Neuro-space mapping technique for microwave device modeling and its use in circuit simulation and statistical design

Nonlinear device modeling is an important area of computer-aided design for fast and accurate microwave design and optimization. The purpose of this thesis is to develop advanced modeling techniques for efficient generation of microwave device models. The proposed techniques combine the universal fitting capability of neural networks and the cost-effective optimization concept of space mapping, to achieve reliable device models for nonlinear circuit simulation and statistical design.;To meet the constant need for new device models due to the rapid progress in semiconductor technology, a neuro-space mapping (Neuro-SM) technique is firstly proposed. It automatically modifies the behavior of existing models to match new device behavior. Neuro-SM models improve the accuracy of existing device models while retaining the model speed. An advanced Neuro-SM formulation is proposed with analytical mapping representations and exact sensitivity analysis for efficient model training and evaluation. The analytical Neuro-SM model can be incorporated into high-level simulators to increase the speed and accuracy of circuit design. By mapping the existing equivalent circuit models to detailed device physics data, the Neuro-SM can also efficiently expand the scope of models in existing circuit simulators to include device physics behavior.;This Neuro-SM concept is expanded for efficient large-signal statistical modeling of nonlinear microwave devices. A linear statistical space mapping technique and a statistical neuro-space mapping technique are proposed. The proposed techniques introduce a new statistical space mapping concept that can expand a large-signal nominal model into a large-signal statistical model. The nominal model is extracted or trained from one complete set of dc, small-signal, and large-signal data. The behavior of a random device in the population is obtained by a mapping from that of the nominal device. In the linear statistical space mapping technique, we propose to use a simple linear dynamic mapping. In the statistical Neuro-SM, this mapping is nonlinear and represented by neural networks to overcome the accuracy limitations of the linear mapping in modeling large statistical variations among different devices. The statistical parameters of the model are extracted from dc and small-signal S-parameter data of many device samples. In this way, the proposed techniques allow efficient large-signal statistical model development with reduced expense of otherwise massive large-signal measurements for many devices.