Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling

TLDR

It is a systematic computational method to address the situation where an existing device model cannot fit new device data well. The paper proposes a new CAD method that advances Neuro‑space mapping and introduces an analytical formulation achieving the same accuracy as circuit‑based Neuro‑SM with higher computational efficiency. The method modifies current and voltage relationships, analytically maps the coarse model to the Neuro‑SM model for dc, small‑signal, and large‑signal simulation and sensitivity analysis, and employs a two‑phase gradient‑optimization training algorithm for rapid model training. The analytical formulation eliminates extra circuit equations, enabling efficient modeling of HBT, MESFET, and HEMT devices in harmonic balance simulations, and facilitates systematic, automated updates of nonlinear device model libraries for circuit simulators.

Abstract

A new computer-aided design (CAD) method for automated enhancement of nonlinear device models is presented, advancing the concept of Neuro-space mapping (Neuro-SM). It is a systematic computational method to address the situation where an existing device model cannot fit new device data well. By modifying the current and voltage relationships in the model, Neuro-SM produces a new model exceeding the accuracy limit of the existing model. In this paper, a novel analytical formulation of Neuro-SM is proposed to achieve the same accuracy as the basic formulation of Neuro-SM (known as circuit-based Neuro-SM) with much higher computational efficiency. Through our derivations, the mapping between the existing (coarse) model and the overall Neuro-SM model is analytically achieved for dc, small-signal, and large-signal simulation and sensitivity analysis. The proposed analytical formulation is a significant advance over the circuit-based Neuro-SM, due to the elimination of extra circuit equations needed in the circuit-based formulation. A two-phase training algorithm utilizing gradient optimization is also developed for fast training of the analytical Neuro-SM models. Application examples on modeling heterojunction bipolar transistor (HBT), metal-semiconductor-field-effect transistor (MESFET), and high-electron mobility transmistor (HEMT) devices and the use of Neuro-SM models in harmonic balance simulations demonstrate that the analytical Neuro-SM is an efficient approach for modeling various types of microwave devices. It is useful for systematic and automated update of nonlinear device model library for existing circuit simulators.