Photolithography for the formerly "non-critical" implant blocking layers is becoming more challenging as edge placement control budgets for junction definition shrink with each node. In addition to the traditional proximity effects associated with the implant layer mask, the underlying active and gate layers can interact through a variety of mechanisms to influence the edge placement of the developed implant layer. These mechanisms include bulk reflectivity differences, resist thickness thin film interference effects, reflective notching from pattern sidewalls, reflections from curved surfaces, focus differences, and more. While the use of organic developable bottom antireflection coating (dBARC) can be effective in minimizing these influences, it does represent an added complexity and cost, and processes are still relatively immature. Without such a dBARC, the CD variation due to underlying layers can easily exceed 50 nm, or more than 25% of the target dimension. We propose here a framework for modeling and correcting for these underlayer effects. The approach is based upon calibration of an optical model representing only implant mask proximity effects and two additional optical models which represent the effects of the underlayer topography. Such an approach can be effective in delivering much improved CD control for complex layouts, and represents only a small impact to full-chip correction runtime.

The 22nm node will be patterned with very challenging Resolution Enhancement Techniques (RETs) such as double exposure or double patterning. Even with those extreme RETs, the k1 factor is expected to be less than 0.3. There is some concern in the industry that traditional edge-based simulate-then-move Optical Proximity Correction (OPC) may not be up to the challenges expected at the 22nm node. Previous work presented the advantages of a so-called inverse OPC approach when coupled with extreme RETs or illumination schemes. The smooth mask contours resulting from inverse corrections were shown not to be limited by topological identity, feedback locality, or fragment conformity. In short, inverse OPC can produce practically unconstrained and often non-intuitive mask shapes. The authors will expand this comparison between traditional and inverse OPC to include likely 22nm RETs such as double dipole lithography and double patterning, comparing dimensional control through process window for each OPC method. The impact of mask simplification of the inverse OPC shapes into shapes which can be reliably manufactured will also be explored.

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