Automating antenna impedance characterization with Connect
Nominal Connect offers custom HMI applications for RF engineers to coordinate, calibrate, and save test runs
What is antenna matching?
Antennas have become ubiquitous in modern technology, serving a vital role in the transmission and reception of RF signals across a diverse set of applications. Their influence ranges from the cell phones we carry in our pockets to the missile defense radars that line our coasts. They are therefore critical hardware components that require extensive test and validation campaigns.
Impedance matching is an important part of the antenna design and test process that ensures the antenna performs with maximum efficiency within the desired frequency ranges. For RF test engineers, antenna characterization can be an involved and difficult process, as it requires repeated calibrations, measurements, and analysis with benchtop instruments such as a Vector Network Analyzer (VNA). As antenna production continues to grow, test engineers are forced to perform tedious, critical testing – again and again and again.
To effectively operate and execute these tests, test engineers need first-class computer-based automation, extensive python script libraries, and an intuitive interface for executing and interpreting tests.
That’s where Nominal Connect comes in.
Nominal Connect: The platform for antenna benchtop instruments
Nominal Connect provides test engineers with a platform to calibrate and command benchtop instruments, gather measurements in real-time, and save sweep parameters. In particular, it unlocks value for test engineers by operationalizing python scripts for hardware.
Nominal Connect provides customizable Human-Machine Interface (HMI) framework on top of Python scripts. Without an HMI, these Python scripts are run directly on the command line, creating failures, inflexibility, and information siloes. With Connect’s Python-first SDK, engineers can take advantage of open-source instrumentation libraries such as PyVISA to easily communicate with numerous benchtop instruments.
In the case of impedance characterization, in less than 30 lines of Python code, an engineer can:
Establish connection to a VNA
Set sweep parameters (bandwidth, start/stop frequency, power level, point count)
Run a frequency sweep
Measure and return S-parameters of the device-under-test (DUT)
Nominal Connect’s real-time visualization and customizable GUIs unlock the extensive capabilities of python-based hardware engineering. Furthermore, Connect integrates with Nominal Core, which provides saved test sequences, cloud-based sharing of results across teams, and a timeseries database purpose-built for hardware engineering.
Setting parameters and running the sweep
Primary parameters for a VNA sweep include:
Start frequency: the beginning frequency of the sweep
Stop frequency: the final frequency of the sweep
Point count: the number of discrete frequency steps within the sweep range
Power level: the power level of the VNA source signal
Bandwidth: the bandwidth of the intermediate frequency (IF) filter
With Connect’s framework, an engineer can build a simple and intuitive interface that allows users to input values for these parameters and send commands to configure the VNA accordingly. No need to rewrite messy config files every time you want to run a sweep, just input the desired values and click “Run Sweep.” Connect’s real-time visualization will show you the measurements and give results immediately.
Saving results
Once a run is complete, an engineer can easily save the collected measurements to a CSV or Touchstone file, documenting the results of the test and making the data accessible, queryable, and shareable.
Pre-designed Smith Chart widget pre-built into Nominal Connect UI, saving engineer time
Nominal Connect handles the data;
RF engineers focus on the mission
By building an application in Nominal Connect that streamlines much of the antenna impedance matching process, an RF test engineer can save several hours of time spent on debugging scripts or building a UI, and instead focus on collecting measurements, drawing insights, and sharing results – to build at scale, faster.