In this paper, an adjoint state-space dynamic neural network method for modeling nonlinear circuits and components is presented. This method is used for modeling the transient behavior of the nonlinear electronic and photonic components. The proposed technique is an extension of the existing state-space dynamic neural network (SSDNN) technique. The new method simultaneously adds the derivative information to the training patterns of nonlinear components, allowing the training to be done with less data without sacrificing model accuracy, and, consequently, makes training faster and more efficient. In addition, this method has been formulated such that it can be suitable for the parallel computation. The use of derivative information and parallelization makes training using the proposed technique much faster than the SSDNN. In addition, the models created using the proposed method are much faster to evaluate compared with the conventional models present in traditional circuit simulation tools. The validity of the proposed technique is demonstrated through the transient modeling of the physics-based CMOS driver, commercial NXP's 74LVC04A inverting buffer, and nonlinear photonic components.

Additional Metadata
Keywords Microelectronic circuit modeling, neural networks, nonlinear behavioral modeling, parallel programming, photonic device modeling, sensitivity analysis, Transient analysis
Persistent URL dx.doi.org/10.1109/TCPMT.2015.2484284
Journal IEEE Transactions on Components, Packaging and Manufacturing Technology
Citation
Sadrossadat, S.A. (Sayed Alireza), Gunupudi, P, & Zhang, Q.J. (2015). Nonlinear Electronic/Photonic Component Modeling Using Adjoint State-Space Dynamic Neural Network Technique. IEEE Transactions on Components, Packaging and Manufacturing Technology, 5(11), 1679–1693. doi:10.1109/TCPMT.2015.2484284