OPTICAL TUNING TEST SYSTEM USING PARALLEL OVEN PIPELINES WITH PARALLEL INSTRUMENT CHANNELS AND MACHINE LEARNING ASSISTANCE

    公开(公告)号:US20240353491A1

    公开(公告)日:2024-10-24

    申请号:US18756281

    申请日:2024-06-27

    Abstract: A test system includes a test and measurement instrument, ovens to hold devices under test (DUT), each oven having an oven switch selectably connected to the DUTs, channel switches selectably connected to the oven switches and to one channel of the instrument, one or more processors to: select an oven and its oven switch, connect that oven switch to a subset of DUTs in that oven, connect the channel switches to that oven switch to receive signals from the subset of DUTs, send the signals to channels of the instrument to acquire waveforms from the subset of DUTs in parallel, and repeat connecting of the channel switches and that oven switch until the instrument has acquired waveforms from each DUT in that oven, use machine learning to tune each DUT, test whether each DUT in that oven is optimally tuned, and repeat until all DUTs have been tuned and tested.

    Swept parameter oscilloscope
    2.
    发明授权

    公开(公告)号:US12085590B2

    公开(公告)日:2024-09-10

    申请号:US17862293

    申请日:2022-07-11

    Abstract: A test and measurement instrument has a user interface configured to allow a user to provide one or more user inputs, a display to display results to the user, a memory, one or more processors configured to execute code to cause the one or more processors to receive a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recover a clock signal from the waveform array, generate a waveform image for each waveform, render the waveform images into video frames to produce an image array of the video frames, select at least some of the video frames to form a video sequence, and play the video sequence on a display. A method of animating waveform data includes receiving a waveform array containing waveforms resulting from sweeping one or more parameters from a set of parameters, recovering a clock signal from the waveforms, generating a waveform image from each of the waveforms, rendering the waveform images into video frames to produce an image array of the video frames, selecting at least some of the video frames to play as a video sequence, and playing the video sequence on a display.

    Combined TDECQ measurement and transmitter tuning using machine learning

    公开(公告)号:US11940889B2

    公开(公告)日:2024-03-26

    申请号:US17877829

    申请日:2022-07-29

    CPC classification number: G06F11/27 G06F1/022 G06F11/2273

    Abstract: A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

    Machine learning for taps to accelerate TDECQ and other measurements

    公开(公告)号:US11907090B2

    公开(公告)日:2024-02-20

    申请号:US17876817

    申请日:2022-07-29

    CPC classification number: G06F11/2733 G06F11/267

    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.

    Combined higher order statistics and artificial intelligence signal analysis

    公开(公告)号:US11449697B2

    公开(公告)日:2022-09-20

    申请号:US17018198

    申请日:2020-09-11

    Inventor: John J. Pickerd

    Abstract: A test and measurement instrument for analyzing signals using machine learning. The test and measurement instrument can determine a recovered clock signal based on the digital signal, set window positions for a fast Fourier transform of the digital signal, window the digital signal into a series of windowed waveform data based on the window positions, transform each of the windowed waveform data into a frequency-domain windowed waveform data using a fast Fourier transform, and determine high-order spectrum data of each of the frequency-domain windowed waveform data. The test and measurement instrument includes a neural network configured to receive the high-order spectrum data of the frequency-domain windowed transform data and classify each windowed waveform data based on the high-order spectrum data.

    EYE CLASSES SEPARATOR WITH OVERLAY, AND COMPOSITE, AND DYNAMIC EYE-TRIGGER FOR HUMANS AND MACHINE LEARNING

    公开(公告)号:US20220247648A1

    公开(公告)日:2022-08-04

    申请号:US17592437

    申请日:2022-02-03

    Abstract: A system for generating images on a test and measurement device includes a first input for accepting a waveform input signal carrying sequential digital information and an image generator structured to generate a visual image using a segment of the waveform input only when two or more sequential codes of digital information match sequential codes carried in the sequential digital information of the segment of the waveform input. A user-defined state-machine comparator may be used to determine which segments of the waveform input signal are used in the image generation.

    MONO CHANNEL BURST CLASSIFICATION USING MACHINE LEARNING

    公开(公告)号:US20220036238A1

    公开(公告)日:2022-02-03

    申请号:US17386400

    申请日:2021-07-27

    Abstract: A system an input to receive a waveform signal, and one or more processors configured to execute code to cause the one or more processors to extract data bursts from the waveform signal, generate corresponding data vectors from the raw data for each data burst, and use machine learning to classify each data burst from the corresponding data vector. A method of classifying a data burst, comprising receiving an input waveform, extracting data bursts from the input waveform, deriving one or more spectral features of the data bursts, generating corresponding data vectors for each data burst from the one or more spectral features, and using machine learning to classify the data bursts from the corresponding data vectors.

    COMBINED HIGHER ORDER STATISTICS AND ARTIFICIAL INTELLIGENCE SIGNAL ANALYSIS

    公开(公告)号:US20210081630A1

    公开(公告)日:2021-03-18

    申请号:US17018198

    申请日:2020-09-11

    Inventor: John J. Pickerd

    Abstract: A test and measurement instrument for analyzing signals using machine learning. The test and measurement instrument can determine a recovered clock signal based on the digital signal, set window positions for a fast Fourier transform of the digital signal, window the digital signal into a series of windowed waveform data based on the window positions, transform each of the windowed waveform data into a frequency-domain windowed waveform data using a fast Fourier transform, and determine high-order spectrum data of each of the frequency-domain windowed waveform data. The test and measurement instrument includes a neural network configured to receive the high-order spectrum data of the frequency-domain windowed transform data and classify each windowed waveform data based on the high-order spectrum data.

    Apparatus and method for de-embedding a combiner from a balanced signal

    公开(公告)号:US10895588B1

    公开(公告)日:2021-01-19

    申请号:US15958927

    申请日:2018-04-20

    Abstract: A test and measurement system including a plurality of channels and one or more processors. The one or more processors are configured to cause the test and measurement system to receive, via a first channel of the plurality of channels, a positive side of a reference differential signal pair, receive, via a second channel of the plurality of channels, a negative side of the reference differential signal pair, and produce a reference signal based the reference differential signal pair. A combined signal is received, from a combiner, that is a balanced signal produced from the reference differential signal pair. A de-embed filter is generated based on the reference signal and the combined signal and an additional signal is received from the combiner and an effect of the combiner is removed from the additional signal by applying the de-embed filter to the additional signal.

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