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