Algorithmic macromodelling methods for mixed-signal systems

Electronic systems today, especially those for communications and sensing, are typically composed of a complex mix of digital and mixed-signal circuit blocks. Verifying such systems prior to fabrication is challenging due to their size and complexity. Automated model generation is becoming an increasingly important component of methodologies for effective system verification. In this paper, we review algorithmically-based model generation methods for linear and nonlinear systems. We comment on the development of such macromodelling methods over the last decade, clarify their domains of application and evaluate their strengths and current limitations.