Jason Horn

Extension of X-parameters to include long-term dynamic memory effects

A new unified theory and methodology is presented to characterize and model long-term memory effects of microwave components by extending the Poly-Harmonic Distortion (PHD) Model to include dynamics that are identified from pulsed envelope X-parameter measurements on an NVNA. The model correctly predicts the transient RF response to time-varying RF excitations including the asymmetry between off-to-on and on-to-off switched behavior as well as responses to conventional wide-bandwidth communication signals that excite long-term memory effects in power amplifiers. The model is implemented in the ADS circuit envelope simulator.

Load-pull + NVNA = enhanced X-parameters for PA designs with high mismatch and technology-independent large-signal device models

X-parameters are the mathematically correct supersets of S-parameters valid for nonlinear (and linear) components under large-signal (and small-signal) conditions. This work presents an automated application combining a nonlinear vector network analyzer (NVNA) instrument with automated load-pull measurements that extends the measurement and extraction of X-parameters over the entire Smith Chart. The augmented X-parameter data include magnitude and phase as nonlinear functions of power, bias, and load, at each harmonic generated by the device and measured by the NVNA. The X-parameters can be immediately used in a nonlinear simulator for complex microwave circuit analysis and design. This capability extends the applicability of measurement-based X-parameters to highly mismatched environments, such as high-power and multi-stage amplifiers, and power transistors designed to work far from 50 ohms. It provides a powerful and general technology-independent alternative, with improved accuracy and speed, to traditional large-signal device models which are typically slow to develop and typically extrapolate large-signal operation from small-signal and DC measurements.

Multi-tone, Multi-port, and Dynamic Memory Enhancements to PHD Nonlinear Behavioral Models from Large-signal Measurements and Simulations

The PHD nonlinear behavioral model is extended to handle multiple large tones at an arbitrary number of ports, and enhanced for dynamic long-term memory. New capabilities are exemplified by an amplifier model, derived from large-signal network analyzer (LSNA) data, valid for arbitrary impedance environments, and a model of a 50GHz integrated mixer, including leakage terms and IF mismatch dependence. Dynamic memory is demonstrated by an HBT amplifier model identified from up-converted band-limited noise excitations. The models are validated with independent LSNA component data or, for simulation-based models, with the corresponding circuit models.

GaN Device Modeling with X-Parameters

Arbitrary-load-dependent X-parameters, automatically measured with a load-tuner working with an NVNA, are used to characterize and model a packaged 10W GaN transistor. A full nonlinear two-port functional block model for PA and other circuit design is immediately available for nonlinear simulation. It is demonstrated that the model predicts well the independent effects of harmonic load tuning without having to independently control harmonic loads during characterization. The nonlinear model can be used effectively to obtain optimal fundamental and harmonic impedances for device operation, as well as predict, accurately, other nonlinear FOMs including PAE and harmonic distortion. Source-pull is shown to be unnecessary except for efficient power transfer, yet the model is fully capable of predicting correct device response when embedded in any source and load impedance at the fundamental and harmonics.

Large-signal FET model with multiple time scale dynamics from nonlinear vector network analyzer data

A non-quasi static large-signal FET model is presented incorporating self-heating and other multiple timescale dynamics necessary to describe the large-signal behavior of III–V FET technologies including GaAs and GaN. The model is unique in that it incorporates electro-thermal and trapping dynamics (gate lag and drain lag) into both the model current source and the model nonlinear output charge source, for the first time. The model is developed from large-signal waveform data obtained from a modern nonlinear vector network analyzer (NVNA), working in concert with an output tuner and bias supplies. The dependences of Id and Qd on temperature, two trap states, and instantaneous terminal voltages are identified directly from NVNA data. Artificial neural networks are used to represent these constitutive relations for a compiled implementation into a commercial nonlinear circuit simulator (Agilent ADS). Detailed comparisons to large-signal measured data are presented.

Device modeling with NVNAs and X-parameters

This paper reviews and contrasts two complementary device modeling approaches based on data readily obtainable from a nonlinear vector network analyzer (NVNA) [1]. The first approach extends the application of waveform data to improve the characterization, parameter extraction, and validation methodologies for “compact” transistor models. NVNA data is used to train artificial neural network -based constitutive relations depending on multiple coupled dynamic variables, including temperature and trap states for an advanced compact model suitable for GaAs and GaN transistors. The second approach is based on load-dependent X-parameters* [2], [3], [5], [6], measured using an output tuner working with the NVNA. It is demonstrated that X-parameters measured versus load at the fundamental frequency predict well the independent effects of harmonic load tuning on a 10W GaN packaged transistor without having to independently control harmonic loads during characterization. A comparison of the respective merits of the two approaches is presented.

A simplified extension of X-parameters to describe memory effects for wideband modulated signals

An original way is presented to model memory effects of microwave amplifiers in the case of wideband modulated signals. The model is derived as a limiting case of the more general dynamic X-parameter theory. For a given component, the model is identified from pulsed envelope X-parameter measurements performed with an NVNA. The resulting nonlinear X-parameter model is quantitatively described by a 2-variate kernel function that enables the derivation of an optimal static AM-AM AM-PM characteristic for every possible input envelope probability density function. The model is validated by performing a set of 2-tone experiments. The model can be implemented in the ADS circuit envelope simulator.

Measurement-based large-signal simulation of active components from automated nonlinear vector network analyzer data via X-parameters

Predictable measurement-based large-signal design has been demonstrated with a unique set of interoperable commercially available nonlinear technologies for measurement, simulation, and design of nonlinear components. The new NVNA instrument, automated X-parameter measurements and extraction, and auto-configurable compiled PHD component in ADS, together enable design of nonlinear circuits entirely from fully calibrated nonlinear component data.

Scaling of X-parameters for device modeling

The relationships between X-parameters of a given transistor and a second transistor geometrically scaled with respect to the first are derived and presented for the first time. The different types of X-parameters scale differently. These relationships enable X-parameters measured on a fixed size of transistor, diode, or other similar test structure to be scaled to other sizes and produce X-parameter functions as continuous function of size (e.g. total gate width or area). This capability endows X-parameters with a key property of conventional scalable “compact models”, enabling an improved MMIC design capability where size/geometry is a key design degree of freedom. The scalable X-parameters for device modeling are implemented in a commercial nonlinear simulator. The theoretical predictions are validated with numerical results from simulation-based extractions and experimental nonlinear measurements taken with an NVNA on active devices of different sizes.

Compact and behavioral modeling of transistors from NVNA measurements: New flows and future trends

This paper reviews three modern transistor modeling flows enabled by large-signal waveform and/or X-parameter1 measurements from a commercially available nonlinear vector network analyzer (NVNA) instrument. NVNA transistor characterization more safely exercises the device over a wider operating domain than is possible with conventional DC and linear S-parameter measurements, is more indicative of the device large-signal response in actual use conditions, provides data at much faster timescales than pulsed I-V methods, and provides large-signal model validation as a free additional benefit. In the first flow considered, NVNA waveform data is used as a target to extract and tune compact model parameter values and for model validation under large-signal conditions. In the second flow, NVNA waveform data is used to directly construct the multi-variate nonlinear current-source and charge-based nonlinear capacitor functions of an advanced electrothermal and trap-dependent compact model suitable for GaAs and GaN FETs, effectively bypassing the need for explicit model constitutive relation formulation. The final approach is based on the X-parameter measurement and behavioral modeling framework supported by the NVNA, producing nonlinear transistor models directly in the frequency domain. Recent advances in X-parameter methods for transistors, including simple scalability with geometry, show early potential for useful device models, under certain conditions, without the requirement of specifying an internal topology or equivalent circuit at all.