Function generator specifications explained, which specs to validate, and how to automate the test with TestFlow straight from the datasheet.

Function generator specifications decide whether a signal source is good enough for your test, and they are also the exact list of things you should validate before you trust it. This guide breaks down the function generator specifications that matter, what to test for each, and how to automate the whole validation with TestFlow straight from the datasheet.
When you read a function generator or signal generator datasheet, these are the specs that drive real test decisions:
Validating a function generator is not one measurement. For each spec above you set the generator to a target, measure the real output with a calibrated instrument such as a DMM, scope, or analyzer, and compare against the datasheet tolerance. Do that across the range, repeat it, and log every result. Done by hand, this is hours of front-panel work and transcription into a spreadsheet.
Most engineers validate a generator one of two ways. Manually, by setting each point on the front panel and writing readings into Excel, which is slow and error-prone. Or with a hand-written SCPI or PyVISA script, which is faster to run but fragile and specific to one instrument pairing.
TestFlow turns the function generator datasheet into the validation plan and the automation that runs it.
Whether you run a Keysight 33600A, a 33500B, or a third-party signal generator, the specs to test are the same, and so is the manual effort you can remove.
Start free at testflow.io, upload your function generator datasheet, and get an automated validation plan in minutes.
Connect your instruments, describe a test in plain English, and TestFlow builds and runs it in minutes.
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