Daniel Gunyan

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.

A 60 GHz MMIC chipset for 1-Gbit/s wireless links

This paper describes the development of a MMIC chipset for 60 GHz; radio links and radars. The chipset includes a low noise amplifier, an image rejection mixer, a frequency quadrupler, and a power amplifier. All were optimized to work together as a 1-Gbit/s radio link in the unlicensed 59 GHz to 64 GHz wireless band, although most are suitable for any application from 55 GHz to 70 GHz. These MMICs are fabricated in Agilents advanced e-beam PHEMT process and have been demonstrated in fully operational 1-Gbit/s radio-links in field testing.

Nonlinear validation of arbitrary load X-parameter and measurement-based 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 paper compares a PHD model generated from arbitrary load-dependent measured X-parameters and a measurement-based non-quasi-static device model and validates them against tuned-load measurements. CW, IMD, and ACPR swept-power measurements are compared. The models agree on the simulated device behavior and compare well to validation measurements..

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.

The Random Component of Mixer-Based Nonlinear Vector Network Analyzer Measurement Uncertainty

The uncertainty, due to random noise, of the measurements made with a mixer-based nonlinear vector network analyzer are analyzed. An approximate covariance matrix corresponding to the measurements is derived that can be used for fitting models and maximizing the dynamic range in the measurement setup. The validity of the approximation is verified with measurements.

Characterization of active harmonic phase standard with improved characteristics for nonlinear vector network analyzer calibration

This paper presents unique characteristics of a new active harmonic phase standard (HPS) for use in phase calibration of nonlinear vector network analyzers (NVNAs). The phase and amplitude characteristics are measured at various input powers, fundamental frequencies, and temperatures. The new active HPS is found to have a low sensitivity to these variables. This means that unlike conventional SRD-based HPS approaches, the new active HPS can be calibrated at a single fundamental frequency, input power level, and temperature, and still be successfully applied to a wide range of input powers and fundamental frequencies. The new HPS is compared to a conventional SRD-based HPS and found to be superior for NVNA calibration and related applications.

Drain-Source Symmetric Artificial Neural Network-Based FET Model with Robust Extrapolation Beyond Training Data

A large-signal FET model based on artificial neural networks (ANNs) is extended for rigorous intrinsic drain-source symmetry and robust extrapolation beyond the range of training data. Enhanced ANN architectures and training algorithms constrain the five nonlinear model state functions to transform according to the discrete symmetry rules related to the device invariance with respect to intrinsic drain-source exchange. This extends the applicability of the previous ANN-based model to situations where the instantaneous voltage crosses Vds= 0, such as switches and mixers. The model is compiled in Agilent ADS, together with advanced extrapolation routines extending the model beyond the range of training data for improved convergence. The model has been generated for FETs from several III-V semiconductor processes, and validated with extensive independent small and large-signal measurements.

Noise considerations when determining phase of large-signal microwave measurements

Advances in microwave instrumentation now make it feasible to accurately measure not only the magnitude spectrum, but also the phase spectrum of wide-bandwidth signals. In a practical measurement, the spectrum is measured over a finite window of time. The phase spectrum is related to the position of this window, causing the spectrum to differ between measurements of an identical waveform. It is difficult to compare multiple measurements with different window positions or to incorporate them into a model. Several methods have been proposed for determining the phase spectrum such that multiple measurements can be effectively compared and utilized in models. The methods are reviewed in terms of the information required to determine the phase and compared in terms of their robustness in the presence of measurement noise