The W7012E PathWave IC-CAP FET Plus Extraction Package software includes measurement and extraction procedures for various MESFET and HEMT models. It also enables the proprietary Keysight Root MOS, FET, and Diode models.

Highlights

The W7012E PathWave IC-CAP FET Plus Extraction Package includes:

  • Angelov-GaN model for GaN HEMT devices
  • Keysight EDA EEFET3 / EEHEMT1 empirical, nonlinear models for GaAs FET
  • Curtice, Statz MESFET models for MESFET
  • Keysight Root FET Model for MESFETs and HEMTs
  • Keysight Root MOSFET Model for LD and power MOS devices
  • Keysight Root Diode Model for varactor and Schottky diodes

The W7012E PathWave IC-CAP FET Plus Extraction Package software offers the following models for high-power FET and HEMT RF devices.

Curtice, Statz MESFET

These models include extraction routines for three popular industry-standard MESFET models:

  • Curtice quadratic
  • Curtice cubic
  • Statz (Raytheon)

The differences between the three models are in the empirical relationships that describe the DC and AC characteristics of the device. IC-CAP extracts the model parameters from a combination of DC and S-parameter measurements.

Keysight EDA EEFET3 / EEHEMT1

These are empirical, nonlinear models for general GaAs FET applications: large-signal, 3-terminal IC, and packaged devices. They accurately model DC and bias-dependent S-parameters, time delay, subthreshold current, and dispersion of Rds. Also included is the drain current model based on Keysight EDA models of original equations and advanced models for Cgs and Cgd, including transcapacitance effects. Static self-heating effects in drain current are also taken into account. The module provides highly automated parameter extraction techniques with package parasitics extracted automatically. HEMTs are similar to MESFETs but with one distinguishing difference in the behavior of gm vs. Vgs. EEHEMT1 is a superset of EEFET3 and has a set of analytic functions for modeling the gm compression of a HEMT.

Angelov-GaN Model

The Angelov-GaN Extraction Package offers a complete modeling solution for Gallium Nitride (GaN) devices. Modeling these devices is challenging due to trapping and thermal effects on the device's electrical characteristics. GaAs models are generally not accurate enough for this type of device. The Angelov-GaN model, developed by Prof. I. Angelov at the Chalmers University of Technology, has quickly established itself as one of the industry's standard solutions.

The package was developed in conjunction with industry partners and validated on real GaN processes. It provides a dedicated software environment that enables users to perform the necessary measurements and extract the Angelov-GaN model. Typical DC and network analyzers are supported for making DC and S-parameter measurements and de-embedding. A convenient user interface enables users to execute a step-by-step extraction flow to extract the model parameters. A turn-key flow provided in the package allows complete customization. Simulations are performed using PathWave Advanced Design System (ADS) using the publicly available Verilog-A version of the Angelov-GaN model.

  • Complete measurement module — The package acquires DC and S-parameter measured data used to extract the model and manages open/short de-embedding. Measurements can be performed using the Keysight B1505A High Power/High Voltage Analyzers and the Keysight PNA vector analyzer.
  • Turn-key extraction flow — A step-by-step extraction flow is provided to be used to obtain an initial parameter set. This flow is based on Keysight's experience working side-by-side with industry partners to model these types of devices.
  • Fully flexible — The extraction flow is fully customizable. New extraction steps can be created and organized to adapt the extraction to a specific technology. The model implementation uses open Verilog-A code, and users can enhance its formulation.
  • Easy to use — A dedicated user interface guides users through settings, measurements, and the extraction procedure. A powerful data display enables users to compare measured versus simulated data in a convenient multi-plot window.

Keysight Root Models

The Keysight Root models are a data-based model rather than a physical or empirical model. They generate a device-specific, large-signal model directly from measured DC and small-signal S-parameter data that accurately represents the terminal characteristics of the device. The software constructs the model without circuit simulation or optimization. The use of S-parameters and DC data results in a model that can accurately predict nonlinear device performance with frequency dispersion, as a model simulated with a single set of nonlinear current functions cannot. The Keysight Root models predict the device performance as a function of bias, power level, and frequency. It allows circuits containing devices for which measured data exists but for which there are no good physical or empirical models to be simulated.

The toolkit controls the system hardware to take DC and S-parameter measurements. Thus, the model is generated without the usual need for simulation and optimization as required by all previous empirical models. Output data files are directly readable by ADS. During a simulation, ADS interpolates the tabular state-function data from the generated Keysight Root model using multidimensional spline functions to emulate the terminal characteristics of the device.

This license includes three different toolkits for the following extractions:

  • Keysight Root FET Model - works for MESFETs and HEMTs. It is scalable to allow simulation of devices with different geometries based on the characterization of a single device. Used for positive drain-source bias (Vds ≥ 0), which corresponds to the typical operating region.
  • Keysight Root MOSFET Model - can be applied to vertical, LD, and power MOS devices, where substrate effects do not need to be a part of the model. It covers both NMOS and PMOS devices.
  • Keysight Root Diode Model - used for varactor and Schottky diodes, in a two-port or one-port configuration.

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