Automated Transient Input Stimuli Generation for Analog Circuits

Abstract

We present an automated directed random input stimulus generation algorithm with high coverage for nonlinear analog circuits. Our methodology is able to generate input stimuli to meet two kinds of objectives: 1) to reach user-defined goal regions and 2) increased coverage of state space. The principal benefit of our approach is that it can provide directed input stimulus generation, as opposed to the randomly generated input stimulus by Monte Carlo-based methods. The methodology introduces multiobjective rapidly-exploring random trees (MORRTs), which add a bias and a feedback loop to the standard rapidly-exploring random trees algorithm. The biasing is provided by a statistical inference algorithm. Simultaneous biasing toward goal regions and coverage is possible in MORRT to a user-defined extent. Our methodology generates several input stimuli that are concentrated in the goals or relevant operating regions, while providing high coverage of the state space. We demonstrate the efficiency and scalability of our approach on high-dimensional analog case studies.