Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lithographic limitations and manufacturing uncertainties are resulting in fabricated shapes on wafer that are topologically equivalent, but geometrically different from the corresponding drawn shapes. While first-order sensitivity information can measure the change in pattern parasitics when the shape variations are small, there is still a need for a high-order algorithm that can extract parasitic variations incrementally in the presence of a large number of simultaneous shape variations.  proposes such an algorithm based on the ll-known method of floating random walk (FRW). Specifically,  formalize the notion of random path sharing beten several conductors undergoing shape perturbations and use it as a basis of a fast capacitance sensitivity extraction algorithm and a fast incremental variational capacitance extraction algorithm. The efficiency of these algorithms is further improved with a novel FRW method for dealing with layered media. Our numerical examples show a 10X speed up with respect to the boundary-element method adjoint or finite-difference sensitivity extraction, and more than 560X speed up with respect to a non-incremental FRW method for a high-order variational extraction.
['Tarek A. El-Moselhy', 'Ibrahim M. Elfadel', 'Luca Daniel']
I am writing a research paper on a idea A capacitance solver for incremental variation-aware extraction
[{"from": "human", "value": "I am writing a research paper on a idea A capacitance solver for incremental variation-aware extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lithographic limitations and manufacturing uncertainties are resulting in fabricated shapes on wafer that are topologically equivalent, but geometrically different from the corresponding drawn shapes. While first-order sensitivity information can measure the change in pattern parasitics when the shape variations are small, there is still a need for a high-order algorithm that can extract parasitic variations incrementally in the presence of a large number of simultaneous shape variations.  proposes such an algorithm based on the ll-known method of floating random walk (FRW). Specifically,  formalize the notion of random path sharing beten several conductors undergoing shape perturbations and use it as a basis of a fast capacitance sensitivity extraction algorithm and a fast incremental variational capacitance extraction algorithm. The efficiency of these algorithms is further improved with a novel FRW method for dealing with layered media. Our numerical examples show a 10X speed up with respect to the boundary-element method adjoint or finite-difference sensitivity extraction, and more than 560X speed up with respect to a non-incremental FRW method for a high-order variational extraction."}]