Optical transmitter tuning using machine learning and reference parameters

1. A test and measurement system, comprising:

a test and measurement device;

a connection to allow the test and measurement device to connect to an optical transceiver; and

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

set operating parameters for the optical transceiver to reference operating parameters by sending the reference operating parameters to the optical transceiver;

acquire a waveform from the optical transceiver;

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

set the operating parameters for the optical transceiver to the predicted operating parameters by sending the predicted operating parameters to the optical transceiver; and

test the optical transceiver using the predicted operating parameters by receiving a waveform from the optical transceiver to determine whether the optical transceiver passes or fails.

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 device, a test automation system and a machine learning system separate from the test and measurement device.

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 predicted 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. A method to tune an optical transceiver, comprising:

connecting the optical transceiver to a test and measurement device;

setting operating parameters for tuning the optical transceiver to reference operating parameters by sending the operating parameters to the optical transceiver;

acquiring a waveform from the optical transceiver;

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

setting the operating parameters for tuning the optical transceiver to the predicted operating parameters by sending the predicted operating parameters to the optical transceiver; and

validating the predicted operating parameters by receiving a waveform signal from the optical transceiver.

8. The method as claimed in claim 7 , further comprising repeating the method for each temperature in a range of temperatures.

9. The method as claimed in claim 7 , wherein building one or more tensors comprises building a levels tensor, an impulse tensor, and a combined tensor.

10. The method as claimed in claim 7 , wherein building a combined tensor comprises building an image having three or more channels.

11. The method as claimed in claim 10 , wherein the image includes a bar graph image representing at least one of a temperature value and a noise value.

12. The method as claimed in claim 7 , 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.

13. The method as claimed in claim 7 , 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.

14. The method as claimed in claim 7 , 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.

15. The method as claimed in claim 7 , wherein validating the predicted operating parameters comprises measuring the output of the transceiver and determining if the output is within a predetermined range.

16. The method as claimed in claim 7 , further comprising applying an inverse normalization to the predicted operating parameters.

17. A method of training a machine learning system for tuning optical transceivers, comprising:

creating a predetermined number of sets of reference operating parameters;

repeating, for each set of reference operating parameters:

setting the operating parameters for the optical transceiver to a set of reference operating parameters by sending the reference operating parameters to the optical transceiver;

acquiring a waveform from the optical transceiver;

tuning the operating parameters of the optical transceiver to meet a desired output; and

performing transmitter dispersion eye closure quaternary (TDECQ) analysis on the waveform;

providing results of the analysis and the waveforms to a tensor builder;

sending one or more tensors to one or more neural networks with the associated waveforms and reference operating parameters; and

repeating the method until a sufficient number of transmitters have been tuned.

18. The method as claimed in claim 17 , wherein creating the predetermined number of sets of reference operating parameters comprises:

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

generating a histogram of each 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.

19. The method as claimed in claim 17 , further comprising normalizing the reference parameters.

20. The method as claimed in claim 17 , wherein the one or more neural networks comprise one or more neural networks for levels, one network for providing values for feed-forward equalization filters, and one network for a time dispersion eye closure quaternary value.