Analog Macromodeling using Kernel Methods
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Variational interconnect analysis via PMTBR
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Projection-based approaches for model reduction of weakly nonlinear, time-varying systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Incremental large-scale electrostatic analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
We formulate the mask modeling as a parametric model order reduction problem based on the finite element discretization of the Helmholtz equation. By using a new parametric mesh and a machine learning technique called Kernel Method, we convert the nonlinearly parameterized FEM matrices into affine forms. This allows the application of a well-understood parametric reduction technique to generate compact mask model. Since this model is based on the first principle, it naturally includes diffraction and couplings, important effects that are poorly handled by the existing heuristic mask models. Further more, the new mask model offers the capability to make a smooth trade-off between accuracy and speed.