Machine learning for taps to accelerate TDECQ and other measurements

Abstract

A test and measurement instrument has an input configured to receive a signal from a device under test, a memory, a user interface to allow the user to input settings for the test and measurement instrument, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test. A method of testing a device under test includes acquiring a waveform representing a signal received from the device under test, generating one or more tensor arrays based on the waveform, applying machine learning to the one or more tensor arrays to produce equalizer tap values, applying the equalizer taps values to the waveform to produce an equalized waveform, performing a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

Description

TECHNICAL FIELD

This disclosure relates to test and measurement systems, and more particularly to systems for component parameter optimization and performance measurements.

BACKGROUND

When the signal speed increases, transmitters and receivers typically use equalizers to improve the system performance. For example, the IEEE 100G/400G Ethernet standards defines the measurement with a 5-tap feed-forward equalizer (FFE). See, for example, “IEEE 802.3cd-2018”. http://standards.ieee.org/develop/project/802.3cd.html, 2018; “IEEE 802.3bs-2017”, http://standards.ieee.org/findstds/standard/802.3bs-2017.html 2017.

Many standards have performance measurements that the devices under test must meet. Some standards require measurements made to meet the standard be performed on the equalized signals. For example, IEEE 802.3 standards for 100G/400G specify the transmitter and dispersion eye closure (TDECQ) measurement as key pass/fail criteria for 26GBaud and 53GBaud PAM4 optical signaling. See id. The TDECQ measurement involves a 5-tap FFE. Optimization of the FFE taps improves device performance and increases the likelihood that the device will meet the standard specification requirements.

Speeding up this process saves time and reduces costs. On some production lines where the devices under test (DUTs) number in the tens of thousands, it may take seconds to complete a test. Reducing that time to a second or less would increase production and reduce costs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an embodiment of a test and measurement instrument.

FIG. 2 shows an illustration of a transmitter and dispersion eye closure quaternary (TDECQ) measurement.

FIG. 3 shows an example of a process for optimizing FFE taps for a performance measurement.

FIG. 4 shows examples of eye diagrams before and after FFE.

FIG. 5 shows a graphical representation of output FFE taps.

FIG. 6 shows an embodiment of a method of generating tensors from a waveform.

FIG. 7 shows an embodiment of a method of training a machine learning network to perform FFE tap optimization.

FIG. 8 shows an embodiment of a method of using machine learning to provide optimized FFE taps for a complex measurement.

DESCRIPTION

Machine learning techniques can significantly improve the speed of complex measurements such as Transmitter and Dispersion Eye Closure Quaternary (TDECQ) measurements, for example. The measurement speed improvements translate to the improvement of production throughput, for example on a manufacturing line. For the high-speed signal testing, the eye diagram of the signal has been used by machine learning to get measurement results. The full or partial pattern waveform are also used for machine learning for measurement. U.S. patent application Ser. No. 17/747,954, “SHORT PATTERN WAVEFORM DATABASE BASED MACHINE LEARNING FOR MEASUREMENT,” filed May 18, 2022 (referred to here as “Kan”), the contents of which are hereby incorporated by reference, describes an alternative technique of using a short pattern waveform database for machine learning for measurement. Based on the method described in Kan, the embodiments here describe a new method that uses the machine learning to speed up the most time consuming steps in measurements to reduce the overall measurement time.

One should note that the below discussion, for ease or understanding, focuses on 5-tap feed-forward equalizers (FFE), but the techniques described here apply to optimization of any number of equalizer, or filter, taps, for any type of equalizer. Similarly, while the performance measurement used in the below discussion comprises TDECQ measurement, any performance measurement made on equalized waveforms could benefit from application of the embodiments here. The term “equalized waveform” as used here means a waveform after application of an equalizer.

The embodiments here include a test and measurement instrument, such as an oscilloscope used in testing a device under test (DUT). One example discussed below involves a process for testing DUTs comprising optical transceivers or transmitters, with the understanding that the embodiments may apply to any DUT that generates a signal.

FIG. 1 shows an embodiment of a testing setup in the instance of an optical transmitter 14 as a DUT. The testing setup includes a test and measurement system that may include a test and measurement instrument such as an oscilloscope 10. The test and measurement instrument 10 receives, at an input, a signal from the DUT 14, typically through an instrument probe 16. In the case of an optical transmitter, the probe will typically comprise a test fiber coupled to an optical to electrical converter 18 that provides a signal to the test and measurement instrument. The signal is sampled and digitized by the instrument to become an acquired waveform. A clock recovery unit (CRU) 20 may recover the clock signal from the data signal, if the test and measurement instrument 10 comprises a sampling oscilloscope for example. The test and measurement instrument has one or more processors represented by processor 12, a memory 22 and a user interface 26. The memory may store executable instructions in the form of code that, when executed by the processor, causes the processor to perform tasks. The memory may also store one or more acquired waveforms. The user interface 26 of the test and measurement instrument allows a user to interact with the instrument 10, such as to input settings, configure tests, etc. The test and measurement instrument may also include a reference equalizer and analysis module 24.

The embodiments here employ machine learning in the form of a machine learning network 30, such a deep learning network. The machine learning network may include a processor that has been programmed with the machine learning network as either part of the test and measurement instrument, or to which the test and measurement instrument has access. As test equipment capabilities and processors evolve, the processor 12 may include both.

As discussed above, the complex measurement example employing an equalizer comprises the TDECQ measurement using an FFE with five taps. FIG. 2 shows an illustration of a TDECQ measurement. This diagram results from a 5-tap feed forward equalizer (FFE) with one unit interval (1UI) tap spacing optimized to minimize the TDECQ value.

TDECQ value is computed with the following formula:

$\mathrm{TDECQ}=10{\log }_{10}\left(\frac{{\mathrm{OMA}}_{\mathrm{outer}}}{6\times Q_r\times \sqrt{{\sigma }_G^2+{\sigma }_S^2}}\right)$ Where OMA_outer is related to the power of the optical signal. Q_r is a constant value. σ_G² is the standard deviation of a weighted Gaussian noise that can be added to the eye diagram shown in FIG. 2 and still get the larger of symbol error ratio at the two vertical slicers with 0.1UI apart is 4.8e-4. The value of σ_S is the scope or instrument noise recorded when no signal is fed into the O/E module.

A single TDECQ measurement on the compliance pattern SSPRQ (short stress pattern random quaternary) takes seconds to complete using conventional methods. The most time-consuming step in the measurement is the FFE tap adaption. The IEEE specification explicitly defines the process to calculate the TDECQ value with the FFE taps. FIG. 3 shows a block diagram of this process.

The test and measurement instrument having one or more processors receives the waveform 40 and optimizes the FFE tap values at 42 to produce optimized FFE taps 44. This process may employ one of many different methods of determining the optimized taps. The resulting taps improve the eye diagram as shown in FIG. 4. FIG. 4 shows the eye diagram before the FFE on the left and after the FFE on the right. The eye diagram after FFE has a larger eye opening. FIG. 5 shows a graphical representation of the FFE taps.

Returning to FIG. 3, the measurement process 46 applies the optimized taps to the waveform and performs the measurement at 46 in one of many conventional ways. As mentioned above, the measurement could be any measurement based upon a performance requirement, such as a range or specific value for the measurement like the TDECQ value. The measurement value will determine if the DUT meets the performance requirement or fails.

The overall TDECQ measurement on the compliance pattern SSPRQ (short stress pattern random quaternary) can take seconds to complete for each DUT. In the case of a manufacturing line testing tens of thousands of optical transceivers as DUTs, reducing this time has a massive effect on production speeds. As discussed above, the optimization of the taps for a particular waveform takes up the most time of the overall measurement. Therefore, reducing the optimization time will speed up production and lower costs.

The embodiments here use the machine learning network to determine the FFE taps for the waveform and reduce the time to less than a second per DUT. One aspect of this approach uses the short pattern waveform database tensor generator discussed in “Kan” referenced above. FIG. 6 shows an embodiment of that process. The waveform 40 received from the DUT is converted into an array of tensor images, also referred to as a tensor array, by the generator 50. It creates an array of 2D histogram images that cover short lengths or portions of the waveform pattern. Each element of the array of tensors is a different image containing an overlay of multiple instances of a particular short pattern in the waveform 40. The pattern is different for each tensor in the array. For example, one element of the tensor array may be an overlaid image of all instances in the waveform 40 of the short 3-symbol-length pattern of symbols 0, 1, 0, another element of the tensor array may be an overlaid image of all instances in the waveform 40 of the short 3-symbol-length pattern of symbols 0, 2, 0, and so on. FIG. 6 also shows an example of a resulting tensor array 52.

The process obtains optimized FFE taps for each pattern waveform as shown in FIG. 7. The optimized FFE taps are associated with the input tensors as they come from the same pattern waveforms. The input tensors and the corresponding FFE taps as labels become training data to be fed to the machine leaning network 56. The incoming waveform 40 undergoes FFE tap optimization at 42 using any existing method to produce training FFE taps 44 for that waveform. The short pattern waveform database tensor generator 50 produces a training tensor array 52. These are then sent to the machine learning network 56 to train the network to produce optimized filter tap values based upon a tensor array input.

Once the machine learning network has undergone training, it can produce optimized filter tap values much more quickly than conventional methods. FIG. 8 shows an embodiment of a runtime process. The waveform 40 undergoes tensor array generation at the generator 50. The trained machine learning network 56 receives the tensor array 52 and uses it to produce the predicted optimized taps at 58. These taps differ from the previous FFE taps used during training, as discussed above, because they result from the machine learning system, not from conventional methods. The FFE taps are then applied to the waveform and the TDECQ measurement is made by the measurement module 46.

Using machine learning to speed up FFE, DFE and other equalizer adaptions have been explored recently. The embodiments here use different inputs and the output from the machine learning is then used to get the measurement results. The example, as shown in FIG. 8 involves TDECQ measurement, but the same approach can be applied to other measurements, as discussed above.

Aspects of the disclosure may operate on a particularly created hardware, on firmware, digital signal processors, or on a specially programmed general purpose computer including a processor operating according to programmed instructions. The terms controller or processor as used herein are intended to include microprocessors, microcomputers, Application Specific Integrated Circuits (ASICs), and dedicated hardware controllers. One or more aspects of the disclosure may be embodied in computer-usable data and computer-executable instructions, such as in one or more program modules, executed by one or more computers (including monitoring modules), or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The computer executable instructions may be stored on a non-transitory computer readable medium such as a hard disk, optical disk, removable storage media, solid state memory, Random Access Memory (RAM), etc. As will be appreciated by one of skill in the art, the functionality of the program modules may be combined or distributed as desired in various aspects. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits, FPGA, and the like. Particular data structures may be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated within the scope of computer executable instructions and computer-usable data described herein.

The disclosed aspects may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed aspects may also be implemented as instructions carried by or stored on one or more or non-transitory computer-readable media, which may be read and executed by one or more processors. Such instructions may be referred to as a computer program product. Computer-readable media, as discussed herein, means any media that can be accessed by a computing device. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media.

Computer storage media means any medium that can be used to store computer-readable information. By way of example, and not limitation, computer storage media may include RAM, ROM, Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Video Disc (DVD), or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, and any other volatile or nonvolatile, removable or non-removable media implemented in any technology. Computer storage media excludes signals per se and transitory forms of signal transmission.

Communication media means any media that can be used for the communication of computer-readable information. By way of example, and not limitation, communication media may include coaxial cables, fiber-optic cables, air, or any other media suitable for the communication of electrical, optical, Radio Frequency (RF), infrared, acoustic or other types of signals.

Additionally, this written description makes reference to particular features. It is to be understood that the disclosure in this specification includes all possible combinations of those particular features. For example, where a particular feature is disclosed in the context of a particular aspect, that feature can also be used, to the extent possible, in the context of other aspects.

All features disclosed in the specification, including the claims, abstract, and drawings, and all the steps in any method or process disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. Each feature disclosed in the specification, including the claims, abstract, and drawings, can be replaced by alternative features serving the same, equivalent, or similar purpose, unless expressly stated otherwise.

Also, when reference is made in this application to a method having two or more defined steps or operations, the defined steps or operations can be carried out in any order or simultaneously, unless the context excludes those possibilities.

EXAMPLES

Illustrative examples of the disclosed technologies are provided below. An embodiment of the technologies may include one or more, and any combination of, the examples described below.

Example 1 is a test and measurement instrument, comprising: an input configured to receive a signal from a device under test; a memory; a user interface to allow the user to input settings for the test and measurement instrument; and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

Example 2 is the test and measurement instrument of Example 1, wherein the one or more processors are further configured to execute code to determine whether the value indicates that the device under test meets the performance requirement.

Example 3 is the test and measurement instrument of either of Examples 1 or 2, wherein the code that causes the one more processors to apply machine learning comprises code to cause the one or more processors to send the tensor arrays to a machine learning network on a device separate from the test and measurement instrument.

Example 4 is the test and measurement instrument of any of Examples 1 through 3, wherein the code to cause the one or more processors to apply machine learning to the one or more tensor arrays to produce equalizer tap values comprises code to cause the one or more processors to produce feed-forward equalizer tap values for a feed-forward equalizer (FFE).

Example 5 is the test and measurement instrument of any of Examples 1 through 4, wherein the code to cause the one or more processors to perform a measurement on the equalized waveform comprises code to cause the one or more processors to perform a transmitter and dispersion eye closure quaternary (TDECQ) measurement on the equalized waveform to produce the value.

Example 6 is the test and measurement instrument of any of Examples 1 through 5, wherein the one or more processors are further configured to execute code to cause the one or more processors to train a machine learning network, the code to cause the one or more processors to: receive a training waveform; use the training waveform to produce training equalizer tap values; generate one or more training tensor arrays from the training waveform; and provide the one or more training tensor arrays and the training equalizer tap values to the machine learning network as a training data set.

Example 7 is the test and measurement instrument of Example 6, wherein the code to cause the one or more processors to produce training equalizer tap values comprises code to produce training equalizer tap values for a feed-forward equalizer.

Example 8 is the test and measurement instrument of any of Examples 1 through 7, further comprising a probe, wherein the device under test is coupled to the input by the probe.

Example 9 is the test and measurement instrument of Example 8, wherein the probe comprises an optical fiber.

Example 10 is the test and measurement instrument of any of Examples 1 through 9, wherein the probe comprises an optical to electrical converter.

Example 11 is the test and measurement instrument of Examples 8 through 10, wherein the probe is configured to connect to a device operating under IEEE standard 802.3.

Example 12 is a method of testing a device under test, comprising: acquiring a waveform representing a signal received from the device under test; generating one or more tensor arrays based on the waveform; applying machine learning to the one or more tensor arrays to produce equalizer tap values; applying the equalizer tap values to the waveform to produce an equalized waveform; and performing a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

Example 13 is the method of Example 12, further comprising determining whether the value indicates that the device under test meets the performance requirement.

Example 14 is the method of either Examples 12 and 13, wherein applying machine learning to the one or more tensor array to produce equalizer tap values comprises applying machine learning to the one or more tensor arrays to produce feed-forward equalizer tap values.

Example 15 is the method of Example 14, wherein the feed-forward equalizer tap values are for a 5-tap feed forward equalizer.

Example 16 is the method of any of Examples 12 through 15, wherein performing a measurement on the equalized waveform comprises measuring the transmitter and dispersion eye closure quaternary (TDECQ) of the equalized waveform.

Example 17 is the method of any of Examples 12 through 16, further comprising training a machine learning network, the training comprising: receiving a training waveform; using the training waveform to produce training equalizer tap values; and generating one or more training tensor arrays from the training waveform; and providing the one or more training tensor arrays and the training equalizer tap values to the machine learning network as a training data set.

Example 18 is the method of Example 17, wherein using the training waveform to produce training equalizer tap values comprises using the training waveform to produce training equalizer tap values for a feed-forward equalizer.

Example 19 is the method of any of Examples 12 through 18, wherein acquiring the waveform representing the signal received from the device under test comprises receiving an optical signal through a test fiber, the optical signal created by operation of the device under test.

Example 20 is the method of Example of 19, further comprising converting the optical signal to an electrical signal.

Although specific aspects of the disclosure have been illustrated and described for purposes of illustration, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, the disclosure should not be limited except as by the appended claims.

Claims

1. A test and measurement instrument, comprising: an input configured to receive a signal from a device under test;a memory;a user interface to allow a user to input settings for the test and measurement instrument; andone or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test;generate one or more tensor arrays based on the waveform;apply machine learning to the one or more tensor arrays to produce equalizer tap values; andapply equalization to the waveform using the equalizer tap values to produce an equalized waveform; andperform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

2. The test and measurement instrument as claimed in claim 1, wherein the one or more processors are further configured to execute code to determine whether the value indicates that the device under test meets the performance requirement.

3. The test and measurement instrument as claimed in claim 1, wherein the code that causes the one more processors to apply machine learning comprises code to cause the one or more processors to send the tensor arrays to a machine learning network on a device separate from the test and measurement instrument.

4. The test and measurement instrument as claimed in claim 1, wherein the code to cause the one or more processors to apply machine learning to the one or more tensor arrays to produce equalizer tap values comprises code to cause the one or more processors to produce feed-forward equalizer tap values for a feed-forward equalizer (FFE).

5. The test and measurement instrument as claimed in claim 1, wherein the code to cause the one or more processors to perform a measurement on the equalized waveform comprises code to cause the one or more processors to perform a transmitter and dispersion eye closure quaternary (TDECQ) measurement on the equalized waveform to produce the value.

6. The test and measurement instrument 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 train a machine learning network, the code to cause the one or more processors to: receive a training waveform;use the training waveform to produce training equalizer tap values;generate one or more training tensor arrays from the training waveform; andprovide the one or more training tensor arrays and the training equalizer tap values to the machine learning network as a training data set.

7. The test and measurement instrument as claimed in claim 6, wherein the code to cause the one or more processors to produce the training equalizer tap values comprises code to produce the training equalizer tap values for a feed-forward equalizer.

8. The test and measurement instrument as claimed in claim 1, further comprising a probe, wherein the device under test is coupled to the input by the probe.

9. The test and measurement instrument as claimed in claim 8, wherein the probe comprises an optical fiber.

10. The test and measurement instrument as claimed in claim 8, wherein the probe comprises an optical to electrical converter.

11. The test and measurement instrument as claimed in claim 8, wherein the probe is configured to connect to the device under test operating under IEEE standard 802.3.

12. A method of testing a device under test, comprising: acquiring a waveform representing a signal received from the device under test;generating one or more tensor arrays based on the waveform;applying machine learning to the one or more tensor arrays to produce equalizer tap values;applying the equalizer tap values to the waveform to produce an equalized waveform; andperforming a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

13. The method as claimed in claim 12, further comprising determining whether the value indicates that the device under test meets the performance requirement.

14. The method as claimed in claim 12, wherein applying machine learning to the one or more tensor arrays to produce equalizer tap values comprises applying machine learning to the one or more tensor arrays to produce feed-forward equalizer tap values.

15. The method as claimed in claim 14, wherein the feed-forward equalizer tap values are for a 5-tap feed forward equalizer.

16. The method as claimed in claim 12, wherein performing a measurement on the equalized waveform comprises measuring a transmitter and dispersion eye closure quaternary (TDECQ) of the equalized waveform.

17. The method as claimed in claim 12, further comprising training a machine learning network, the training comprising: receiving a training waveform;using the training waveform to produce training equalizer tap values; andgenerating one or more training tensor arrays from the training waveform; andproviding the one or more training tensor arrays and the training equalizer tap values to the machine learning network as a training data set.

18. The method as claimed in claim 17, wherein using the training waveform to produce the training equalizer tap values comprises using the training waveform to produce the training equalizer tap values for a feed-forward equalizer.

19. The method as claimed in claim 12, wherein acquiring the waveform representing the signal received from the device under test comprises receiving an optical signal through a test fiber, the optical signal created by operation of the device under test.

20. The method as claimed in claim 19, further comprising converting the optical signal to an electrical signal.