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De-embedding Load-Pull Data

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New for October 2017!

The video content on this page came from Keysight, thanks, guys!

Load-pulling power devices is important for predicting their nonlinear performance, versus what impedances they see on input and output.  Because you are dealing with high-power devices, it is often impossible to test them on-wafer, with RF probes that could be easily calibrated out.  Your device ends up in a fixture so that you can draw the heat out of it and maintain constant temperature. The fixture parameters (for de-embedding) can be arrived at through measurement or modeling, these parameters are often called "error boxes".

The hard part is, how do you take load-pull contours and transform them through the error boxes?  Interpolating the data onto a uniform grid, applying the error box(es), and redrawing the contours is one way.  Keysight's ADS can be hooked up with Python, where Python does the interpolation, so that your de-embedding is seamless.  You can even tune the fixture parameters in real time!

De-embedding Measured Load Pull Data Using EM Analysis of a Fixture

Modern high frequency circuit simulation is complex and dealing with generated data can be challenging. In this video clip, Matt Ozalas describes how to enhance analysis of measured data using simulation data and he shows an example of de-embedding measured load-pull data using EM analysis of a fixture. You can also view additional video examples here that demonstrate the power and flexibility of linking Python and Keysight's Advanced Design System (ADS).

Matt Ozalas talks about de-embedding load pull data, using a combination of Python and ADS

 

 

 

Author : Unknown Editor

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