2023/11/14 by Christos Giamouzis, Giamouzis, Christos, Dimitrios Garyfallou +5
Physics and Astronomy · Engineering · Materials Science · #Model Reduction and Neural Networks #Electromagnetic Simulation and Numerical Methods #Magnetic Properties and Applications
paper · pdf · doi:10.48550/arxiv.2311.08478
Model order reduction (MOR) is an important step in the design process of integrated circuits. Specifically, the electromagnetic models extracted from modern complex designs result in a large number of passive elements that introduce limitations in the simulation process. MOR techniques based on balanced truncation (BT) can overcome these limitations by producing compact reduced-order models (ROMs) that approximate the behavior of the original models at the input/output ports. In this paper, we present a low-rank BT method that exploits the extended Krylov subspace and efficient implementation techniques for the reduction of large-scale models. Experimental evaluation on a diverse set of analog and mixed-signal circuits with millions of elements indicates that up to x5.5 smaller ROMs can be produced with similar accuracy to ANSYS RaptorX ROMs.