Abstract: A method of securing a probe tip to a device under test (DUT), the method comprising: positioning the probe tip near a test point of the DUT, the probe tip comprising a connection point on a signal-path portion of the probe tip and an attachment tab, the connection point making an electrical connection with the test point of the DUT, the attachment tab extending away from the signal-path portion of the probe tip; applying an adhesive to the DUT through a hole in the attachment tab of the probe tip; and curing the adhesive to secure the probe tip to the DUT.

Abstract: A test and measurement instrument includes one or more processors to execute code to cause the processors to: access a user instance of the test and measurement instrument; receive one or more requests from the user instance of the test and measurement instrument; determine any collisions between the one or more requests and any other requests for elements of the test and measurement instrument; resolve any collisions as necessary; perform one or more operations to fulfill the request; and display information resulting from the one or more operations on an instance user interface.

Abstract: A test and measurement system includes a test and measurement instrument, including a port to receive a signal from a device under test (DUT), and one or more processors, configured to execute code that causes the one or more processors to: adjust a set of operating parameters for the DUT to a first set of reference parameters; acquire, using the test and measurement instrument, a waveform from the DUT; repeatedly execute the code to cause the one or more processors to adjust the set of operating parameters and acquire a waveform, for each of a predetermined number of sets of reference 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 optimal operating parameters; adjust the set of operating parameters for the DUT to the predicted optimal operating parameters; and determine whether the DUT passes a predetermined performance measurement when adjusted to the set of predicted optimal operating parameters.

Abstract: A method of training a machine learning system to determine operating parameters for optical transceivers includes connecting the transceiver to a test and measurement device, tuning the transceiver with a set of parameters, capturing a waveform from the transceiver, sending the waveform and the set of parameters to a machine learning system, and repeating the tuning, capturing, and sending until a sufficient number of samples are gathered.

Abstract: A test and measurement instrument for determining magnetic core losses of a device under test during in circuit operation. The test and measurement instrument receives a primary current signal from a primary winding of a device under test and receives a primary voltage signal measured across a magnetic core of the device under test. Based on the primary electric current signal and the primary voltage signal, the test and measurement instrument determines a magnetic loss of the device under test. In some examples, the test and measurement instrument can use primary and secondary voltage and current inputs to determine the magnetic loss of the device under test. The magnetic loss of the device under test can be displayed on a display of the test and measurement instrument. The magnetic loss can include a total magnetic loss, a hysteresis loss, a copper loss, and/or other losses.

Abstract: An equivalent-time sampling test and measurement instrument for acquiring a repeating pattern signal under test at or near a maximum sampling speed of the test and measurement instrument. The test and measurement instrument includes a first input configured to receive repeating pattern information about a signal under test, a second input configured to receive the signal under test, one or more processors configured to determine an optimized trigger holdoff period that is set based on a minimum trigger holdoff period of a test and measurement instrument, and an acquisition unit configured to acquire a portion of the signal under test every optimized trigger holdoff period.

Abstract: A machine learning management system includes a repository having one or more partitions, the one or more partitions being separate from others of the partitions, a communications interface, and one or more processors configured to execute code to: receive a selected model and associated training data for the selected model through the communications interface from a customer; store the selected model and the associated training data in a partition dedicated to the customer; and manage the one or more partitions to ensure that the customer can only access the customer's partition. A method includes receiving a selected model and associated training data for the selected model from a customer, storing the selected model and the associated training data in a partition dedicated to the customer in a repository, and managing the one or more partitions to ensure that the customer can only access the partition dedicated to the customer.

Abstract: A test and measurement device includes an input port for receiving a bus conducting data from a device under test, and processing element coupled to the input port. The processing element is configured to execute instructions that cause the processing element to determine a data sequence from a signal of the bus received on a main channel of the device, and use information from at least one other signal of the bus on an auxiliary channel of the device based upon a protocol associated with the bus to adjust parameters for performing error detection on the data sequence.

Abstract: A test system includes a repository of component models containing characteristic parameters for each component model, one or more processors to receive a list of selected component models through a user interface to be tested as a combination, access the characteristic parameters for each selected component model, build a tensor image using the characteristic parameters, send the tensor image to one or more trained neural networks to predict interoperability of the combination, and receive a prediction about the combination. A method includes receiving a list of selected component models through a user interface to be tested as a combination, accessing characteristic parameters for the selected component models, building a tensor image for each combination of the selected component models, sending the tensor image to one or more trained neural networks to predict interoperability of the combination, and receiving a prediction about the combination.

Abstract: A test and measurement instrument includes a port to connect to a device under test (DUT) to receive waveform data, a connection to a machine learning network, and one or more processors configured to: receive one or more inputs about a three-dimensional (3D) tensor image; scale the waveform data to fit within the 3D tensor image; build the 3D tensor image; send the 3D tensor image to the machine learning network; and receive a predictive result from the machine learning network. A method includes receiving waveform data from one or more device under test (DUT), receiving one or more inputs about a three-dimensional (3D) tensor image, scaling the waveform data to fit within the 3D tensor image, building the 3D tensor image, sending the 3D tensor image to a pre-trained machine learning network, and receiving a predictive result from the machine learning network.

Abstract: 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.

Abstract: A waveform generator includes a carrier band generator to produce a carrier signal, one or more selectable frequency multipliers to receive the carrier signal and to output a selected carrier signal having a frequency of a multiple of the carrier signal, at least two main digital-to-analog converters (DACs), each main DAC to receive a digital in-phase (I) or quadrature (Q) signals, and to convert the digital I and Q signals to analog I and Q signals in accordance with a control signal, at least two offset DACs, each offset DAC to receive the digital I or Q signals to convert the digital I and Q signals to analog I and Q signals in accordance with the control signal, a mixer to mix the analog I and Q signals with the selected carrier signal to produce an output signal, and a variable filter configured to produce a filtered output signal.

Abstract: A manufacturing system has a machine learning (ML) system having one or more neural networks and a configuration file associated with a trained neural network (NN), a structured data store having interfaces to the ML system a test automation application, a training store, a reference parameter store, a communications store, a trained model store, and one or more processors to control the data store to receive and store training data, allow the ML system to access the training data to train the one or more NNs, receive and store reference parameters and to access the reference parameters, receive and store prediction requests for optimal tuning parameters and associated data within the communication store, to provide requests to the ML system, allow the ML system to store trained NNs in the trained models store, and to recall a selected trained NN and provide the prediction to the test automation application.

Abstract: A machine learning network has a plurality of test and measurement devices, one or more of the test and measurement devices has one or more communication interfaces configured to allow the device to receive and process physical layer signals, a memory, and one or more processors configured to execute code to cause the one or more processors to receive physical layer data, perform one or more operations on the physical layer data according to a machine learning model to produce changed physical layer data, and transmit the changed physical layer data to at least one other node in the machine learning neural network. The machine learning network may include a learner node.

Abstract: A test and measurement instrument includes an input configured to receive an input signal from a device under test (DUT), an output display, and one or more processors configured to execute code that causes the one or more processors to measure a noise component of the input signal, compensate the measured noise component based on the measurement population and a relative amount of noise generated by the test and measurement instrument and a total noise measurement, and produce the compensated measured noise component as a noise measurement on the output display. Methods are also described.

Abstract: A cable assembly has a connector to receive a signal, a cable connected to the connector, the cable having a length and one or more conductors along at least part of the length to conduct the signal, a magnetic material external to the one or more conductors, and an elastomer material external to the one or more conductors. A cable assembly has a connector to receive a differential signal, a cable connected to the connector having symmetric pair conductors, one or more discrete magnetic components spaced along the length of the cable, and one or more elastomer components next to at least one of the one or more magnetic components. A cable assembly has a connector to receive a differential signal, a cable connected to the connector having symmetric pair conductors, an elastomer material at least partially enclosing the cable, and a magnetic material at least partially enclosing the cable.

Abstract: A customizable safety enclosure for a device under test (DUT) includes a plurality of non-conductive panels to enclose the DUT, and a plurality of non-conductive removeable corner joints each configured to secure a corner of the enclosure. A method of supplying a customized safety enclosure for a DUT to a customer includes receiving information from the customer about the DUT size and testing environment, fabricating one or more variable components of the enclosure, producing custom assembly instructions for the enclosure, packaging a plurality of components of the enclosure including the variable components and one or more fixed components, and sending the package of components and the custom assembly instructions to the customer.

Abstract: Systems, devices and methods for high-speed I/O margin testing can screen high volumes of pre-production and production parts and identify cases where the electrical characteristics have changed enough to impact operation. The margin tester disclosed is lower cost, easier to use and faster than traditional BERT and scopes and can operate on the full multi-lane I/O links in their standard operating states with full loading and cross-talk. The margin tester assesses the electrical receiver margin of an operation multi-lane high speed I/O link of a device under test simultaneously in either or both directions. In a technology-specific form, an embodiment of the margin tester can be implemented as an add-in card margin tester to test motherboard slots of a mother board under test, or as a as a motherboard with slots to test add-in cards.

Abstract: A vector network analyzer (VNA) can include a control processor, a receiver coupled with the control processor, switching circuitry coupled with the receiver, a radio frequency (RF) bridge coupled with the switching circuitry, a transmission line coupled with the RF bridge, wherein the transmission line is configured to be coupled with a load; and a signal generator coupled with the RF bridge.

Abstract: 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.