iVAMS 1.0: Polynomial-Metamodel-Integrated Intelligent Verilog-AMS for Fast, Accurate Mixed-Signal Design Optimization

Electronic circuit behavioral models built with hardware description/modeling languages such as Verilog-AMS for system-level simulations are typically functional models. They do not capture the physical design (layout) information of the target design. Numerous iterations of post-layout design adjustments are usually required to ensure that design specifications are met with the presence of layout parasitics. In this paper a paradigm shift of the current trend is presented that integrates layout-level information in Verilog-AMS through metamodels such that system-level simulation of a mixed-signal circuit/system is realistic and as accurate as true parasitic netlist simulation. The simulations performed with these parasitic-aware models can be used to estimate system performance without layout iterations. We call this new form of Verilog-AMS as iVAMS (i.e. Intelligent Verilog-AMS). We call this iVAMS 1.0 as it is simple polynomial-metamodel integrated Intelligent Verilog-AMS. As a specific case study, a voltage-controlled oscillator (VCO) Verilog-AMS behavioral model and design flow are proposed to assist fast PLL design space exploration. The PLL simulation employing quadratic metamodels achieves approximately 10X speedup compared to that employing the layout extracted, parasitic netlist. The simulations using this behavioral model attain high accuracy. The observed error for the simulated lock time and average power dissipation are 0.7% and 3%, respectively. This behavioral metamodel approach bridges the gap between layout-accurate but fast simulation and design space exploration. The proposed method also allows much shorter design verification and optimization to meet stringent time-to-market requirements. Compared to the optimization using the layout netlist, the runtime using the behavioral model is reduced by 88.9%.Comment: 25 pages, 13 figure

A hybrid approach to nonlinear macromodel generation for time-varying analog circuits

Modeling frequency-dependent nonlinear characteristics of complex analog blocks and subsystems is critical for enabling efficient verification of mixed-signal system designs. Recent progress has been made for constructing such macromodels, however, their accuracy and/or efficiency can break down for certain problems, particularly those with high-Q filtering. In this paper we explore a novel hybrid approach for generating accurate analog macromodels for time-varying weakly nonlinear circuits. The combined benefits of nonlinear Padé approximations and pruning by exploitation of the system’s internal structure allows us to construct nonlinear circuit models that are accurate for wide input frequency ranges, and thereby capable of modeling systems with sharp frequency selectivity. Such components are widely encountered in analog signal processing and RF applications. The efficacy of the proposed approach is demonstrated by the modeling of large time-varying nonlinear circuits that are commonly found in these application areas. 1