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: Systems and methods for automated recognition of a device under test and retrieving data associated with the device under test based on the recognition. The systems and methods include receiving a recognition key based on an identifying characteristic of the device under test, matching the received recognition key to a stored key in a database, retrieving data related to the stored key when the received recognition key matches the stored key, transmitting instructions to perform an action on a test and measurement device based on the retrieved data, receiving new data related to the device under test, and updating the data in the database related to the stored key with the new data.

Abstract: A calibrated test and measurement cable for connecting one or more devices under test and a test and measurement instrument, including a first port structured to electrically connect to a first signal lane, a second port structured to electrically connect to a second signal lane, a third port structured to electrically connect to a test and measurement instrument, and a multiplexer configured to switch between electrically connecting the first port to the third port and connected the second port to the third port. The first and second signal lanes can be included on the same device under test or different devices under test. An input can receive instructions to operate the multiplexer.

Abstract: A computing device includes one or more memories including test and measurement knowledge, a generative artificial intelligence (AI) model having access to the one or more memories, a display, user controls to allow the user to provide inputs, and one or more processors configured to execute that code that causes the one or more processors to: access an application programming interface (API) of an AI assistant for the generative AI model to allow the user to interact with the AI assistant, receive one or more user inputs through one or more of the API or a user interface, the user inputs providing a description of one or more waveforms to be generated, use the AI assistant to develop each waveform definition from the description and access the generative AI model, receive one or more waveforms from the AI assistant, and store the one or more waveforms.

Abstract: A test and measurement system has a test and measurement instrument having an adaptor with an interface configured to communicate through one or more communications links with a new device under test to receive new test results, a memory configured to store a database of test results and a database of analyzed test results related to tests performed with one or more prior devices under test, a data analyzer connected to the test and measurement instrument through the one or more communications link, the data analyzer configured to analyze the new test results based on the stored test results, and a health score generator configured to generate a health score for the new device under test based on the analysis from the data analyzer.

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 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 system for acquiring a test-and-measurement signal from a device under test (DUT) including a test-and-measurement probe, a user interface, a robot, and a controller. The probe is configured to acquire an electronic signal from the DUT. The user interface displays a digital representation of a physical electronic circuit of the DUT, including portrayals of virtual nodes that correspond to actual nodes on the DUT. The robot is configured to automatically position the probe with respect to the DUT. The controller is configured to receive from the user interface an electronic indication of a selected node of the digital representation of the physical electronic circuit, where the selected node is one of the virtual nodes. The controller is further configured to provide instructions to the robot to automatically position the probe to a position on the physical electronic circuit corresponding to the actual node.

Abstract: A margin tester including an identification reader configured to receive an adaptor identifier of an adaptor, an interface configured to connect to a device under test through the adaptor, and one or more processors configured to assess a margin, such as an electrical margin or an optical margin, of a device under test and tag the assessment with the adaptor identifier. Assessing the margin can include assessing the margin based on an expected margin that is predicted or provided based on the adaptor identifier.

Abstract: A cable structured to be repeatedly connected to a device, each repeated connection causing degradation of the cable, the cable including a condition indicator disposed on the cable and configured to be updated with each successive connection of the cable into the device.

Abstract: A margin testing device includes at least one interface structured to connect to a device under test (DUT) one or more controllers structured to create a set of test signals based on a sequence of pseudo random data and one or more pre-defined parameters, and an output structured to send the set of test signals to the DUT. Methods and a system for testing a DUT with the disclosed margin tester and other testing device are also described.

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 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: 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 with 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 system for data creation, storage, analysis, and training while margin testing includes a margin test generator coupled through an interface to a Device Under Test (DUT). The margin test generator is structured to modify test signals for testing the DUT during one or more testing states of a test session to create testing results. The testing results are stored in a data repository along with a DUT identifier of the DUT tested during the test session. A comparator determine whether any results of the DUT test results match a predictive outcome that is based from an analysis of previous DUT tests. If so, a message generator produces an indication that the tested DUT matched the predictive outcome.

Abstract: A test and measurement system has a test and measurement instrument having an adaptor with an interface configured to communicate through one or more communications links with a new device under test to receive new test results, a memory configured to store a database of test results and a database of analyzed test results related to tests performed with one or more prior devices under test, a data analyzer connected to the test and measurement instrument through the one or more communications link, the data analyzer configured to analyze the new test results based on the stored test results, and a health score generator configured to generate a health score for the new device under test based on the analysis from the data analyzer.

Abstract: A test and measurement system for analyzing a device under test, including a database configured to store test results related to tests performed with one or more prior devices under test, a receiver to receive new test results about a new device under test, a data analyzer configured to analyze the new test results based on the stored test results, and a health score generator configured to generate a health score for the new device under test based on the analysis from the data analyzer.

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 thermal management system for a test-and-measurement probe that includes a thermally insulated shroud and a fluid inlet conduit. The shroud is configured to enclose a first portion of a probe head of the probe within an interior cavity of the shroud, while permitting a second portion of the probe head to extend out of the shroud. The shroud further includes a fluid outlet passageway configured to permit a heat-transfer fluid to pass from a probe-head end of the interior cavity, through the interior cavity of the shroud, and out of the shroud through an access portion of the shroud. The fluid inlet conduit enters the shroud through the access portion of the shroud, extends through the interior cavity of the shroud, and is configured to introduce the heat-transfer fluid to the probe-head end of the interior cavity.

Abstract: A system for data creation, storage, analysis, and training while margin testing includes a margin test generator coupled through an interface to a Device Under Test (DUT). The margin test generator is structured to modify test signals for testing the DUT during one or more testing states of a test session to create testing results. The testing results are stored in a data repository along with a DUT identifier of the DUT tested during the test session. A comparator determine whether any results of the DUT test results match a predictive outcome that is based from an analysis of previous DUT tests. If so, a message generator produces an indication that the tested DUT matched the predictive outcome.