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Overlay control is gaining more attention in recent years as technology moves into the 32nm era. Strict overlay requirements are being driven not only by the process node but also the process techniques required to meet the design requirements. Double patterning lithography and spacer pitch splitting techniques are driving innovative thinking with respect to overlay control. As lithographers push the current capabilities of their 193nm immersion exposure tools they are utilizing newly enabled control 'knobs'. 'Knobs' are defined as the adjustment points that add new degrees of freedom for lithographers to control the scanner. Expanded control is required as current scanner capabilities are at best marginal in meeting the performance requirements to support the ever demanding process nodes. This abstract is an extension of the SPIE 2008 paper in which we performed thorough sources of variance analysis to provide insight as to the benefits of utilizing high order scanner control knobs [1]. The extension this year is to expand the modeling strategies and to validate the benefit through carefully designed experiments. The expanded modeling characterization will explore not only high order correction capabilities but also characterize the use of field by field corrections as a means to improve the overlay performance of the latest generation of immersion lithography tools. We will explore various correction strategies for both grid and field modeling using KT Analyzer™. ASSUMPTIONS OF TRADITIONAL OVERLAY CONTROLA standard assumption of traditional overlay control strategy is that most overlay error is caused by well-known, relatively simple (linear) misbehaviors in some of the exposure equipment subsystems. This assumption, examined in more detail, yields some of the following precepts of traditional overlay control strategy:Overlay error is highly systematic, so it is not necessary to measure overlay for every field; it is only necessary to measure a relatively small subset of fields across the wafer, and then fit the data with a model that describes the behavior of the exposure equipment subsystems involved in overlay performance. For example, a sample plan of nine fields is enough to fit a linear grid model. Four targets per field are enough to characterize the rectilinear shape of the field.The subsystems involved behave (or misbehave) in linear fashion, so the traditional models are linear. Linear feedback correction is sufficient to minimize the systematic error.The subsystems involved in systematic grid error (wafer stepping stage) are independent of the subsystems involved in systematic field error (reticle stage and lens). Therefore, the traditional models have independent terms for grid and field behavior.The exposure equipment has only one reticle stage and one lens, and these components are very stable over the short term. Therefore, even though the overlay data set contains measurements from many fields, all the fields are modeled together in "composite field" analysis, to provide one set of feedback corr...