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Pin accessibility encounters nontrivial challenges due to the smaller number of routing tracks, higher pin density, and more complex design rules. Consequently, securing design rule-correct routability has become a critical bottleneck for sub-10-nm IC designs (particularly in the detailed routing stage) costing days of runtime. To reduce turnaround time, IC designers demand new design methodologies to analyze the routing feasibility of a given layout architecture (e.g., conditional design rules, pin assignment patterns, etc). There are several conventional methods capable of assessing routability that consider pin accessibility. However, precise diagnosis of unroutable layouts remains an open problem for IC design practitioners. In this article, we propose two novel frameworks that: 1) efficiently analyzes design rule-correct routability via an integer linear programming (ILP)-derived Boolean satisfiability (SAT) formulation written in light-weight conjunctive normal form, on top of multicommodity flow theory and 2) precisely diagnose explicit reasons for design-rule violations (DRVs) in the form of human-interpretable explanations, while specifying conflicting design rules with a physical location. While covering a variety of conditional design rules, we have refined our formulation by using SAT encoding techniques, supernode simplification, Boolean constraint propagation-based preprocessing, etc. We demonstrate that our routability analysis framework produces design rule-correct routability assessment within 0.02% of ILP runtime on average. Also, our routability diagnosis framework precisely examines DRVs, revealing design-rule conflicts for a variety of pin layouts and switchboxes. We show our frameworks scalability by utilizing practical benchmarks ranging up to 40 000 grid-size layouts (i.e., 200 Htrack $\times $ 200 Vtrack), producing results within an hour.

We propose a stability preserved Arnoldi algorithm for matrix exponential in the time domain simulation of large-scale power delivery networks (PDNs), which are formulated as semi-explicit differential-algebraic equations (DAEs). The matrix exponential and vector products (MEVPs) compose the solution of DAEs in multistep integration methods and can be efficiently approximated with the rational Krylov subspace. To produce stable simulation results for the ill-conditioned system from semi-explicit DAEs, the revised Arnoldi algorithm introduces a new structured orthogonalization process to construct the Krylov subspace. We demonstrate the performance of the new algorithm with theoretical proof and experiments. In the computation of MEVPs, we utilize the exponential related $\varphi $ functions to improve the numerical accuracy. We further explore the optimal ratio to confine the spectrum in the rational Krylov subspace. Finally, the transient framework is tested on a group of system-level PDNs, showing that matrix exponential-based algorithms could achieve high efficiency and accuracy.