Tektronix, Inc.

Sonix, Inc.

Tektronic, Inc.

John J Pickerd

Kan Tan

Pirooz Hojabri

Daniel G Knierim

Richard A Booman

William A Hagerup

Patrick Satarzadeh

Rolf P Anderson

Heike Tritschler

Evan Douglas Smith

Barton T Hickman

William Q Law

Que Thuy Tran

Gregory A Martin

Thomas C Hill

Benjamin A Ward

Maria Agoston

P E Ramesh

Kalev Sepp

Laudie J Doubrava

Jeffrey J Trgovich

Sampathkumar R Desai

Sarah R Boen

Marvin E La Voie

Scott A Davidson

Saifee F Jasdanwala

Williams Fabricio Flores Yepez

Kevin Ryan

Parthasarathy Raju M

Sam J Strickling

Khadar Baba Shaik

Sharon M Mc Masters

Ping Qiu

Thomas F Lenihan

Paul H Buckle

Justin E Patterson

In a method and apparatus for automated inspection, an image is acquired of an object under inspection and a difference image is generated showing the difference between the acquired image and a reference image of a defect-free object of the same type. Characteristics of the difference image, or detected isolated regions of the difference image, are passed to an automated defect classifier to classify defects in the object under inspection. The characteristics of the difference image may be pixels of the difference image or features determined therefrom. The features may be extracted using a neural network, for example. The automated defect classifier is trained using difference images and may be further trained, in operation, based on operator classifications and using simulated images of defects identified by an operator.

Method and apparatus for automated defect detection

A test system has ovens configured to hold devices under test (DUTs), DUT switches, each connected to the DUTs in an oven, splitters, each splitter connected to a DUT switch, an instrument switch connected to one output of each splitter, the other output of each splitter connected to a test instrument, and one or more processors to control the instrument switch to select one of the DUT switches connected to an oven, control the selected DUT switch to connect each DUT in the oven to a channel of the test and measurement instrument, use machine learning to tune the DUT to a set of parameters until the DUT passes or fails, repeat the connecting, tuning, and testing of each DUT until all DUTs in an oven have been tested, and repeat the selection and control of the DUT switches until each DUT in each oven has been tuned and tested.

Tuning a device under test using parallel pipeline machine learning assistance

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. A method of waveform classification using a cyclic loop image includes receiving an input waveform, receiving a selection of a segment of the input waveform, transforming the segment of the input waveform into cyclic loop image data, the transforming comprising generating a horizontal ramp sweep signal based on edge transitions in the input waveform, and storing the cyclic loop image data in a memory; and sending the cyclic loop image data to a machine learning system to determine an attribute of the input waveform.

System and method for multi-level signal cyclic loop image representations for measurements and machine learning

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 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.

System and method for separation and classification of signals using cyclic loop images

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 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.

Cyclic loop image representation for waveform data

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.

Swept parameter oscilloscope

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 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. A method to tune optical transceivers includes connecting a transceiver to a test and measurement device, setting operating parameters for the transceiver to an average set of parameters, acquiring a waveform from the transceiver, measuring the waveform to determine if the transceiver passes or fails, sending the waveform and operating parameters to a machine learning system when the transceiver fails, using the machine learning system to provide adjusted operating parameters, setting the operating parameters to the adjusted parameters, and repeating the acquiring, measuring, sending, using, and setting until the transceiver passes.

Optical transceiver 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 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 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.

Combined higher order statistics and artificial intelligence signal analysis

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.

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Hillsboro, OR, United States of America

John J Pickerd