US 12,271,114 B2
Method and apparatus for predicting substrate image
Scott Anderson Middlebrooks, Duizel (NL); Adrianus Cornelis Matheus Koopman, Hilversum (NL); Markus Gerardus Martinus Maria Van Kraaij, Eindhoven (NL); Maxim Pisarenco, Son en Breugel (NL); and Stefan Hunsche, Santa Clara, CA (US)
Assigned to ASML NETHERLANDS B.V., Veldhoven (NL)
Appl. No. 17/441,729
Filed by ASML NETHERLANDS B.V., Veldhoven (NL)
PCT Filed Mar. 26, 2020, PCT No. PCT/EP2020/058488
§ 371(c)(1), (2) Date Sep. 22, 2021,
PCT Pub. No. WO2020/200993, PCT Pub. Date Oct. 8, 2020.
Claims priority of provisional application 62/829,270, filed on Apr. 4, 2019.
Prior Publication US 2022/0187713 A1, Jun. 16, 2022
Int. Cl. G03F 7/00 (2006.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01)
CPC G03F 7/705 (2013.01) [G03F 7/70616 (2013.01); G06N 3/08 (2013.01); G06T 7/0006 (2013.01); G06T 2207/10061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain a training data set comprising (i) metrology data of a metrology tool used to measure a printed pattern of a substrate, and (ii) a representation of a mask pattern employed for imaging the printed pattern on the substrate; and
train, based on the training data set, a machine learning model operable to predict a substrate image of the substrate as measured by the metrology tool based on a cost function, wherein the substrate image corresponds to the printed pattern of the substrate as measured via the metrology tool,
wherein the predicted substrate image that is produced by the machine learning model comprises a segmented image.