公开(公告)号：US20240028002A1

公开(公告)日：2024-01-25

申请号：US18353844

申请日：2023-07-17

Applicant: Tektronix, Inc.

Inventor： Wenzheng Sun ,  Evan Douglas Smith ,  John J. Pickerd

IPC: G05B19/418

CPC classification number: G05B19/4183 ,  G05B19/41885

Abstract: A test and measurement system includes a test and measurement instrument configured to receive waveform data from a device under test (DUT) on a manufacturing line, a machine learning system connected to the test and measurement instrument, and one or more processors configured to execute code that causes the one or more processors to: collect optimal tuning parameter data sets from the DUT after the DUT is tuned on the manufacturing line, determine one or more parameter data sets from the optimal tuning parameter data, load the one or more parameter data sets into the DUT, collect waveform data from the DUT for the one or more parameter data sets as training data sets, train the machine learning system using the training data sets, and use the machine learning system after training to produce an output related to the DUT.

公开(公告)号：US20230228803A1

公开(公告)日：2023-07-20

申请号：US18094947

申请日：2023-01-09

Applicant: Tektronix, Inc.

Inventor： Wenzheng Sun ,  Pavel R. Zivny

IPC: G01R31/26 ,  G06N3/08

CPC classification number: G01R31/2603 ,  G06N3/08

Abstract: A test and measurement system has one or more inputs connectable to a device under test (DUT), and one or more processors configured to execute code that causes the one or more processors to: gather a set of training waveforms by acquiring one or more waveforms from one or more DUTs or from simulated waveforms, remove noise from the set of training waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network. A method of training a neural network having receiving one or more waveforms from one or more DUTs, or generating one or more waveforms from a waveform simulator, removing noise from a set of training waveforms gathered from the one or more waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network.

公开(公告)号：US20240393918A1

公开(公告)日：2024-11-28

申请号：US18665258

申请日：2024-05-15

Applicant: Tektronix, Inc.

Inventor： John J. Pickerd ,  Kan Tan ,  Wenzheng Sun

IPC: G06F3/0482 ,  G06F3/0484

Abstract: A test and measurement instrument includes one or more ports to allow the test and measurement instrument to receive data from a device under test (DUT), a connection to a machine learning network, a display configured to display a user interface, one or more controls to allow the test and measurement instrument to receive inputs from a user, and one or more processors configured to execute code that causes the one or more processors to: render a menu on the display that displays different types of tensors, receive, from the one or more controls, a user selection that identifies a selected type of tensor, and build the selected type of tensor from the data from the DUT and send the selected type of tensor to the machine learning network. A method of providing a user interface is also disclosed.

公开(公告)号：US20230222382A1

公开(公告)日：2023-07-13

申请号：US18081616

申请日：2022-12-14

Applicant: Tektronix, Inc.

Inventor： Wenzheng Sun ,  Xiaolan Wang ,  Justin E. Patterson

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A system to develop and test machine learning models has a waveform emulator machine learning system, a user interface to allow a user to input one or more design parameters for the waveform emulator machine learning system, one or more processors configured to execute code to cause the one or more processors to: send the one or more design parameters to the waveform emulator machine learning system; receive one or more data sets from the waveform emulator machine learning system, the one or more data sets based on the one or more design parameters; train a developed machine learning model using at least one of the one or more data sets, resulting in a trained machine learning model; validate the trained machine learning model using a previously unused one of the one or more data sets; adjust the trained machine learning model as needed; and repeat the training, validating, and adjusting until an optimal machine learning model is trained. A method of developing and testing machine learning models includes providing one or more design parameters to a waveform emulator machine learning system, receiving one or more data sets from the waveform emulator machine learning system, training a developed machine learning model using at least one of the one or more data sets, resulting in a trained machine learning model, validating the trained machine learning model using a previously unused one of the one or more data sets, adjusting the trained machine learning model as needed, and repeating the training, validating, and adjusting until an optimal machine learning model is trained.