Abstract:
A serial data link measurement and simulation system for use on a test and measurement instrument presents on a display device. A main menu having elements representing a measurement circuit, a simulation circuit and a transmitter. The main menu includes processing flow lines pointing from the measurement circuit to the transmitter and from the transmitter to the simulation circuit. The main menu includes a source input to the measurement circuit and one or more test points from which waveforms may be obtained. The simulation circuit includes a receiver. The measurement and simulation circuits are defined by a user, and the transmitter is common to both circuits so all aspects of the serial data link system are tied together.
Abstract:
A de-embed probe, including two inputs configured to connect to a device under test, a memory, a signal generator configured to output a signal, a plurality of load components, a plurality of switches, and a controller. Each load component is configured to provide a different load. A first switch of the plurality of switches is associated with the signal generator and the other switches of the plurality of switches are each associated with one load component. The controller is configured to control the plurality of switches to connect combinations of the loads from the plurality of load components and the signal from the signal generator across the two inputs.
Abstract:
A harmonic time interleave (HTI) system can include a sample clock to provide a reference signal, a summing component to receive the reference signal and a second input, a splitter component to receive an input signal, and delay blocks to each receive an output from the splitter. The HTI system can also include digitizing components to receive the reference signal from the sample clock and an output from each of the mixing components, and a poly-phase filter matrix block to receive an output from each of the digitizing components. The HTI system can also include an interleave reconstruction block to receive an output from the poly-phase filter matrix block and interleave time domain signal samples from each digitizer to create a reconstructed waveform.
Abstract:
An apparatus and method for splitting a wide band input signal and overlaying multiple frequency bands on each path associated with one or more digitizers. All frequencies from the split signal on each path can be fed to a mixer. The local oscillator of each mixer receives a sum of signals, which can each be set to any arbitrary frequency, as long as an associated matrix determinant of coefficients is non-zero. Each oscillator signal is multiplied by a coefficient, which can represent phase and magnitude, prior to summing the oscillator signals together. Each mixer mixes a combined signal with the input, thereby generating a set of multiple overlaid frequency bands. The digitized signals are processed to substantially reconstruct the original input signal. Thus, the wide band input signal is digitized using multiple individual digitizers. In particular, a system can support two wide band signals using four digitizers of narrower bandwidth.
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 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 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.
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 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.
Abstract:
A test and measurement instrument has an input port to allow the instrument to receive one or more waveforms from a device under test (DUT), one or more low pass filters to remove a portion of the noise from the one or more waveforms, and one or more processors to: select a waveform pattern from the waveforms, measure noise in the one or more waveforms and generate a noise representation of the noise removed, create one or more images using the waveform pattern and the one or more filtered waveforms, add the noise representation to the one or more images to produce at least one combined image, input the at least one combined image to one or more deep learning networks, and receive one or more predicted values for the DUT.