Premium
Broadband scalable compact circuit model for on‐chip spiral inductors by neural network
Author(s) -
Han Bo,
Shi Xiaofeng,
Li Jun
Publication year - 2016
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2216
Subject(s) - inductor , scalability , electronic engineering , chip , artificial neural network , space mapping , equivalent circuit , bicmos , computer science , topology (electrical circuits) , engineering , electrical engineering , transistor , telecommunications , artificial intelligence , database , voltage
A scalable model combining the advantages of the compact model and space‐mapping neural network (SMNN) has been presented to characterize radio‐frequency behaviors of on‐chip spiral inductors. The physics‐based T equivalent circuit model has been used for constructing the proposed scalable SMNN model. All values of the T model elements are fast and accurately extracted based on the mathematical formulations derived by analyzing the resonant responses. A 4‐layer perceptron neural network has been applied for the space mapping of the T model and the measurement data. Compared with the conventional models of on‐chip spiral inductors, the proposed SMNN model not only preserves the accuracy of measurement data but also runs as fast as an approximate compact model. The presented SMNN model has been verified by a series of octagon spiral inductors fabricated by 130‐nm BiCMOS process of HHNEC. Excellent agreements are obtained between the measurement and simulation of the proposed SMNN model up to 40 GHz.