Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Two highly efficient second-order algorithms for training feedforward networks
IEEE Transactions on Neural Networks
Stochastic degradation modeling and simulation for analog integrated circuits in nanometer CMOS
Proceedings of the Conference on Design, Automation and Test in Europe
Analog Integrated Circuits and Signal Processing
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The paper discusses a technique to perform efficient circuit reliability analysis of large analog and mixed-signal systems. The proposed method includes the impact of both process variations and transistor aging effects. The complexity of large systems is dealt with by partitioning the system into manageable subblocks that are modeled separately. These models are then evaluated to obtain the system specifications. However, highly expensive reliability simulations, combined with nonlinear output behavior and the high dimensionality of the problem is still a very challenging task. Therefore the use of fast function extraction symbolic regression (FFX) is proposed. This allows to capture the high-dimensional nonlinear problem with good accuracy. Also, an active learning sample selection algorithm is introduced to minimize the amount of expensive aging simulations. The algorithm trades of space exploration with function nonlinearity detection and model uncertainty reduction to select optimal model training samples. The simulation method is demonstrated on a 6 bit Flash ADC, designed in a 32nm CMOS technology. Experimental results show a speedup of 360x over existing aging simulators to evaluate 100 Monte-Carlo samples with good accuracy.