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The classic Bossung Curve analysis is the most commonly applied tool of the lithographer. The analysis maps a control surface for critical dimensions (CD's) as a function of the variables of focus and exposure (dose). Most commonly the technique is used to calculate the optimum focus and dose process point that yields the greatest depth-of-focus (DoF) over a tolerable range of exposure latitude. Recent ITRS roadmaps have cited the need to control CD's to less than 4 nm Across-Chip-Linewidth-Variation (ACLV). A closely related requirement to ACLV is the need to properly evaluate the implementation of Optical Proximity Correction (OPC) in the final resist image on the wafer. Calculation of ACLV and the process points are typically addressed with the use of theoretical simulator evaluations of the actinic wavefront and the photoresist's interactions. Engineers frequently prefer the clean results of the simulation over the more cumbersome and less understood perturbations seen in the empirical metrology data resulting in a loss of valuable process control information. Complexity increases when the analysis assumes a super-positioning of the responses of multiple feature-types in the search for an overlapping process window. Until recently, simulations rarely validated design response to the process and never incorporated the characteristics of the exposure tool and reticle. Fortunately empirical Bossung curve calculations can supply valuable tool, process and reticle specific interaction information if the techniques are expanded through the use of spatial and temporal perturbation models of the actinic image wavefront. In this implementation the classic focus-exposure matrix is shown to be a powerful tool for the determination of optimum focus and focus uniformity across the full exposure field. Although not the tool of choice for pupil aberration analysis, the method is the best implementation for determining the behavior of device critical feature response when the constructs of OPC, forbidden-pitch and inherent reticle variability are involved. Improved process performance can be achieved with algorithms that provide a calculation of the optimum focus ridge whose resulting feature response-to-dose curves are more easily traced to simulation. Response surface models are presented and applied to a calculation of the Best Focus surface for the exposure field. Unlike specialty reticles used in defocus error, the Bossung curve maps the response of the reticle specific feature or OPC design and can provide information on errors induced by the lens/optomechanical system of the exposure tool. The Bossung curve delivers several additional response surfaces needed for proper qualification of any exposure-tool and reticle set. These include the ability to contour-map the critical Feature-Best-Focus surface response across the exposure field of the reticle that accounts for feature and process design variations, the Depth-of-Focus uniformity surface for each critical feature across the full exposure, an Isofocal ridge analysis of the process and the associated process perturbation response and the effective dose-uniformity response needed to achieve target feature size uniformity across the exposure. The Feature-Best-Focus response surface is critical to any systemic analysis because it is the optimum estimation of the reticle feature uniformity without the perturbations induced by exposure defocus. It is shown that when combined in the analysis these techniques provide improved and quick full-field and process-range feature control limit and tolerance calculation for new designs. The exposure limits thus calculated can then provide a realistic and stable process control set for use in the classic process window analysis. Finally, by deconvolving the systemic reticle signature, the original data provides a feature-specific analysis of Dose-Uniformity. The dose-maps created in this step can be linked to local variations in MEEF and can be used for IntraField Dose Compensation in advanced exposure tools.
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