Automated cavity filter tuning using machine learning

A test and measurement instrument, comprising:

• one or more ports to connect to one or more devices under test (DUT) having tuning screws, and to a robot;
• and
• one or more processors configured to execute code, the code to cause the one or more processors to:
• send commands to the robot to position the tuning screws on the one or more DUTs to one or more sets of positions, each set of positions comprises a parameter set for the tuning screws;
• acquire a set of operating parameters for each parameter set from the one or more DUTs;
• generate a parameter set image for each set of operating parameters, wherein the parameter set image includes a plot of an operating parameter of the one or more DUTs;
• create a combined image of the parameter set images, wherein the combined image uses a different channel for each of the parameter set images;
• provide the combined image to a machine learning system to obtain a predicted set of values;
• adjust the predicted set of values as needed to produce a set of predicted positions;
• send commands to the robot to position the tuning screws on the one or more DUTs to positions in the set of predicted positions;
• obtain a set of tuned operating parameters from the one or more DUTs;
• and
• validate operation of the one or more DUTs based upon the tuned operating parameters.