A wideband and scalable model of spiral inductors using space-mapping neural network

A wideband and scalable model of RF CMOS spiral inductors by virtue of a novel space-mapping neural network (SMNN) is presented. A new modified 2-pi equivalent circuit is used for constructing the SMNN model. This new modeling approach also exploits merits of space-mapping technology. This SMNN model has much enhanced learning and generalization capabilities. In comparison with the conventional neural network and the original 2-pi model, this new SMNN model can map the input-output relationships with fewer hidden neurons and have higher reliability for generalization. As a consequence, this SMNN model can run as fast as an approximate equivalent circuit, yet preserve the accuracy of detailed electromagnetic simulations. Experiments are included to demonstrate merits and efficiency of this new approach.