Kla Corporation

Guy Parsey

Pradeep Vukkadala

John S Graves

Xiaohan Li

Kunlun Bai

Cao Zhang

Anatoly Burov

John Joseph Biafore

Premkumar Panneerchelvam

Chad Huard

Ankur Agarwal

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

Systems and methods for setting up a physics-based model are provided. One system includes one or more components that are executed by one or more computer subsystems and that include a physics-based model describing a semiconductor fabrication-related process and a set up component configured for setting up the physics-based model in multiple phases in each of which only a subset of all of the parameters of the physics-based model are set up. A configuration of the set up component is changed between at least two of the multiple phases based on the subset of all of the parameters of the physics-based model set up in the at least two of the multiple phases. The set up component may perform a Bayesian optimization technique for cascaded model set up or calibration using multiple information sources and objective functions.

Systems and methods for setting up a physics-based model

Growing community of inventors

Ann Arbor, MI, United States of America

Guy Parsey