Tektronix, Inc.

Kan Tan

John J Pickerd

Pirooz Hojabri

Mark L Guenther

Benjamin A Ward

Maria Agoston

Kalev Sepp

William A Hagerup

Pavel R Zivny

Thomas C Hill

John C Calvin

Sarah R Boen

Rolf P Anderson

Evan Douglas Smith

Heike Tritschler

Sharon M Mc Masters

Ping Qiu

A test and measurement instrument, such as an oscilloscope, having a Nyquist frequency lower than an analog bandwidth, the test and measurement instrument having an input configured to receive a signal under test having a repeating pattern, a single analog-to-digital converter configured to receive the signal under test and sample the signal under test over a plurality of repeating patterns at a sample rate, and one or more processors configured to determine a frequency of the signal under test and reconstruct the signal under test based on the determined frequency of the signal, the pattern length of the signal under test, and/or the sample rate without a trigger.

Real-equivalent-time oscilloscope

A test and measurement device has an input port to receive a signal from a device under test (DUT), the signal having a symbol rate, one or more analog-to-digital converters (ADC) to convert the signal to waveform samples at a sampling rate, and one or more processors, when aliasing is present: up-sample a portion of the signal having aliased samples to produce up-sampled samples; use the up-sampled samples to produce a real-time waveform; perform clock recovery on the real-time waveform to produce a recovered clock; and resample the aliased samples to produce a non-aliased waveform.

Real-equivalent-time clock recovery for a nearly-real-time real-equivalent-time oscilloscope

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.

Eye classes separator with overlay, and composite, and dynamic eye-trigger for humans and machine learning

A test and measurement instrument has one or more input ports to connect the instrument to a device under test (DUT), one or more processors configured to execute code to cause the one or more processors to: receive an equalized waveform and an un-equalized waveform through the input port from the DUT, without any knowledge of a digital pattern that corresponds to the waveforms and without extracting the digital pattern from the waveforms, align the un-equalized waveform and the equalized waveform in time to produce an aligned un-equalized waveform and an aligned equalized waveform, and use the aligned equalized waveform and the aligned un-equalized waveform to determine equalizer tap values.

Transmitter equalizer tap extraction

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.

Bit error ratio estimation using machine learning

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.

Combined TDECQ measurement and transmitter tuning using machine learning

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.

Optical transmitter tuning using machine learning and reference parameters

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.

Machine learning for taps to accelerate TDECQ and other measurements

A test and measurement device includes one or more ports configured to connect to a device under test (DUT), a time domain reflectometry (TDR) source configured receive a source control signal and to produce an incident signal to be applied to the DUT, one or more analog-to-digital converters (ADC) configured to receive a sample clock and sample the incident signal from the TDR source and a time domain reflection (TDR) signal or a time domain transmission (TDT) signal from the DUT to produce an incident waveform and a TDR/TDT waveform, one or more processors configured to execute code to cause the one or more processors to: control a clock synthesizer to produce the sample clock and the source control signal, and use a period of the TDR source, a period of the sample clock, and the number of samples to determine time locations for samples in the incident waveform and the TDR/TDT waveform, and a display configured to display the incident waveform and the TDR/TDT waveform. A method of sampling a waveform using a real-equivalent-time oscilloscope having a time domain reflectometry source, comprising: controlling a clock synthesizer to produce a sample clock and a source control signal; using a time domain reflectometry (TDR) source to receive the source control signal and to produce an incident signal to be applied to a device under test (DUT); receiving the sample clock at one or more analog-to-digital converters (ADC) and sampling the incident signal from the TDR source and a TDR/TDT signal from the DUT to produce an incident waveform and a TDR/TDT waveform; determining time locations for samples in the incident waveform and the TDR/TDT waveform, using a period of the TDR source, a period of the sample clock, and a number of samples; and displaying the incident waveform and the TDR/TDT waveform.

Real-equivalent-time oscilloscope with time domain reflectometer

A method of equalizing a communication link includes setting a number of coefficients to a required number, determining a number of pulse responses for a waveform, setting all values in a set of values to zero, repeating, until all values have been assigned, determining a current lowest parameter, using a position of the current lowest parameter as an index, determining a minimum value between a first term multiplied by a main pulse response minus a summation of each parameter multiplied by each value, divided by the current lowest parameter, and a corresponding pulse response, and assigning the minimum value to the value having a position equal to the position of the current lowest parameter, and determining a value of each coefficient in a set of coefficients by multiplying each value with the sign of a corresponding pulse response; defining an equalizer having a number of taps having a value based on the corresponding coefficient; and applying the equalizer to a waveform.

Explicit solution for DFE optimization with constraints

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.

Low frequency S-parameter measurement

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.

Passive variable continuous time linear equalizer with attenuation and frequency control

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.

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

An apparatus configured to acquire S-parameters of a communications channel includes a physical interface configured to transmit and receive signals through a communications channel under test, a processor, configured to execute instructions that, when executed cause the processor to: send a first data pattern from the transmitter through the communications channel at a first data rate; acquire a first waveform corresponding to the first data pattern and determine a first pulse response; calculate a first transfer function from the first pulse response; send a second data pattern from the transmitter through the communications channel at a second data rate; acquire a second waveform corresponding to the second data pattern and determine a second pulse response; calculate a second transfer function from the second pulse response; and combine the first and second transfer functions to determine an S-parameter of the communications channel.

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Portland, OR, United States of America

Kan Tan