EngineeringComputational EngineeringElectrical EngineeringCeramicsMicroelectronicsStatistical ModelingDevice ModelingElectrical Properties Of CeramicsIntegrated Circuit DesignInterconnect DesignInterconnect TechnologySupport Vector MachinesInterconnect ModelingStructural Risk MinimizationSupport Vector RegressionSupport Vector Regression Surrogates
In this paper, we introduce a new method: support vector regression (SVR) method to modeling low temperature co-fired ceramic (LTCC) multilayer interconnect. SVR bases on structural risk minimization (SRM) principle, which leads to good generalization ability. A LTCC based stripline-to-stripline interconnect used as example to verify the proposed method. Experiment results show that the developed SVR model perform a good predictive ability in analyzing the electrical performance.
7
Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations
Bernhard Schölkopf, Alex Smola, Robert C. Williamson et al. · Neural Computation · 2000 · 2.8K citations
Improvements to the SMO algorithm for SVM regression
Shirish Shevade, S. Sathiya Keerthi, Chiranjib Bhattacharyya et al. · IEEE Transactions on Neural Networks · 2000 · 866 citations
KERNELS EVALUATION OF SVM-BASED ESTIMATORS FOR INVERSE SCATTERING PROBLEMS
E. Bermani, Andrea Boni, Aliaksei Kerhet et al. · Electromagnetic waves · 2005 · 43 citations · Full text