Measurement recipe optimization based on probabilistic domain knowledge and physical implementation

Abstract

Methods and systems for training and implementing a metrology recipe based on performance metrics are employed to quantitatively characterize measurement performance of a metrology system in a particular measurement application. Performance metrics are employed to normalize the optimization process employed during measurement model training, model-based regression, or both.

Description

[0001] Cross-references to related applications [0002] This patent application claims under 35 U.S.C. § 119, filed December 2, 2019, No. 62 / 942,730, entitled "Metrology System Utilizing Probabilistic Domain Knowledge and Physical Realization" Priority to the US Provisional Patent Application, the subject matter of which is hereby incorporated by reference in its entirety. technical field [0003] The embodiments relate to metrology systems and methods, and more particularly, to methods and systems for improved measurement of semiconductor structures. Background technique [0004] Semiconductor devices, such as logic and memory devices, are typically fabricated by a series of processing steps applied to a sample. Various features and structural levels of semiconductor devices are formed from these processing steps. For example, lithography, among other things, involves a semiconductor fabrication process that creates patterns on semiconductor wafers. Additional examples...

AI Technical Summary

Problems solved by technology

Again, this results in suboptimal measurement performance

[0018] Metrology challenges for future metrology applications due to ever smaller resolution requirements, multi-parameter dependencies, increasingly complex geometries and increasing use of opaque materials

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