Tektronix, Inc.

Maria Agoston

Pavel R Zivny

Kan Tan

John J Pickerd

Laszlo J Dobos

Kenneth J Lester

Ronald M Henricksen

William Bruce Harrington

Scott L Halsted

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

Methods and systems are described for analyzing signal impairments using a test and measurement instrument. A method may include decomposing aggregate signal impairments into signal impairments that are correlated and uncorrelated to an acquired data pattern. The uncorrelated signal impairments may be further decomposed into periodic signal impairments (e.g., PJ) and non-periodic uncorrelated signal impairments. A PDF of the non-periodic uncorrelated signal impairments may be mathematically integrated, thereby producing an estimated cumulative distribution function (CDF) curve. Random signal impairments may be estimated as an unbound Gaussian distribution. The CDF curve of the non-periodic uncorrelated signal impairments and the unbound Gaussian distribution may be plotted in Q-space on a display device. Non-periodic bounded uncorrelated signal impairments (e.g., NP-BUJ) PDF may then be isolated. Bounded uncorrelated signal impairments PDF may then be synthesized. Complete uncorrelated signal impairments PDF may be synthesized. A synthesis of the decomposed components can be performed at a user-defined bit error rate to generate the total estimated jitter (e.g., TJ@BER or TN@BER).

Bounded uncorrelated signal impairment deconvolution for total signal impairment extrapolation

Method and systems are described for estimating signal impairments, in particular jitter that includes uncorrelated, non-periodic signal impairments. One system may take the form of an oscilloscope. The estimates may take the form of a probability density function (PDF) for uncorrelated signal impairments that has been modified to replace low probability regions with a known approximation and an extrapolation of the known approximation.

Methods and systems for analyzing decomposed uncorrelated signal impairments

A digitizing instrument is used for modifying pattern data and jitter and noise components of a communication signal. In a typical implementation, the midpoints of a rising edge slope and horizontal portion of the communication signal are determined and multiple digital data records are acquired at the midpoints. The data sample records are transformed to frequency components and the random jitter and noise, and periodic jitter and noise components are determined. A correlated pattern data and the jitter and noise components are matrix elements in a simulated signal channel having communication system elements. Each correlated pattern data and jitter and noise component may be modified for each of the communication system element. The selectively modified correlated pattern data and jitter and noise components are combined to produce a modified communication signal that is displayed as a numeric table, eye diagram or bit error rate presentation.

System for independently modifying jitter and noise components in a signal digitizing instrument

Parameters of a spread spectrum clock signal in a communication signal are characterized by acquiring voltage samples of the communication signal at a nominal time location of an edge of the communication signal. The voltage samples are converted to time samples and the difference between the maximum and minimum time values is determined at the nominal time location. A spread spectrum clock magnitude number is generated by dividing the difference between the maximum and minimum time values by the nominal time location of the acquired voltage samples of the spread spectrum clock signal. A spread spectrum modulation profile of a spread spectrum clock signal is estimated by over sampling the time samples using an aliased index value to generate over sampled triangular waveforms representing the spread spectrum clock modulation profile. One of the over sampled triangular waveforms is use to generate the spread spectrum clock modulation profile.

Method of characterizing parameters and removing spectral components of a spread spectrum clock in a communications signal

A variable persistence waveform database is generated by defining a First-In-First-Out (FIFO) time-ordered queue buffer for maintaining a selected number of waveform records. Digital data samples of a measurement signal are acquired and stored in a plurality of waveform records in the FIFO time-ordered queue buffer. Counts of the digital data samples for the plurality of waveform records are accumulated in a waveform database. Upon filling FIFO time-ordered queue buffer with waveform records, the digital data samples of the oldest acquired waveform record are subtracted from the accumulated counts of the waveform database and deleted from the FIFO time-ordered queue buffer and the digital data samples of the newest acquired waveform record are stored in the FIFO time-ordered queue buffer and added to the accumulated counts in the waveform database.

Method of generating a variable persistence waveform database

A method for reducing drift in a step stimulus from a sampling system, such as an oscilloscope, is described having an initial step of setting a calibration repetition rate. A step stimulus stabilization calibration is performed to acquire a reference mid-crossing time and the current mid-crossing time. Acquisition cycles of TDR/TDT waveform samples are acquired equal to the repetition rate with the initial strobe delay interval in the sampling system being adjusted by the difference between the reference mid-crossing time and the current mid-crossing time. The step stimulus stabilization calibration is performed again to acquire a new current mid-crossing time and initial strobe delay interval and more acquisition cycles of TDR waveform samples are acquired equal to the repetition rate. The process continues until a stop command is activated for the sampling system.

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Beaverton, OR, United States of America

Maria Agoston