OPTICAL TRANSCEIVER TUNING USING MACHINE LEARNING
A method of training a machine learning system to determine operating parameters for optical transceivers includes connecting the transceiver to a test and measurement device, tuning the transceiver with a set of parameters, capturing a waveform from the transceiver, sending the waveform and the set of parameters to a machine learning system, and repeating the tuning, capturing, and sending until a sufficient number of samples are gathered.
1 . A method of training a machine learning system to determine operating parameters for optical transceivers, comprising:
connecting the transceiver to a test and measurement device;
tuning the transceiver with a set of parameters;
capturing a waveform from the transceiver;
sending the waveform and the set of parameters to a machine learning system; and
repeating the tuning, capturing, and sending until a sufficient number of samples are gathered.
2 . The method as claimed in claim 1 , further comprising:
setting a temperature for the transceiver;
waiting until the temperature stabilizes; and
recording the parameters.
3 . The method as claimed in claim 2 , further comprising adding the set of parameters to a histogram of parameters for the temperature.
4 . The method as claimed in claim 2 , further comprising repeating the setting, waiting and recording until a sufficient number of samples are gathered.
5 . The method as claimed in claim 1 , further comprising performing a measurement using the captured waveform.
6 . The method as claimed in claim 5 , wherein the measurement comprises one or more of TDECQ, OMA, ER, AOP, and RLM.
7 . The method as claimed in claim 5 , further comprising sending the measurement to the machine learning system.
8 . The method as claimed in claim 1 , wherein capturing the waveform further comprises performing clock recovery.
9 . The method as claimed in claim 1 , wherein capturing the waveform further comprises generating an eye diagram overlay containing only single-level transitions.
10 . The method as claimed in claim 1 , wherein the repeating occurs after changing the parameters to sweep a range for each parameter in the set of parameters.
11 . The method as claimed in claim 10 , further comprising determining an average for each parameter in the set of parameters.
12 . The method as claimed in claim 1 , wherein the machine learning system is configured to associate the waveform with the set of parameters.
13 . The method as claimed in claim 1 , further comprising testing a prediction accuracy of the machine learning system.
14 . The method as claimed in claim 13 , wherein testing the prediction accuracy of the machine learning system comprises sending test waveforms to the machine learning system.
15 . The method as claimed in claim 13 , wherein a sufficient number of samples are gathered when the prediction accuracy of the machine learning system meets a specified accuracy threshold.