A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Improved Strategies for Radial basis Function Methods for Global Optimization
Journal of Global Optimization
Verifying start-up conditions for a ring oscillator
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Stochastic Kriging for Simulation Metamodeling
Operations Research
Global convergence analysis of mixed-signal systems
Proceedings of the 48th Design Automation Conference
Variability-aware, discrete optimization for analog circuits
Proceedings of the 49th Annual Design Automation Conference
Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the International Conference on Computer-Aided Design
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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 can maximize the settling time for the oscillator to reach its final steady state. If any of these initial states visited during the search is found exhibiting false oscillation behaviors for certain variability conditions, the initial state is reported as problematic. On the other hand, if the initial state with the globally maximum settling time is found without discovering such problematic states, the oscillator is reported free of start-up failures. It can be shown that despite the finite number of initial state candidates considered and finite number of Monte-Carlo samples to model variability, the proposed algorithm can verify the oscillator to a prescribed confidence level. Demonstrated on various even-stage differential ring oscillators, the algorithm was able to validate the circuit for 99% yield with 99.9% confidence level by evaluating 7~60 initial states each with 1,000 Monte-Carlo samples. To our knowledge, this is the first algorithm ever reported to address start-up failures with variability.