A new nonlinear transient modelling technique for high-speed integrated circuit applications based on state-space dynamic neural network

作者：Yi Cao, Ran Ding, Q.J. Zhang · 年份：2004 · DOI：10.1109/mwsym.2004.1338875 · 被引用次数：8 · 研究领域：Model Reduction and Neural Networks、Control Systems and Identification、Analog and Mixed-Signal Circuit Design

A new state-space dynamic neural network (SSDNN) method is presented to model the transient behaviours of high-speed nonlinear circuits. The proposed technique extends the existing dynamic neural network (DNN) approach into a more generalized and robust formulation. A training algorithm exploiting the adjoint sensitivity computation is developed to enable SSDNN to efficiently learn from the transient input and output waveform data without relying on the circuit internal details. An exact representation is derived to convert the proposed SSDNN into circuit format such that the trained SSDNN model can be conveniently used in SPICE-like circuit simulators. The validity of the proposed technique is demonstrated through the transient modelling of high-speed driver/receiver circuits.