Abstract
Application of feedforward neural networks for integrated circuit (IC) modeling is presented. In order to accurately describe IC behaviors, a set of improved equations for dynamic feedforward neural networks has been utilized for IC modeling. The rationality of the improved equations is elucidated by analyzing the relation between the circuits and the equation parameters. Through some special choices of the neuron nonlinearity function, the feed- forward neural networks can themselves be represented by equivalent circuits, which enables the direct use of neural models in existing analogue circuit simulators. Feedforward neural network models for some static and dynamic systems are obtained and compared. Simulated results are included to illustrate the accuracy of the neural networks in circuit modeling.
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Chen, X., Wang, GF., Zhou, W., Zhang, QL., Xu, JF. (2006). Application of Neural Networks for Integrated Circuit Modeling. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_190
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DOI: https://doi.org/10.1007/11760191_190
Publisher Name: Springer, Berlin, Heidelberg
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