US 11,790,515 B2
Detecting defects in semiconductor specimens using weak labeling
Irad Peleg, Tel Aviv (IL); Ran Schleyen, Rehovot (IL); and Boaz Cohen, Lehavim (IL)
Assigned to Applied Materials Israel Ltd., Rehovot (IL)
Filed by Applied Materials Israel Ltd., Rehovot (IL)
Filed on May 23, 2022, as Appl. No. 17/751,507.
Application 17/751,507 is a continuation of application No. 16/892,139, filed on Jun. 3, 2020, granted, now 11,379,972.
Prior Publication US 2022/0301151 A1, Sep. 22, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 20/69 (2022.01); G01N 21/95 (2006.01); G06N 3/06 (2006.01)
CPC G06T 7/0004 (2013.01) [G01N 21/9501 (2013.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/06 (2013.01); G06T 7/0008 (2013.01); G06T 7/11 (2017.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G06T 2207/10061 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for classifying a pattern of interest (POI) on a semiconductor specimen, the system comprising a processor and memory circuitry (PMC) configured to:
obtain data informative of a high-resolution image of the POI on the specimen; and
generate data usable for classifying the POI in accordance with a defectiveness-related classification,
wherein the generating utilizes a machine learning model that has been trained with, at least, a plurality of training samples, each training sample obtained by:
capturing a high-resolution training image by scanning, with a high-resolution examination tool, a respective training pattern on a specimen, the respective training pattern being similar to the POI, and
associating a label with the high-resolution training image, the label being derivative of low-resolution inspection of the respective training pattern.