Variable domain transformation for linear PAC analysis of mixed-signal systems

This paper describes a method to perform linear AC analysis on mixed-signal systems which appear strongly nonlinear in the voltage domain but are linear in other variable domains. Common circuits like phase/delay-locked loops and duty-cycle correctors fall into this category, since they are designed to be linear with respect to phases, delays, and duty-cycles of the input and output clocks, respectively. The method uses variable domain translators to change the variables to which the AC perturbation is applied and from which the AC response is measured. By utilizing the efficient periodic AC (PAC) analysis available in commercial RF simulators, the circuit’s linear transfer function in the desired variable domain can be characterized without relying on extensive transient simulations. Furthermore, the variable domain translators enable the circuits to be macromodeled as weakly-nonlinear systems in the chosen domain and then converted to voltage-domain models, instead of being modeled as strongly-nonlinear systems directly

Hybrid Verification for Analog and Mixed-signal Circuits

With increasing design complexity and reliability requirements, analog and mixedsignal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machinelearning- based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time