Abstract:
An apparatus configured to acquire S-parameters of a communications channel includes a physical interface configured to transmit and receive signals through a communications channel under test, a processor, configured to execute instructions that, when executed cause the processor to: send a first data pattern from the transmitter through the communications channel at a first data rate; acquire a first waveform corresponding to the first data pattern and determine a first pulse response; calculate a first transfer function from the first pulse response; send a second data pattern from the transmitter through the communications channel at a second data rate; acquire a second waveform corresponding to the second data pattern and determine a second pulse response; calculate a second transfer function from the second pulse response; and combine the first and second transfer functions to determine an S-parameter of the communications channel.
Abstract:
Systems and methods directed towards reducing noise introduced into a signal when processing the signal are discussed herein. In embodiments a signal may initially be split by a multiplexer into two or more frequency bands. Each of the frequency bands can then be forwarded through an assigned channel. One or more channels may include an amplifier to independently boost the signal band assigned to that channel prior to a noise source within the assigned channel. This results in boosting the signal band relative to noise introduced by the noise source. In some embodiments, a filter may also be implemented in one or more of the channels to remove noise from the channel that is outside the bandwidth of the signal band assigned to that channel. Additional embodiments may be described and/or claimed herein.
Abstract:
Embodiments of the present invention provide techniques and methods for improving signal-to-noise ratio (SNR) when averaging two or more data signals by finding a group delay between the signals and using it to calculate an averaged result. In one embodiment, a direct average of the signals is computed and phases are found for the direct average and each of the data signals. Phase differences are found between each signal and the direct average. The phase differences are then used to compensate the signals. Averaging the compensated signals provides a more accurate result than conventional averaging techniques. The disclosed techniques can be used for improving instrument accuracy while minimizing effects such as higher-frequency attenuation. For example, in one embodiment, the disclosed techniques may enable a real-time oscilloscope to take more accurate S parameter measurements.
Abstract:
A continuously or step variable passive noise filter for removing noise from a signal received from a DUT added by a test and measurement instrument channel. The noise filter may include, for example, a splitter splits a signal into at least a first split signal and a second split signal. A first path receives the first split signal and includes a variable attenuator and/or a variable delay line which may be set based on the channel response of the DUT which is connected. The variable attenuator and/or the variable delay line may be continuously or stepped variable, as will be discussed in more detail below. A second path is also included to receive the second split signal and a combiner combines a signal from the first path and a signal from the second path into a combined signal.
Abstract:
A method of employing a Decision Feedback Equalizer (DFE) in a test and measurement system. The method includes obtaining an input signal data associated with an input signal suffering from inter-symbol interference (ISI). A bit sequence encoded in the input signal data is determined to support assigning portions of the input signal data into sets based on the corresponding bit sequences. The DFE is applied to each set by employing a DFE slicer pattern corresponding to each set, which results in obtaining a DFE adjusted waveform histogram/PDF/waveform database graph for each set adjusted for ISI and accurately captures jitter suppression. The DFE adjusted waveform histogram/PDF/waveform database graphs are normalized and combined into a final histogram/PDF/waveform database graph for determining an eye contour of an eye diagram and jitter measurements.
Abstract:
Systems and methods directed towards reducing noise introduced into a signal when processing the signal are discussed herein. In embodiments a signal may initially be split by a multiplexer into two or more frequency bands. Each of the frequency bands can then be forwarded through an assigned channel. One or more channels may include an amplifier to independently boost the signal band assigned to that channel prior to a noise source within the assigned channel. This results in boosting the signal band relative to noise introduced by the noise source. In some embodiments, a filter may also be implemented in one or more of the channels to remove noise from the channel that is outside the bandwidth of the signal band assigned to that channel. Additional embodiments may be described and/or claimed herein.
Abstract:
Disclosed is a mechanism for reducing noise caused by an analog to digital conversion in a test and measurement system. An adaptive linear filter is generated based on a converted digital signal and measured signal noise. The adaptive linear filter includes a randomness suppression factor for alleviating statistical errors caused by a comparison of a signal circularity coefficient and a noise circularity coefficient in the adaptive linear filter. The adaptive linear filter is applied to the digital signal along with a stomp filter and a suppression clamp filter. The digital signal may be displayed in a complex frequency domain along with depictions of the adaptive linear filter frequency response and corresponding circularity coefficients. The display may be animated to allow a user to view the signal and/or filters in the frequency domain at different times.
Abstract:
A method for determining jitter and noise of an input signal. The method includes acquiring one or more uncorrelated waveform records by an acquisition unit of a test and measurement instrument, determining a correlated waveform from the acquired waveform(s), dividing the correlated waveform into unit intervals, dividing an uncorrelated waveform into unit intervals, measuring a timing displacement (t1) between the correlated waveform and the uncorrelated waveform for each unit interval to form an apparent-jitter array ([t1]), measuring a voltage displacement (V1) between the correlated waveform and the uncorrelated waveform for reach unit interval to form an apparent-noise array ([V1]), calculating a horizontal shift (ts) between the correlated waveform and the uncorrelated waveform for each unit interval to form a compensated edge time array ([ts]), and calculating a vertical shift (Vs) between the correlated waveform and the uncorrelated waveform for each unit interval to form a compensated amplitude voltage array ([Vs]).
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 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.