Process Window Qualification (PWQ) is a well-established wafer inspection technique used to qualify the design of mask sets and to characterize lithography process windows. While PWQ typically employs a broadband brightfield inspector, novel techniques for patterned wafer darkfield inspection have proven to provide sufficient sensitivity along with noise suppression benefits for lithography layers. This paper describes the introduction and implementation of PWQ on patterned wafer darkfield inspectors. An initial project characterized critical PWQ requirements on the darkfield inspector. The results showed that this new approach meets performance requirements, such as defect of interest (DOI) detection and process window characterization, as well as ease-of-use requirements such as automated setup for advanced design rule products.
It is a well established fact that as design rules and printed features shrink, sophisticated techniques are required to ensure the design intent is indeed printed on the wafer. Techniques of this kind are Optical Proximity Correction (OPC), Resolution Enhancement Techniques (RET) and DFM Design for Manufacturing (DFM). As these methods are applied to the overall chip and rely on complex modeling and simulations, they increase the risk of creating local areas or layouts with a limiting process window. Hence, it is necessary to verify the manufacturability (sufficient depth of focus) of the overall die and not only of a pre-defined set of metrology structures. The verification process is commonly based on full chip defect density inspection of a Focus Exposure Matrix (FEM) wafer, combined with appropriate post processing of the inspection data. This is necessary to avoid time consuming search for the Defects of Interest (DOI's) as defect counts are usually too high to be handled by manual SEM review. One way to post process defect density data is the so called design based binning (DBB). The Litho Qualification Monitor (LQM) system allows to classify and also to bin defects based on design information. In this paper we will present an efficient way to combine classification and binning in order to check design rules and to determine the marginal features (layout with low depth of focus).The Design Based Binning has been connected to the Yield Management System (YMS) to allow new process monitoring approaches towards Design Based SPC. This could dramatically cut the time to detect systematic defects inline.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.