Abstract: A test and measurement system includes a machine learning system, a test and measurement device including a port configured to connect the test and measurement device to a device under test (DUT), and one or more processors, configured to execute code that causes the one or more processors to: acquire a waveform from the device under test (DUT),transform the waveform into a composite waveform image, and send the composite waveform image to the machine learning system to obtain a bit error ratio (BER) value for the DUT. A method of determining a bit error ratio for a device under test (DUT), includes acquiring one or more waveforms from the DUT, transforming the one or more waveforms into a composite waveform image, and sending the composite waveform image to a machine learning system to obtain a bit error ratio (BER) value for the DUT.

Abstract: A test and measurement system includes a clock recovery circuit configured to receive a signal from a device under test and to produce a pattern trigger signal, a flash array digitizer having an array of counters having rows and columns configured to store a waveform image representing the signal received from the device under test, a row selection circuit configured to select a row in the array of counters, and a ring counter circuit configured to receive a clock signal, select a column in the array of counters, produce end of row signals, and produce a fill complete signal upon all of the columns having been swept, the fill complete signal indicating completion of the waveform image, an equivalent time sweep logic circuit configured to receive the pattern trigger signal and the end of row signals from the ring counter and to produce the clock signal with a delay to increment a clock delay to the ring counter until the fill complete signal is received, and a machine learning system configured to receive the

Abstract: A test and measurement device has a connection to allow the test and measurement device to connect to an optical transceiver, one or more processors, configured to execute code that causes the one or more processors to: initially set operating parameters for the optical transceiver to average parameters, acquire a waveform from the optical transceiver, measure the acquired waveform and determine if operation of the transceiver passes or fails, send the waveform and the operating parameters to a machine learning system to obtain estimated parameters if the transceiver fails, adjust the operating parameters based upon the estimated parameters, and repeat the acquiring, measuring, sending, and adjusting as needed until the transceiver passes.

Abstract: A test and measurement system includes 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; 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; and test the optical transceiver using the predicted operating parameters.

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.

Abstract: A test and measurement system includes a primary instrument having an input for receiving a test signal for measurement or analysis from a Device Under Test (DUT) and generating a test waveform from the test signal, and a duplicator for sending a copy of the test waveform to one or more secondary instruments. The one or more secondary instruments are each structured to access the copy of the test signal for analysis, and each of the one or more secondary instruments includes a receiver structured to receive a command related to measurement or analysis of the copy of the test waveform, one or more processes for executing the received command, and an output for sending results of the executed command to be displayed on a user interface that is separate from any user interface of the one or more secondary instruments.

Abstract: A test and measurement instrument for generating an analog waveform, including an interpolator configured to receive a digital signal and output interpolated samples of the digital signal at a sample rate, a filter modulation controller configured to output first filter coefficients at a first time and second filter coefficients at a second time, a convolver configured to generate a convolved signal by convolving the interpolated samples of the digital signal and the first filter coefficients and convolving the interpolated samples of the digital signal and the second filter coefficients; and a digital-to-analog converter configured to convert the convolved signal to an analog signal based on a fixed, constant clock signal.

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.

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.

Abstract: A system to classify signals includes an input to receive incoming waveform data; a memory, and one or more processors configured to execute code to cause the one or more processors to: generate a ramp sweep signal from the incoming waveform data, locate a data burst in the incoming waveform data using a burst detector, receive a signal from the burst detector to cause the memory to store cyclic loop image data in the form of the incoming waveform data as y-axis data and the ramp sweep signal as x-axis data, and employ a machine learning system to receive the cyclic loop image data and classify the data burst. A method of classifying signals includes generating a ramp sweep signal from incoming waveform data, locating a data burst in the incoming waveform data, storing cyclic loop image data for the data burst in the form of the incoming waveform data as Y-axis data and the ramp sweep signal as X-axis data, and using a machine learning system to receive the cyclic loop image and classify the data burst.

Abstract: A system includes an input to receive a digital waveform signal, a memory, and one or more processors configured to execute code to cause the one or more processors to: generate a horizontal ramp sweep signal based on the digital waveform signal; receive a selection input to identify a segment of the digital waveform signal; gate the horizontal ramp sweep signal and the digital waveform signal based on the selection input to produce cyclic loop image data for the segment of the digital waveform; store the cyclic loop image data in the memory; and provide the cyclic loop image data as one or more inputs into a machine learning system.

Abstract: A test and measurement instrument includes an input to receive a non-return-to-zero (NRZ) waveform signal from a device under test, a ramp generator to use the NRZ waveform signal to generate a ramp sweep signal, a gate to gate the ramp sweep signal and the NRZ waveform signal to produce gated X-axis and Y-axis data, and a display to display the gated X-axis and Y-axis data as a cyclic loop image. A method of generating a cyclic loop image includes receiving an input waveform, using the input waveform to generate a ramp sweep signal, gating the ramp sweep signal and the input waveform to produce gated X-axis and Y-axis data, and displaying the gated X-axis and Y-axis data as a cyclic loop image.

Abstract: A test and measurement instrument including a digital-to-analog converter having an output sample rate configured to receive a digital sample waveform and a reference clock and output an analog waveform at the sample rate, a waveform synthesizer configured to receive an input waveform having a baud rate and output a digital sample waveform having a baud rate less than the sample rate of the digital-to-analog converter, and a port configured to output the analog waveform.

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.

Abstract: A continuously or step variable passive noise filter for removing noise from a signal received from a DUT added by a test and measurement instrument channel. The noise filter may include, for example, a splitter splits a signal into at least a first split signal and a second split signal. A first path receives the first split signal and includes a variable attenuator and/or a variable delay line which may be set based on the channel response of the DUT which is connected. The variable attenuator and/or the variable delay line may be continuously or stepped variable, as will be discussed in more detail below. A second path is also included to receive the second split signal and a combiner combines a signal from the first path and a signal from the second path into a combined signal.

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.

Abstract: A method determines scattering parameters, S-parameters, for a device under test for a first frequency range. The method includes receiving S-parameters for the device under test for a second frequency range, the second frequency range greater than the first frequency range. Generally, the S-parameters for the device under test for the second frequency range can be determined using known methods. The method further includes measuring an actual response of the device under test, determining a desired signal of the device under test, and determining the S-parameters for the device under test for the first frequency range based the S-parameters for the second frequency range, actual response of the device under test and the desired signal of the device under test.

Abstract: A test and measurement instrument for generating an analog waveform, including an interpolator configured to receive a digital signal and output interpolated samples of the digital signal at a sample rate, a filter modulation controller configured to output first filter coefficients at a first time and second filter coefficients at a second time, a convolver configured to generate a convolved signal by convolving the interpolated samples of the digital signal and the first filter coefficients and convolving the interpolated samples of the digital signal and the second filter coefficients; and a digital-to-analog converter configured to convert the convolved signal to an analog signal based on a fixed, constant clock signal.

Abstract: A test and measurement instrument including a digital-to-analog converter having an output sample rate configured to receive a digital sample waveform and a reference clock and output an analog waveform at the sample rate, a waveform synthesizer configured to receive an input waveform having a baud rate and output a digital sample waveform having a baud rate less than the sample rate of the digital-to-analog converter, and a port configured to output the analog waveform.

Abstract: Disclosed are systems and methods related to a noise reduction device employing an analog filter and a corresponding inverse digital filter. The combination and placement of the filters within the systems aids in reducing noise introduced by processing the signal. In some embodiments, the combination of filters may also provide for increased flexibility when de-embedding device under test (DUT) link attenuation at higher frequencies. Further, the filters are adjustable, via a controller, to obtain an increased signal to noise ratio (SNR) relative to a signal channel lacking the combination of filters. Additional embodiments may be disclosed and/or claimed herein.