim 10, McConaghy teaches:
creating multiple groups in an optimization space, each group including samples of a different process parameter (see storing performance metrics for PPA in a database based on the type/kind of metric, based on certainty/uncertainty of candidate designs, para 96; build surrogate model for each set of metrics, para 97; see ranges for each group of design variables, para 59; see groups of samples, para 73);
selecting dominant samples in each group (see dominant sample, uncertainty is 0, para 27; see values below uncertainty threshold, para 34; see dominant component of optimization, para 35); and
performing co-optimization using the dominant samples from each group (see multi-objective optimization of dominant components, and their samples/values, para 35, 87.
8. With respect to independent claim 15, McConaghy teaches:
generating a power, performance and area (PPA) model using a domain-driven search algorithm (see metrics such performance, area and power, see claim 13 of McConaghy; see search convergence toward optimal solution for surrogate cost function and surrogate model for PPA, para 26);
assessing, using the PPA model, a first metric and a second metric for each of a plurality of process parameters (using surrogate model, determining certainty for performance metrics which can be maximized or minimized based on optimization cost function, Abstract; see multi-parameter design and performance PPA metrics, Abstract);
updating a PPA frontal sample set including a plurality of samples based on first metric data and second metric data (see multi-objective optimizer and front set of points for metrics, good choice values, para 84); and
performing analysis on the PPA frontal sample set to generate a PPA Pareto front (see analysis based on front sample sets for determining optimal PPA values, para 80-85).
9. With respect to claim 5, McConaghy teaches:
The method of claim 1, wherein the sweep involves enumerating all combinations of the plurality of process parameters (see sweep of design variables, see histogram/enumeration of values, para 10-11; see sweeping of each design variable of initial values in addition to obtaining candidate designs, para 60-61).
10. With respect to claim 6, McConaghy teaches:
The method of claim 1, wherein each process parameter involves two runtimes (see optimization runs, para 11; see overall runtimes and maximum runtimes, para 34; see first and second runs, para 91).
11. With respect to claim 7, McConaghy teaches:
The method of claim 1, wherein the subset of sample candidates are displayed in a condensed manner along a curved line (see sample data along curve in graph in Fig 4).
12. With respect to claim 8, McConaghy teaches:
The method of claim 1, wherein the subset of sample candidates exhibit strong statistical correlation between subsets of process parameters of the plurality of process parameters (see correlation of values, parameter parameters, para 35-39).
13. With respect to claim 9, McConaghy teaches:
the method of claim 1, wherein the surrogate model is executed by a domain-driven search algorithm and wherein the domain-driven search algorithm is used on different types of design technology co-optimization (DTCO) flows (see search and cost function, para 80-85; see dominant sample, uncertainty is 0, para 27; see values below uncertainty threshold, para 34; see dominant component of optimization, para 35).
14. With respect to claim 12, McConaghy teaches:
The method of claim 10, wherein the dominant samples are displayed in a condensed manner along a curved line (see sample data along curve in graph in Fig 4).
15. With respect to claim 13, McConaghy teaches:
The method of claim 10, wherein the dominant samples include a few hundred samples (see 10 to 10,000 training samples, 100 samples, para 30).
16. With respect to claim 14, McConaghy teaches:
The method of claim 10, wherein the dominant samples in each group are selected using a domain-driven search algorithm (see search and cost function, para 80-85; see dominant sample, uncertainty is 0, para 27; see values below uncertainty threshold, para 34; see dominant component of optimization, para 35).
17. With respect to claim 16, McConaghy teaches:
The method of claim 15, wherein the first metric is frequency and the second metric is power (see power and gain/frequency as metrics, para 92).
18. With respect to claim 17, McConaghy teaches:
The method of claim 16, further comprising selecting a resolution for the first metric (see para 80-84, 91-95).
Claim Rejections - 35 USC § 103
19. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
20. Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over McConaghy et al. (US PG Pub No. 2009/0083680) in view of Visweswariah et al. (US PG Pub No. 2011/0106497).
21. With respect to claim 2, while McConaghy appears to be silent regarding the limitations of claim 2 below, Visweswariah teaches:
The method of claim 1, wherein the surrogate model is a summation of multiple quadratic models for each process parameter (see quadratic model summation for process parameters, para 49, 54, 87).
It would have been obvious to one of ordinary skill in the art before the time of the invention to have incorporated Visweswariah’s quadratic model summation into the invention of McConaghy for at least the following reason(s): the summation of model parameters of Visweswariah improves the invention of McConaghy by providing a technique which improves yield, power and performance of semiconductor chip design, which would be advantageous and complementary to the PPA techniques of McConaghy.
22. With respect to claim 3, while McConaghy appears to be silent regarding the limitations of claim 3 below, Visweswariah teaches:
The method of claim 2, wherein a mild nonlinearity between PPA gains and the plurality of process parameters enables using the quadratic models (see non-linearity for parameters like chip leakage, power and performance, see para 42).
(For motivation to combine reference, see rejection of claim 2 above).
23. With respect to claim 4, while McConaghy appears to be silent regarding the limitations of claim 4 below, Visweswariah teaches:
The method of claim 2, wherein weak correlation between the plurality of process parameters enables using the quadratic models (at functions which are not accurate, necessary to use quadratic function, para 49).
(For motivation to combine reference, see rejection of claim 2 above).
24. Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over McConaghy et al. (US PG Pub No. 2009/0083680) in view of Crabtree et al. (US PG Pub No. 2026/0278436).
25. With respect to claim 11, while McConaghy appears to be silent regarding the limitations of claim 11 below, Crabtree teaches:
The method of claim 10, wherein a first group of the multiple groups includes samples related to front-end-of-line (FEOL) process changes and a second group of the multiple groups includes samples related to back-end-of-line (BEOL) process changes (see evaluating using BEOL/FEOL to improve chip yield and reduce variability in semiconductor design, para 596).
It would have been obvious to one of ordinary skill in the art before the time of the invention to have incorporated Crabtree’s FEOL/BEOL samples into the invention of McConaghy for at least the following reason(s): Crabtree’s use of BEOL/FEOL improves the chip design of McConaghy by providing a mechanism to reduce variability among chip manufacturing processes and improve chip yield, which would provide an advantage to the chip improvement processes of McConaghy.
Allowable Subject Matter
26. Dependent claim 18 (and claim 19 which depends therefrom) and dependent claim 20 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
27. With respect to claim 18 (and claim 19 which depends therefrom), the prior art made of record fails to teach the combination of steps recited in independent claim 18, including the following particular combination of steps as recited in claim 18, as follows:
wherein the analysis of the PPA frontal sample set includes:
dividing a range of the first metric into multiple bins, each bin size of the multiple bins of the first metric determined by the resolution of the first metric;
assigning the plurality of samples into the multiple bins associated with the first metric; and
updating an optimal sample set in each bin associated with the first metric.
28. With respect to claim 20, the prior art made of record fails to teach the combination of steps recited in independent claim 20, including the following particular combination of steps as recited in claim 20, as follows:
selecting a resolution for the second metric such that the analysis of the PPA frontal sample set includes:
dividing a range of the second metric into multiple bins, each bin size of the multiple bins of the second metric determined by the resolution of the second metric;
assigning the plurality of samples into the multiple bins associated with the second metric;
updating an optimal sample set in each bin associated with the second metric; and collecting each optimal sample set from each bin to create a group of optimal frontal samples.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUCHIN PARIHAR whose telephone number is (703)756-1970. The examiner can normally be reached on M-F 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jack Chiang can be reached on 571-272-7483. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SUCHIN PARIHAR/
Primary Examiner, Art Unit 2851
Prosecution Insights