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Complexity of process steps integration and material systems for next-generation technology nodes is reaching unprecedented levels, the appetite for higher sampling rates is on the rise, while the process window continues to shrink. Current thickness metrology specifications reach as low as 0.1A for total error budget -breathing new life into an old paradigm with lower visibility for past few metrology nodes: accuracy. Furthermore, for advance nodes there is growing demand to measure film thickness and composition on devices/product instead of surrogate planar simpler pads. Here we extend our earlier work in Hybrid Metrology to the combination of X-Ray based reference technologies (high performance) with optical high volume manufacturing (HVM) workhorse metrology (high throughput). Our stated goal is: put more "eyes" on the wafer (higher sampling) and enable move to films on pattern structure (control what matters). Examples of 1X front-end applications are used to setup and validate the benefits.

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In this paper we will demonstrate a systematic approach to significantly improve and sustain a large fleet of thickness metrology tools being used for several advanced nodes and products including 10/7nm at GLOBALFOUNDRIES Fab8 site.This challenge is compounded due to having multiple platforms of tools in the same fleet (heterogeneous tool matching) -Aleris 8350, Aleris 8510 & LD10 Ellipsometry tools. In order to assess the health of this combined fleet, a set of critical inline parameters were identified and patterned wafers were designated for metrology monitoring purposes. The chosen matching parameters covered a range of thickness values, single layer and multi-layer stacks, and measurements that utilized different tool subsystems. These subsystems include BBSE (Broad-Band Spectroscopic Ellipsometry), UVSE (Ultraviolet Spectroscopic Ellipsometry), and SWE (Single Wavelength Ellipsometry). These critical parameter measurements incorporate our most challenging measurements (down to a total measurement matching budget of 0.2 A) and are a representative subset of the thousands of recipes that we run on these tool sets.This methodology provides us with data to help identify the problem tools in our fleet and breaks down the contributions from four key failure modes. This data is rolled up into a weekly summary from which we can schedule work to investigate and improve tool health. Through tracking the improvements in measurement performance from week to week, we are able to demonstrate the effectiveness of this methodology. Additionally we have been able to link these failure modes to specific hardware configurations in order to improve our ability to maintain our fleet.