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This paper presents fast and automated electromigration (EM) reliability modeling by using automated modeling generation (AMG) algorithm. The AMG converts human based EM modeling into an automated modeling and simulation process with the help of ANSYS parametric design language (APDL) program. For automating the neural model training process, training-driven adaptive sampling is applied to integrate...
This paper proposes a novel distributed parallel EM modeling technique to speed up the process of neural network modeling for EM structures. Existing techniques for EM modeling usually need to repeatedly change the parameters of microwave devices and drive the EM simulator to obtain sufficient training and testing samples. As the complexity in EM modeling problem increases, traditional techniques...
This paper presents a novel global optimization technique for training microwave neural network models. Unlike existing sequential hybrid algorithms, the proposed technique implements parallel gradient-based local search in particle swarm optimization (PSO). The whole swarm is divided into subswarms for multiple processors. The particle with the lowest error in the subswarm in each processor is chosen...
This paper presents an advanced algorithm for automated model generation (AMG) using neural networks. AMG trains a neural network in a stage-by-stage manner to obtain a neural network of required accuracy with least amount of training data. In each stage, either the number of data or the size of the neural network is adjusted. The novelty of the proposed algorithm is to incorporate efficient interpolation...
In this paper, an advanced Neuro-Space Mapping (SM) modeling technique for nonlinear device modeling is proposed. By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. The novelty of our work is to introduce a Neuro-SM model combining separate mappings for voltage and current...
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