US 12,092,962 B1
Measurements of structures in presence of signal contaminations
Jingsheng Shi, Singapore (SG); Yiliang Liu, Singapore (SG); Jie Li, San Jose, CA (US); and Pedro Vagos, Chennevieres (FR)
Assigned to Onto Innovation Inc., Wilmington, MA (US)
Filed by Onto Innovation Inc., Wilmington, MA (US)
Filed on Oct. 26, 2023, as Appl. No. 18/495,247.
Int. Cl. G03F 7/00 (2006.01); G01N 21/95 (2006.01); H01L 21/66 (2006.01)
CPC G03F 7/705 (2013.01) [G01N 21/9501 (2013.01); G03F 7/70625 (2013.01); H01L 22/12 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A method for measuring parameters of interest for a target on a sample, comprising:
obtaining metrology data for the target with a metrology device, the metrology data is a mixture of target signals from the target and non-target signals from a non-target area;
fitting the metrology data to a mixed model containing a model for the target and a local gradient of measured signals; and
determining the parameters of interest for the target based on the fit of the metrology data by the mixed model.
 
15. A metrology device configured for measuring parameters of interest for a target on a sample, comprising:
a source configured to generate radiation to be incident on the target on the sample;
at least one detector configured to detect radiation from the target produced in response to the radiation that is incident on the target; and
at least one processor coupled to the at least one detector, wherein the at least one processor is configured to:
obtain metrology data for the target with the at least one detector, the metrology data is a mixture of target signals from the target and non-target signals from a non-target area;
fit the metrology data to a mixed model containing a model for the target and a local gradient of measured signals; and
determine the parameters of interest for the target based on the fit of the metrology data by the mixed model.
 
29. A method for measuring parameters of interest for a target on a sample, comprising:
obtaining mixed metrology data with a metrology device at a plurality of different locations with respect to the target, where the mixed metrology data from each different location is a mixture of target signals from the target and non-target signals from a non-target area;
determining target metrology data using a trained machine learning model based on the mixed metrology data, wherein the target metrology data comprises the target signals from the target without the non-target signals from the non-target area; and
determining the parameters of interest for the target based on the target metrology data.