Nonlinear Electronic/Photonic Component Modeling Using Adjoint State-Space Dynamic Neural Network Technique

作     者：Sadrossadat, Sayed Alireza Gunupudi, Pavan Zhang, Qi-Jun 

作者机构：Carleton Univ Dept Elect Ottawa ON K1S 5B6 Canada 

出 版 物：《IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY》 (IEEE Trans. Compon. Packag. Manufact. Tech.)

年 卷 期：2015年第5卷第11期

页      面：1679-1693页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 

基　　金：National Sciences and Engineering Research Council  Canada 

主　　题：Microelectronic circuit modeling neural networks nonlinear behavioral modeling parallel programming photonic device modeling sensitivity analysis transient analysis 

摘      要：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.