The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 01, 2020

Filed:

Jun. 07, 2019
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

David Winston, Charlotte, NC (US);

Pravin Kamdar, Austin, TX (US);

Tong Li, Austin, TX (US);

Richard Daniel Kimmel, Poughkeepsie, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/18 (2006.01); G06F 30/367 (2020.01); G06N 5/04 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 17/18 (2013.01); G06F 30/367 (2020.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, can computer program products for optimizing statistical method of computing output yields are provided. Aspects include determining a target criteria of a system for statistical analysis, based on the target criteria, determining a statistical analysis algorithm for the simulation, determining a block size for a plurality of statistical samples of the system for a parallelization of the statistical analysis algorithm, generating the plurality of statistical samples of the system, simulating the plurality of statistical samples of the system to determine one or more output yields, calculating a confidence interval for each of the one or more output yields, wherein the confidence interval comprises a lower bound, comparing the lower bound to a threshold standard deviation of a probability density function, and adjusting the block size for the plurality of statistical samples based on determining that the lower bound is less than the threshold standard deviation.


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