Abstract: A test system includes a test and measurement instrument, ovens to hold devices under test (DUT), each oven having an oven switch selectably connected to the DUTs, channel switches selectably connected to the oven switches and to one channel of the instrument, one or more processors to: select an oven and its oven switch, connect that oven switch to a subset of DUTs in that oven, connect the channel switches to that oven switch to receive signals from the subset of DUTs, send the signals to channels of the instrument to acquire waveforms from the subset of DUTs in parallel, and repeat connecting of the channel switches and that oven switch until the instrument has acquired waveforms from each DUT in that oven, use machine learning to tune each DUT, test whether each DUT in that oven is optimally tuned, and repeat until all DUTs have been tuned and tested.

Abstract: A test and measurement instrument connects to one or more devices under test (DUT) having tuning screws, and to a robot, and has one or more processors to: command the robot to position the tuning screws on the DUTs to one or more sets of positions, each set of positions being a parameter set for the tuning screws, acquire a set of operating parameters for each parameter set from the DUTs, generate a parameter set image for each set, create a combined image of the parameter set images, provide the combined image to a machine learning system to obtain a predicted set of values, adjust the predicted set of values to produce a set of predicted positions, command the robot to position the tuning screws to positions in the set of predicted positions, obtain a set of tuned operating parameters from the DUTs, and validate operation of the DUTs.

Abstract: A test and measurement system includes a machine learning system configured to communicate with a test automation system, a user interface configured to allow a user to provide one or more user inputs and to provide results to the user, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive one or more user inputs through the user interface, the one or more user inputs at least identifying a selected machine learning system configuration to be used to configure the machine learning system, receive a waveform created by operation of a device under test, apply the configured machine learning system to analyze the waveform, and provide an output of predicted metadata about the waveform.

Abstract: In an automated defect detection and classification system, one or more computing devices access scan data acquired in an ultrasonic scan of an object. A first input feature map, including a two-dimensional (2D) scan image, is built from the scan data and input to a first deep neural network to generate a first output feature map. A second input feature map, including an image of a defect-free object, is input to a second deep neural network, having the same structure and weight values as first deep neural network, to produce a second output feature map. The scanned object is determined to contain a defect when a distance between first and second output feature maps is large. In an alternative approach, the 2D scan image and one or more images of the defect-free object are input to different channels of neural network trained using color images.

Abstract: A test and measurement instrument includes a port to allow the test and measurement instrument to connect to a device under test (DUT) to receive signals from the DUT, one or more analog-to-digital converters (ADCs) to receive a signal from the DUT and convert the signal to one or more digital waveforms, a user interface to allow a user to enter a query, and one or more processors configured to execute code that causes the one or more processors to: build one or more images of the one or more digital waveforms from the one or more ADCs, send the one or more images to a domain-adapted multimodal large language model (MLLM), receive parameters from the domain-adapted MLLM, provide the user with parameters for the DUT in response to the query, and apply the parameters to the DUT.

Abstract: A power monitoring system includes one or more power vector analyzers, and a power controller having one or more ports to receive transient event data comprising one or more power images and associated metadata for a transient event from the one or more power vector analyzers, and one or more processors configured to execute code to cause the one or more processors to convert the one or more power images from the one or more power vector analyzers and the associated metadata to one or more transient event vectors, and store the one or more transient event vectors in a vector database.

Abstract: 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.

Abstract: A test and measurement system has a test and measurement instrument that includes a connection to a device under test (DUT); one or more analog-to-digital converters (ADCs) to receive and convert a signal from the DUT to one or more digital waveforms; and one or more processors to: receive the one or more digital waveforms corresponding to one set of tuning parameters applied to the DUT; build one or more image tensors of the one or more digital waveforms; use an artificial intelligence embedding model that generates one or more text strings from metadata and embeds the metadata and the one or more image tensors into a vector; access a vector database; receive a set of indexes having a number of indexes corresponding to a number of matches; use the set of indexes to find one or more sets of optimal tuning parameters; and validate operation of the DUT.

Abstract: 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.

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 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 universal power probe fixture (UPPF) that is configured to be installed into a power signal path between a source device and a load device has one or more UPPF base modules, each UPPF base module including an input terminal block, an output terminal block, and a power transfer circuit including a multiple signal lines electrically connected between the input terminal block and the output terminal block, the signal lines structured to convey high power, and each of the signal lines includes a current probe connection point and at least one voltage probe connection point. The UPPF also has a source device connector adapted to electrically connect the source device to the input terminal block, and a load device connector adapted to electrically connect the load device to the output terminal block. A test system using the UPPF, and an application-specific electric vehicle motor probe adapter are also disclosed.

Abstract: A power vector analyzer to analyze power from a device under test (DUT) includes one or more channels to measure a reference voltage signal from a power line connected to the DUT, one or more channels to measure a reference current signal from the power line, a user interface comprising a display and one or more controls, and a quadrature synchronous detector (QSD) for each phase of apparent power being measured, the QSD configured to use a reference voltage signal from the one or more channels and a reference current signal from the one or more channels to determine the apparent power for each phase of power being measured by the DUT and display the apparent power for each phase on the display.

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 test and measurement instrument includes one or more ports to allow the test and measurement instrument to receive a signal from a device under test (DUT), a user interface to allow the user to send inputs to the test and measurement instrument and receive results, and one or more processors configured to acquire the signal from the DUT, make measurements on the signal to create a decimated measurement set, convert the decimated measurement set into a tensor, send the tensor to a machine learning network, and receive a pass/fail value from the machine learning network. A method includes acquiring a signal from a device under test (DUT), making measurements on the signal to create a decimated measurement set, convert the decimated measurement set into a tensor, sending the tensor to a machine learning network, and receiving a pass/fail value from the machine learning network.

Abstract: A margin tester includes one or more ports to allow the margin tester to connect to a device under test (DUT), a memory, the memory containing a margin tester signature, a transmitter, a receiver to receive signals from the DUT, one or more processors configured to execute code that causes the one or more processors to: receive multiple signals from the receiver through the one or more ports, generate a performance indicator from the multiple signals, send the performance indicator and the margin tester signature to one or more machine learning networks, and receiving a result from the one or more machine learning networks containing a performance measurement prediction for the DUT.

Abstract: A test and measurement instrument has a port to receive a signal from a device under test (DUT), one or more processors configured to execute code that causes the one or more processors to acquire a waveform from the signal, derive a pattern waveform from the waveform using one of either hardware or software clock recovery, perform linear fit pulse response (LFPR) extractions on the pattern waveform to extract more than one LFPR, determine a reference pulse response from the more than one LFPRs, compare at least one of the LFPRs to the reference pulse response to determine a difference, and tune the DUT to reduce the difference. The test and measurement instrument may also use the multiple LFPRs as an input to a machine learning network to perform measurement predictions for the DUT.

Abstract: A test and measurement system includes a first test and measurement instrument having an input to allow the test and measurement instrument to receive signals from one or more devices under test (DUT), and one or more digitizers to convert the signals from the one or more DUTs to digital waveforms, a machine learning network, and one or more processors to: perform one or more measurements of the digital waveforms, send the one or more measurements of the digital waveforms to the machine learning network as an input, use the machine learning network to translate the one or more measurements to measurements made by a reference instrument to produce one or more translated measurements, the reference instrument being more accurate than the first test and measurement instrument, and determine whether the DUT meets a performance requirement based upon the one or more translated measurements.

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.

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.