Kla Corporation

Xiaohan Li

Pradeep Vukkadala

John S Graves

Guy Parsey

Anatoly Burov

Kunlun Bai

Cao Zhang

John Joseph Biafore

Craig Higgins

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.

Prediction and metrology of stochastic photoresist thickness defects

A system for mask design repair may develop a simulation-based model of a layer thickness after one or more process steps for fabricating features on a sample, develop a transformed model of the fabrication process that emulates the simulation-based model and has a faster evaluation speed than the simulation-based model, and where the inputs to the transformed model include the input mask design, and where the outputs of the transformed model include one or more output parameters associated with fabrication of the input mask design as well as one or more sensitivity metrics describing sensitivities of the one or more output parameters to variations of the input mask design. The system may further receive a candidate mask design and generate a repaired mask design based on the transformed model and the candidate mask design.

Lithography mask repair by simulation of photoresist thickness evolution

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Ann Arbor, MI, United States of America

Xiaohan Li