Jan Verspecht

Straightforward and accurate nonlinear device model parameter-estimation method based on vectorial large-signal measurements

To model nonlinear device behavior at microwave frequencies, accurate large-signal models are required. However, the standard procedure to estimate model parameters is often cumbersome, as it involves several measurement systems (DC, vector network analyzer, etc.). Therefore, we propose a new nonlinear modeling technique, which reduces the complexity of the model generation tremendously and only requires full two-port vectorial large-signal measurements. This paper reports on the results obtained with this new modeling technique applied to both empirical and artificial-neural-network device models. Experimental results are given for high electron-mobility transistors and MOSFETs. We also show that realistic signal excitations can easily be included in the optimization process.

New Trends for the Nonlinear Measurement and Modeling of High-Power RF Transistors and Amplifiers With Memory Effects

Power amplifier (PA) behavior is inextricably linked to the characteristics of the transistors underlying the PA design. All transistors exhibit some degree of memory effects, which must therefore be taken into account in the modeling and design of these PAs. In this paper, we will present new trends for the characterization, device modeling, and behavioral modeling of power transistors and amplifiers with strong memory effects. First the impact of thermal and electrical memory effects upon the performance of a transistor will be revealed by comparing continuous wave and pulsed RF large-signal measurements. Pulsed-RF load-pull from the proper hot bias condition yields a more realistic representation of the peak power response of transistors excited with modulated signals with high peak-to-average power ratio. Next, an advanced device modeling method based on large-signal data from a modern nonlinear vector network analyzer instrument, coupled with modeling approaches based on advanced artificial neural network technology, will be presented. This approach enables the generation of accurate and robust time-domain nonlinear simulation models of modern transistors that exhibit significant memory effects. Finally an extension of the X-parameter (X-parameter is a trademark of Agilent Technologies Inc.) behavioral model to account for model memory effects of RF and microwave components will be presented. The approach can be used to model hard nonlinear behavior and long-term memory effects and is valid for all possible modulation formats for all possible peak-to-average ratios and for a wide range of modulation bandwidths. Both the device and behavioral models have been validated by measurements and are implemented in a commercial nonlinear circuit simulator.

Linearization of large-signal scattering functions

We describe a linearization of large-signal scattering functions describing weakly nonlinear device behavior. The linearization takes on a convenient form similar to scattering parameters that clearly illustrates the role of phase-conjugated mixing products in the theory. We develop rules for the evolution of the linearization with time. We illustrate the theory with transistor measurements and apply the theory to the characterization of the reflection coefficients of a microwave source in its large-signal operating state.

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.

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.

An improved coupling method for time domain load-pull measurements

This paper describes an efficient coupling method improving the nonlinear time domain large signal load-pull measurements of active devices. This approach consists in a small RF loop fixed near the blended line that takes place between the DUT and the tuner. We take benefit of two advantages: extremely low losses induced by the coupler, and measurements taken very close to the DUT plane. It is shown that this simple coupling solution offers an average directivity better than 15 dB (before calibration) along a wide RF band from 2 to 18 GHz. This solution is compared with the classical approach of a distributed coupler connected before and after the tuner. A LSNA calibration has been performed, it exhibits an average directivity better than 35 dB.

Introduction to measurements for power transistor characterization

In this article, we will introduce you to measurements for power transistor characterization: why they matter, why they are such a complicated, highly specialized field, and where we think the technology of power transistor characterization is headed. The characterization of microwave power transistors is an important and emerging field with many interesting engineering challenges. One can basically distinguish two areas: model extraction measurements and model validation measurements. To make things simple, isothermal pulsed-bias pulsed S-parameter measurements are typically used for model extraction purposes and load-pull measurements are typically used for model validation purposes. Both areas are rapidly evolving in order to keep track of new power transistor technology. The main issue with pulsed-bias pulsed S-parameter characterization is the need to apply pulses with ever-increasing amplitude (up to 200 V and 10 A) and ever-decreasing pulse width (smaller than 400 ns). The load-pull measurements can be done with a variety of setups, with active or passive approaches, and with or without handling harmonic frequencies. The challenges of load-pull system development are to offer time-domain voltage and current waveforms at the transistor terminals-an invaluable tool to provide insight in highly nonlinear transistor behavior-in addition to the capability to present low input impedances (1 Omega) and to handle high power levels (up to 100 W).

Analysis of Interconnection Networks and Mismatch in the Nose-to-Nose Calibration

We analyze the input networks of the samplers used in the nose-to-nose calibration method. Our model demonstrates that the required input network conditions are satisfied in this method and shows the interconnection errors are limited to measurement uncertainties of input reflection coefficients and adapter S-parameters utilized during the calibration procedure. Further, the input network model fully includes the effects of mismatch reflections, and we use the model to reconcile nose-to-nose waveform correction methods with traditional signal power measurement techniques.

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.