Asml Netherlands B.v.

Google Inc.

Yi-Fan Chen

Duan-Fu Stephen Hsu

Luoqi Chen

Hanying Feng

Rafael C Howell

Xinjian Zhou

Jay Yagnik

Ryan Michael Rifkin

Rasmus Munk Larsen

Sarvjeet Singh

Anandsudhakar Kesari

John Roberts Anderson

Embodiments of the present invention provide methods for optimizing a lithographic projection apparatus including optimizing projection optics therein. The current embodiments include several flows including optimizing a source, a mask, and the projection optics and various sequential and iterative optimization steps combining any of the projection optics, mask and source. The projection optics is sometimes broadly referred to as 'lens', and therefore the optimization process may be termed source mask lens optimization (SMLO). SMLO may be desirable over existing source mask optimization process (SMO) or other optimization processes that do not include projection optics optimization, partially because including the projection optics in the optimization may lead to a larger process window by introducing a plurality of adjustable characteristics of the projection optics. The projection optics may be used to shape wavefront in the lithographic projection apparatus, enabling aberration control of the overall imaging process.

Optimization flows of source, mask and projection optics

Implementations include systems and methods for scoring candidates for set recommendation problems. An example method includes repeating, for each code in code arrays for items in a set of items, determining a most common value for the code. In some implementations, the method includes determining that the most common value occurs with a frequency that meets an occurrence threshold and adding the code and the most common value to set-inclusion criteria. In other implementations, the method includes determining a value for the code from a code array for a seed item and adding the code and the most common value to set-inclusion criteria when the value for the code from the code array for the seed item matches the most common value. The method may also include evaluating a similarity with a candidate item based on the set-inclusion criteria and basing a recommendation regarding the candidate item on the similarity.

Scoring candidates for set recommendation problems

Growing community of inventors

Sunnyvale, CA, United States of America

Yi-Fan Chen