US 12,444,174 B2
Rare event training data sets for robust training of semiconductor yield related components
Jan Lauber, San Francisco, CA (US); Sabyasachi Das, Hsinchu (TW); and Jason Kirkwood, Mountain View, CA (US)
Assigned to KLA Corporation, Milpitas, CA (US)
Filed by KLA Corporation, Milpitas, CA (US)
Filed on Nov. 6, 2023, as Appl. No. 18/502,942.
Claims priority of provisional application 63/532,910, filed on Aug. 16, 2023.
Prior Publication US 2025/0061692 A1, Feb. 20, 2025
Int. Cl. G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/774 (2022.01) [G06T 7/0004 (2013.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 20/70 (2022.01); G06T 2207/10061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30148 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A system configured for determining information for a specimen, comprising:
one or more computer systems configured for:
collecting images of known rare defect types previously detected on one or more other specimens;
assigning training labels to the images responsive to the known rare defect types in the images; and
storing the collected images and the assigned training labels as a rare defect type training data set, wherein the rare defect type training data set is unsuitable for use in training a component configured for determining information for the specimen from runtime images generated for the specimen by an imaging system until the rare defect type training data set is combined with training images and corresponding training labels generated for the specimen.