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As lithography and other patterning processes become more complex and more non-linear with each generation, the task of physical design rules necessarily increases in complexity also. The goal of the physical design rules is to define the boundary between the physical layout structures which will yield well from those which will not. This is essentially a rule-based pre-silicon guarantee of layout correctness. However the rapid increase in design rule requirement complexity has created logistical problems for both the design and process functions [1]. Therefore, similar to the semiconductor industry's transition from rule-based to model-based optical proximity correction (OPC) due to increased patterning complexity, opportunities for improving physical design restrictions by implementing model-based physical design methods are evident. In this paper we analyze the possible need and applications for model-based physical design restrictions (MBPDR). We first analyze the traditional design rule evolution, development and usage methodologies for semiconductor manufacturers. Next we discuss examples of specific design rule challenges requiring new solution methods in the patterning regime of low K1 lithography and highly complex RET. We then evaluate possible working strategies for MBPDR in the process development and product design flows, including examples of recent model-based pre-silicon verification techniques [2]. Finally we summarize with a proposed flow and key considerations for MBPDR implementation.

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In the context of 65nm logic technology where gate CD control budget requirements are below 5nm, it is mandatory to properly quantify the impact of the 2D effects on the electrical behavior of the transistor [1,2]. This study uses the following sequence to estimate the impact on transistor performance: 1)A lithographic simulation is performed after OPC (Optical Proximity Correction) of active and poly using a calibrated model at best conditions. Some extrapolation of this model can also be used to assess marginalities due to process window (focus, dose, mask errors, and overlay). In our case study, we mainly checked the poly to active misalignment effects. 2)Electrical behavior of the transistor (Ion, Ioff, Vt) is calculated based on a derivative spice model using the simulated image of the gate as an input. In most of the cases Ion analysis, rather than Vt or leakage, gives sufficient information for patterning optimization. We have demonstrated the benefit of this approach with two different examples:-design rule trade-off : we estimated the impact with and without misalignment of critical rules like poly corner to active distance, active corner to poly distance or minimum space between small transistor and big transistor. -Library standard cell debugging: we applied this methodology to the most critical one hundred transistors of our standard cell libraries and calculate Ion behavior with and without misalignment between active and poly. We compared two scanner illumination modes and two OPC versions based on the behavior of the one hundred transistors. We were able to see the benefits of one illumination, and also the improvement in the OPC maturity.

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