Robust and Efficient Uncertainty Quantification and Validation of RFIC Isolation

Modern communication and identification products impose demanding constraints on reliability of components. Due to this statistical constraints more and more enter optimization formulations of electronic products. Yield constraints often require efficient sampling techniques to obtain uncertainty quantification also at the tails of the distributions. These sampling techniques should outperform standard Monte Carlo techniques, since these latter ones are normally not efficient enough to deal with tail probabilities. One such a technique, Importance Sampling, has successfully been applied to optimize Static Random Access Memories (SRAMs) while guaranteeing very small failure probabilities, even going beyond 6-sigma variations of parameters involved. Apart from this, emerging uncertainty quantifications techniques offer expansions of the solution that serve as a response surface facility when doing statistics and optimization. To efficiently derive the coefficients in the expansions one either has to solve a large number of problems or a huge combined problem. Here parameterized Model Order Reduction (MOR) techniques can be used to reduce the work load. To also reduce the amount of parameters we identify those that only affect the variance in a minor way. These parameters can simply be set to a fixed value. The remaining parameters can be viewed as dominant. Preservation of the variation also allows to make statements about the approximation accuracy obtained by the parameter-reduced problem. This is illustrated on an RLC circuit. Additionally, the MOR technique used should not affect the variance significantly. Finally we consider a methodology for reliable RFIC isolation using floor-plan modeling and isolation grounding. Simulations show good comparison with measurements

ICESTARS : integrated circuit/EM simulation and design technologies for advanced radio systems-on-chip

ICESTARS solved a series of critical issues in the currently available infrastructure for the design and simulation of new and highly-complex Radio Frequency (RF) front ends operating beyond 10 and up to 100 GHz. Future RF designs demand an increasing blend of analog and digital functionalities. The super and extremely high frequency (SHF, 3-30GHz, and EHF, 30-300GHz) ranges will be used to accomplish future demands for higher capacity channels. With todays frequency bands of approximately 1 to 3 GHz it is impossible to realize extremely high data transfer rates. Only a new generation of CAD and EDA tools will ensure the realization of complex nanoscale designs. It necessitates both new modeling approaches and new mathematical solution procedures for differential equations with largely differing time scales, analysis of coupled systems of DAEs (circuit equations) and PDEs (Maxwell equations for electromagnetic couplings) plus numerical simulations with mixed analog and digital signals. In ICESTARS new techniques and mathematical models working in highly integrated environments were developed to resolve this dilemma. The ICESTARS research area covered the three domains of RF design: (1) time-domain techniques, (2) frequency-domain techniques, and (3) EM analysis and coupled EM circuit analysis. The ICESTARS consortium comprised two industrial partners (NXP Semiconductors, Infineon Technologies AG), two SMEs (Magwel, AWR-APLAC) and five universities (Upper Austria, Cologne, Oulu, Wuppertal, Aalto), involving mathematicians, electronic engineers, and software engineers

Nonlinear model order reduction based on trajectory piecewise linear approach: Comparing different linear cores

Refined models for MOS-devices and increasing complexity of circuit designs cause the need for Model Order Reduction (MOR) techniques that are capable of treating nonlinear problems. In time-domain simulation the Trajectory PieceWise Linear (TPWL) approach is promising as it is designed to use MOR methodologies for linear problems as the core of the reduction process. We compare different linear approaches with respect to their performance when used as kernel for TPWL

Model order reduction for semi-explicit systems of differential algebraic equations

Increasing complexity of mathematical models demands techniques of model order reduction (MOR) that enable an efficient numerical simulation. For example, network approaches yield systems of differential algebraic equations (DAEs) in electric circuit simulation. Thereby, miniaturization and increasing packing densities result in extremely large DAE systems. MOR methods are well developed for linear systems of ordinary differential equations (ODEs), whereas the nonlinear case represents still an open field of research. We consider MOR for semi-explicit systems of DAEs. Techniques for ODEs can be generalized to semi-explicit DAEs by a direct or an indirect approach. In the direct approach, we introduce artificial parameters in the system. Accordingly, MOR methods for ODEs can be applied to the regularized system. Numerical simulations using the constructed MOR strategies are presented

Nonlinear model order reduction based on trajectory piecewise linear approach: Comparing different linear cores

Refined models for MOS-devices and increasing complexity of circuit designs cause the need for Model Order Reduction (MOR) techniques that are capable of treating nonlinear problems. In time-domain simulation the Trajectory PieceWise Linear (TPWL) approach is promising as it is designed to use MOR methodologies for linear problems as the core of the reduction process. We compare different linear approaches with respect to their performance when used as kernel for TPWL