Shrinking wafer overlay budgets raise the importance of careful characterization and control of the contributing components, a trend accelerated by multi-patterning immersion lithography [1]. Traditionally, the mask contribution to wafer overlay has been estimated from measurement of a relatively small number of standard targets. There are a number of studies on test masks and standard targets of the impact of mask registration on wafer overlay [2],[3]. In this paper, we show the value of a more comprehensive characterization of mask registration on a product mask, across a wide range of spatial frequencies and patterns. The mask measurements will be used to obtain an accurate model to predict mask contribution to wafer overlay and correct for it.
Process-induced overlay errors are a growing problem in meeting the ever-tightening overlay requirements for integrated circuit production. While uniform process-induced stress is easily corrected, non-uniform stress across the wafer is much more problematic, often resulting in non-correctable overlay errors. Measurements of the wafer geometry of free, unchucked wafers give a powerful method for characterization of such non-uniform stress. We will describe a Patterned Wafer Geometry (PWG) tool, which uses optical methods to measure the geometry of in-process wafers. PWG data can be related to In-Plane Distortion (IPD) of the wafer through the PIR (Predicted IPD Residual) metric. This paper will explore the relationship between the PIR data and measured overlay data on Engineered Stress Monitor (ESM) wafers containing various designed stress variations. The process used to fabricate ESM wafers is quite versatile and can mimic many different stress variation signatures. For this study, ESM wafers were built with strong across-wafer stress variation and another ESM wafer set was built with strong intrafield stress variation. IPD was extensively characterized in two different ways: using standard overlay error metrology and using PWG metrology. Strong correlation is observed between these two independent sets of data, indicating that the PIR metric is able to clearly see wafer distortions. We have taken another step forward by using PIR data from the PWG tool to correct process-induced overlay error by feedforward to the exposure tool, a novel method that we call PWG-FF. We conclude that appropriate wafer geometry measurements of in-process wafers have strong potential to characterize and reduce process-induced overlay errors.
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