Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a novel parameterized interconnect order reduction algorithm, CORE, to efficiently capture both inter-die and intra-die variations. CORE applies a two-step explicit-and-implicit scheme for multiparameter moment matching. As such, CORE can match significantly more moments than other traditional techniques using the same model size. In addition, a recursive Arnoldi algorithm is proposed to quickly construct the Krylov subspace that is required for parameterized order reduction. Applying the recursive Arnoldi algorithm significantly reduces the computation cost for model generation. Several RC and RLC interconnect examples demonstrate that CORE can provide up to 10/spl times/ better modeling accuracy than other traditional techniques, while achieving smaller model complexity (i.e. size). It follows that these interconnect models generated by CORE can provide more accurate simulation result with cheaper simulation cost, when they are utilized for gate-interconnect co-simulation.
['Xin Li', 'Peng Li', 'Lawrence T. Pileggi']
I am writing a research paper on a idea Parameterized interconnect order reduction with explicit-and-implicit multi-parameter moment matching for inter/intra-die variations
[{"from": "human", "value": "I am writing a research paper on a idea Parameterized interconnect order reduction with explicit-and-implicit multi-parameter moment matching for inter/intra-die variations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a novel parameterized interconnect order reduction algorithm, CORE, to efficiently capture both inter-die and intra-die variations. CORE applies a two-step explicit-and-implicit scheme for multiparameter moment matching. As such, CORE can match significantly more moments than other traditional techniques using the same model size. In addition, a recursive Arnoldi algorithm is proposed to quickly construct the Krylov subspace that is required for parameterized order reduction. Applying the recursive Arnoldi algorithm significantly reduces the computation cost for model generation. Several RC and RLC interconnect examples demonstrate that CORE can provide up to 10/spl times/ better modeling accuracy than other traditional techniques, while achieving smaller model complexity (i.e. size). It follows that these interconnect models generated by CORE can provide more accurate simulation result with cheaper simulation cost, when they are utilized for gate-interconnect co-simulation."}]