SYSTEMS AND METHODS FOR TUNING AND MEASURING A DEVICE UNDER TEST USING MACHINE LEARNING

1 . A test and measurement system, comprising:

a test and measurement instrument, including a port to receive a signal from a device under test (DUT); and

one or more processors, configured to execute code that causes the one or more processors to:

adjust a set of operating parameters for the DUT to a first set of reference parameters;

acquire, using the test and measurement instrument, a waveform from the DUT;

repeatedly execute the code to cause the one or more processors to adjust the set of operating parameters and acquire a waveform, for each of a predetermined number of sets of reference parameters;

build one or more tensors from the acquired waveforms;

send the one or more tensors to a machine learning system to obtain a set of predicted optimal operating parameters;

adjust the set of operating parameters for the DUT to the set of predicted optimal operating parameters; and

determine whether the DUT passes a predetermined performance measurement when adjusted to the set of predicted optimal operating parameters.

2 . The test and measurement system as claimed in claim 1 , wherein the one or more processors are further configured to execute code to cause the one or more processors to repeat the execution of the code for each temperature setting in a range of temperatures settings.

3 . The test and measurement system as claimed in claim 1 , wherein the one or more processors are distributed among the test and measurement instrument, a test automation system and a machine learning system separate from the test and measurement instrument.

4 . The test and measurement system as claimed in claim 1 , wherein the machine learning system comprises a plurality of neural networks, one or more to produce the set of predicted optimal operating parameters, one to produce a predicted measurement value, and one to produce a set of optimized feed-forward equalization filter tap values.

5 . The test and measurement system as claimed in claim 1 , wherein the predetermined number of sets of reference parameters is three, one set of average values for the parameters, one set having values higher than the average values, and one set having values lower than the average values.

6 . The test and measurement system as claimed in claim 1 , wherein the one or more processors are further configured to execute code to cause the one or more processors to apply an inverse normalization to the predicted operating parameters.

7 . The test and measurement system as claimed in claim 1 , wherein the test and measurement instrument is an oscilloscope.

8 . A method to determine optimal operating parameters for a device under test (DUT), the method comprising:

connecting the DUT to a test and measurement instrument;

adjusting a set of operating parameters for the DUT to a first set of reference parameters;

acquiring, using the test and measurement instrument, a waveform from the DUT;

repeating the adjusting the set of operating parameters and acquiring a waveform, for each of a predetermined number of sets of reference parameters;

building one or more tensors from the acquired waveforms;

sending the one or more tensors to a machine learning system to obtain a set of predicted optimal operating parameters;

adjusting the set of operating parameters for the DUT to the set of predicted optimal operating parameters; and

determining whether the DUT passes a predetermined performance measurement when adjusted to the set of predicted optimal operating parameters.

9 . The method as claimed in claim 8 , further comprising repeating the method for each temperature in a range of temperatures.

10 . The method as claimed in claim 8 , wherein building one or more tensors comprises building a levels tensor, an impulse tensor, and a combined tensor.

11 . The method as claimed in claim 8 , wherein building a combined tensor comprises building an image having three or more channels.

12 . The method as claimed in claim 11 , wherein the image includes a bar graph image representing at least one of a temperature value and a noise value.

13 . The method as claimed in claim 8 , further comprising generating reference operating parameters, comprising:

tuning a plurality of optical transceivers at a plurality of temperatures;

generating a histogram of each tuning parameter, at each temperature, for the plurality of tuned optical transceivers;

calculating a mean reference parameter set by averaging values for each parameter in the histograms;

increasing values in the mean parameter set to create a first delta reference parameter set; and

decreasing values in the mean parameter set to create a second delta reference parameter set.

14 . The method as claimed in claim 8 , wherein building one or more tensors comprises building a tensor having three channels, one channel for a mean reference tensor, one channel for a first delta tensor, and one channel for a second delta tensor.

15 . The method as claimed in claim 8 , wherein sending the one or more tensors to a machine learning system comprises:

sending the one or more tensors to two neural networks in the machine learning system to obtain optimized operating parameters;

sending an impulse tensor to a third neural network to obtain optimized feed-forward equalization filter taps; and

sending a combined tensor to a fourth neural network to obtain a predicted transmitter and dispersion eye closure penalty quaternary (TDECQ) measurement.

16 . The method as claimed in claim 8 , wherein determining whether the DUT passes a predetermined performance measurement comprises measuring an output of the DUT and determining if the output is within a predetermined range.

17 . The method as claimed in claim 8 , further comprising applying an inverse normalization to the set of predicted optimal operating parameters.