KLA Corporation
PREDICTION AND METROLOGY OF STOCHASTIC PHOTORESIST THICKNESS DEFECTS
Last updated:
Abstract:
A mask pattern for a semiconductor device can be used as an input to determine a photoresist thickness probability distribution using a machine learning module. For example, the machine learning module can determine a probability map of Z-height. This can be used to determine stochastic variation in photoresist thickness for a semiconductor device. The Z-height may be calculated at a coordinate in the X-direction and Y-direction.
Status:
Application
Type:
Utility
Filling date:
2 Jun 2021
Issue date:
28 Apr 2022