An algorithm of training sample selection for integrated circuit device modeling based on artificial neural networks

In this paper, we propose a training sample selection algorithm for artificial neural networks device modeling to reduce training consuming time and data test cost. The proposed algorithm can be used to get the appropriate size of the training samples under the required training accuracy. Comparison between neural networks model and test data agrees well in a given training accuracy and proves correctness of the algorithm.