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 .
Non-Final Office Action
DETAILED ACTION
Examiner’s Notes
(a) Claim date: 04/28/2023.
(b) Priority date: 10/11/2022.
(c) Invention: Machine learning for yield prediction/optimization using process tools.
Claim Rejections - 35 USC 102
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.Claims 1-3, 5, 9-10, 13-17, 20, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by the prior art of record “DAVID” <US 20170109646 A1>.(As to claim 1, 10, 16 DAVID discloses):
1. A non-transitory computer readable medium encoded with a computer program that
comprises instructions to cause a processor to [Fig. 8, depicting functions that are known to be performed using computer comprising software and processor]:
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compute a set of bin thresholds based on slope changes in an ordered set of discrete
probabilistic classification scores [0086: “(Gradient Boosting Machine) and Random Forests algorithms can produce the best results. Other machine learning algorithms, including the ones mentioned above, can also work well and should be considered”; Note: Claimed limitation “discrete probabilistic classification scores” using the “slope changes” is functionally equivalent to producing the “best results” using “machine learning” produced Score (Fig. 8, 806) as disclosed by the prior art];
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assign the discrete probabilistic classification scores [Fig. 13, Step 1; Note: the Yield_Prediction function is being assigned various scores, refer to the right side of the equation (i.e “weight”, “offset” and etc)] to the bins based on the values of the discrete probabilistic classification scores and the bin thresholds [Fig. 13, Step 3]; and
select processes associated with the discrete probabilistic classification scores of one or
more of the bins based on costs of the respective processes and a global budget [Fig. 8, 806 in conjunction of Fig. 13, Step 2 and 3; Note: Claimed limitation “cost” and “global budget” is functionally equivalent to the condition set by the term “defined_trheshold”].
(As to claim 2, DAVID discloses):
2. The non-transitory computer readable medium of claim 1, wherein:
the dataset comprises a circuit design [0135: “design …die…stacked IC's”];
the processes comprise electronic design automation (EDA) processes [0135, “design”; Note: EDA tools are well known to be used for circuit design]; and
the probabilistic classification scores reflect probabilities that the EDA processes will
detect a defect in the circuit design [0051: “machine learning algorithms can be used to predict when a fault or defect”].
(As to claim 3, DAVID discloses):
3. The non-transitory computer readable medium of claim 1, wherein the computer
program further comprises instructions to cause the processor to:
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smooth the ordered discrete probabilistic classification scores [Fig. 6, 608”]; and
compute the bin thresholds by computing the slope changes in the smoothed ordered
discrete probabilistic classification scores and selecting a subset of the slope
changes as the bin thresholds based on a change-point threshold [Fig. 6, 610].
(As to claim 5, 9, 15, DAVID discloses):
5. The non-transitory computer readable medium of claim 1, wherein the computer
program further comprises instructions to cause the processor to:
select the processes based a decreasing density function [0150: “reduction or feature selection is performed to reduce the number of input parameters for processing the algorithm”].
(As to claim 13, DAVID discloses):
13. The IC device claim 10, wherein the change point detection circuitry comprises:
circuitry configured to compute a discrete second derivative of neighboring points of
the filtered ordered discrete probabilistic classification scores [0086: “(Gradient Boosting Machine)”].
(As to claim 14, DAVID discloses):
14. The IC device of claim 10, further comprising:
selection circuitry configured select processes associated with the discrete probabilistic
classification scores of one or more of the bins based on costs of the respective
processes [Fig. 8, 806].
(As to claim 17, DAVID discloses):
17. The method of claim 16, wherein the selecting a subset of the processes comprises:
selecting the subset of the processes based on a weighted random selection without
replacement function [091: “determining whether or not to employ the score, or weight the use of that prediction”].
(As to claim 20, DAVID discloses):
20. The method of claim 16, further comprising:
merging one of the bins with an adjacent one of the bins based on a minimum bin size
criterion [0141: “A threshold can be set for the confidence value to bin the confidence value as high or low. For example, if the confidence metric varies between 0 and 1, and the threshold is set at 0.5, then confidence values above 0.5 will be deemed as high confidence, while values below 0.5 will be deemed to be low confidence.”].
Allowable Subject Matter
The following claims would be allowable if all rejections/objections cited in this office action (if any) are overcome and rewritten to include all of the limitations of the base claim and any intervening claims.The reason for this allowance is: the claimed subject matter could not have been anticipated or obviated using any prior arts.Allowable claims are: 4, 6-8, 11-12 and 18-19.
Conclusion
The prior art made of record in the form PTO-892 are not relied upon is considered pertinent to applicant's disclosure.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.Contact information:Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED ALAM whose telephone number is (571) 270-1507, email address: [mohammed.alam@uspto.gov] and fax number (571) 270-2507. The examiner can normally be reached on 10AM to 4PM (EST), Monday to Friday. 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./Mohammed Alam/Primary Examiner, Art Unit 2851