A new model of on-chip inductors on ferrite film using KB-FDSMN neural network

Abstract

A new model of on-chip planar inductors on ferrite film is developed by virtue of the knowledge-based frequency-dependent space-mapping neural network (KB-FDSMN). A modified p-equivalent circuit is used to construct the KB-FDSMN model for improving reliability in the model generalization. This new model makes use of empirical formulas to quickly estimate some circuit parameters for reducing the number of independent variables, whereas a three-layer neural network is trained for the desirable accuracy and used to compute the rest of circuit parameters. This new approach provides an efficient scheme to model the on-chip magnetic film inductors. In comparison with the conventional neural network model and the standalone modified p-equivalent model, this new KB-FDSMN model can map the input-output relationships with fewer hidden neurons yet better accuracy and higher reliability in the model generalization. V C 2010 Wiley Periodicals, Inc.Int J RF and Microwave CAE 20: 399-407, 2010.

Gain characteristics estimation of heteromorphic RFID antennas using neuro‐space mapping

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“…Inspired by the performance of Neuro‐SM in [27–29], we innovatively propose an estimation framework based on Neuro‐SM for antenna gain, as shown in Fig. 3.…”

Design of an on-chip Hilbert fractal inductor using an improved feed forward neural network for Si RFICs

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