High-level modeling for operational amplifiers (opamps) has been previously carried out successfully using models generated by published automated model generation approaches. Furthermore, high-level fault modeling (HLFM) has been shown to work reasonably well using manually designed fault models. However, no evidence shows that published automated model generation approaches based on opamps have been used in HLFM. This paper describes HLFM for analog circuits using an adaptive self-tuning algorithm called multiple model generation system using delta. The generation algorithms and simulation models were written in MATLAB and the hardware description language VHDL-AMS, respectively. The properties of these self-tuning algorithms were investigated by modeling complementary metal-oxide-semiconductor opamps, and comparing simulations using the HLFM against those of the original simulation program with integrated circuit emphasis circuit utilizing transient analysis. Results show that the models can handle both linear and nonlinear fault situations with better accuracy than previously published HLFMs.

Automatic approaches for macromodel generation from SPICE-level descriptions have been of great interest over the last few years for the design of large, complex mix- signal SoCs (system-on-chips) and SiPs (system-in-packages). In this paper a novel approach termed multiple model generation system (MMGS) is developed for extracting either single-input single-output (SISO) or multiple-input single-output (MISO) macromodels from a SPICE netlist. This model generation process detects nonlinearity through variations in output error. Examples of the application of MMGS are presented for simple two-input systems incorporating a two-stage CMOS operational amplifier (op amp).