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Root D.E.,Agilent Technologies | Xu J.,Agilent Technologies | Horn J.,Agilent Technologies | Iwamoto M.,Agilent Technologies | Simpson G.,Maury Microwave Corporation
2010 Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits, INMMiC 2010 - Conference Proceedings | Year: 2010

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. © 2010 IEEE. Source

Dudkiewicz S.,Maury Microwave Corporation
Microwave Journal | Year: 2011

The improvements in large-signal device characterization brought on by a new class of vector receiver load pull systems compared to older scalar techniques using calibrated automated load pull tuners is discussed. Because powers are measured from power meters and de-embedded through tuners, extremely accurate tuner characterization and tuner repeatability are required. Vector-receiver load pull overcomes these weaknesses by directly measuring the a- and b-waves of a device in real-time, thereby determining the large-signal input impedance at each input power and enabling the determination of delivered input power, power gain and power-added efficiency. Since the system is calibrated at the DUT reference plane, inaccuracies arising from tuner de-embedding, and possibly lengthy tuner characterizations are eliminated. Additionally, overall measurement time is greatly reduced by the system's ability to mathematically compute source contours and eliminate the multiple source-pull load pull iterations required by traditional load pull. Source

Maury Microwave Inc. | Date: 2015-10-23

Systems and methods of measuring and determining noise parameters. An exemplary method measures noise data and determines element values of a device noise model for a device under test (DUT), using a test system including an impedance tuner coupled to an input of the DUT for presenting a controllable variable impedance to the DUT and a noise receiver coupled to an output of the DUT. Noise data is measured as a function of at least one measurement parameter. The measured data includes raw noise data read from the noise receiver, and is used to determine element values of the device noise model. The system may include a database of device models

Maury Microwave Inc. | Date: 2014-07-30

A mechanical impedance tuner has at least two probe carriages mounted for movement along an axis parallel to the center conductor. The at least two probe carriages including a first probe carriage and a second probe carriage. Each probe carriage has at least N probes where N is an integer equal to or greater than one, and at least one of the N probes is mechanically different or of different nominal geometry from the probes on at least one of the other carriages so that each such probe has an non-identical frequency response.

Maury Microwave Inc. | Date: 2015-05-27

An impedance tuner includes a controller, an RF transmission line, and a movable capacitive object configured for movement commanded by the controller relative to the transmission line to alter impedance. A position sensor is configured to provide feedback position data to the controller indicative of the actual position of the capacitive object after it is moved. The controller is configured to utilize the feedback position data in a closed loop to position the capacitive object at a desired position within a tolerance.

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