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Upon the observation of a circuit failure, problematic circuit blocks and parameters need to be localized before they can be fixed or by-passed - this has traditionally been highly manual and problem-specific. In this paper, we present a bug localization methodology that automatically identifies and ranks potential root-causes probabilistically. We model linear and nonlinear sub-circuits using the...
This paper describes a simulation-based approach to establish whether a ring-oscillator always converges to the correct mode of operation regardless of its initial conditions and variability conditions. The verification is performed using a predictive global optimization algorithm that looks for a problematic initial state from a discretized state space. The algorithm explores the initial states that...
This paper describes how the difficult problems of designing verifying and testing analog circuits in presence of variability can be converted to easier ones by discretizing the search spaces or discriminating one case from another. For instance discretizing the continuous design space of analog circuits enables the use of an efficient predictive global circuit optimizer. Also discretizing the initial...
<?Pub Dtl?>Global convergence failures (GCFs), accounting for many of the start-up failures in analog/mixed-signal (AMS) systems, occur when a nonlinear circuit starts from a poorly initialized state. That is, a circuit may or may not converge to the correct mode of operation depending on how it is started upon power-on. This paper proposes a practical procedure to eliminate the uncertainty...
This paper proposes two practical approaches to address global convergence failures, commonly encountered in mixed-signal systems in which analog and digital components closely interact. That is, a nominally-working system may fail intermittently depending on its initial conditions upon start-up. The first approach uses a data clustering analysis and verifies global convergence with randomly-selected...
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