Abstract:
A method of dynamically determining an oscilloscope noise characteristic includes retrieving a power spectral density (PSD) model of noise from a storage based upon a current configuration of the oscilloscope, generating a representation of any filtering being applied to a waveform generated by a device under test, using the PSD and the representation to produce a modified power spectral density, and using the modified power spectral density to determine a dynamic oscilloscope noise characteristic. A test and measurement instrument has one or more inputs to acquire waveforms from a device under test (DUT), one or more processors to retrieve a power spectral density (PSD) model of noise from a database, generate a representation of any filtering being applied to a waveform generated by the DUT, use the PSD and the representation to produce a modified PSD, and use the modified PSD to determine a dynamic instrument noise characteristic.
Abstract:
A test and measurement device includes an input port to receive an input signal, a sampling circuit structured to generate a sample from the input signal, in which generating the sample from the input signal produces an amount of kickout energy, and an energy reducing circuit coupled between the sampling circuit and one or more other components of the test and measurement device, the energy reducing circuit structured to decrease the amount of kickout energy from the sampling circuit. The energy reducing circuit may include or be combined with a pick-off circuit. Methods are also described.
Abstract:
A test and measurement instrument includes one or more ports to allow the instrument to connect to a DUT, a memory, a user interface including a display to display waveform signals received from the DUT and controls to allow a user to select settings for the instrument, and one or more processors configured to execute code that causes the one or more processors to: receive a signal from the DUT having multiple signal levels and multiple jitter thresholds; and adjust each measurement of the signal from the DUT using a jitter compensation value for each jitter threshold to produce a final measurement. A method includes receiving a waveform signal having multiple signal levels and multiple jitter thresholds from a device under test (DUT), and adjusting measurements of each level of the signal using a jitter compensation value for each level to produce final measurements.
Abstract:
A test and measurement device includes an input port to receive an input signal, a sampling circuit structured to generate a sample from the input signal, in which generating the sample from the input signal produces an amount of kickout energy, and an energy reducing circuit coupled between the sampling circuit and one or more other components of the test and measurement device, the energy reducing circuit structured to decrease the amount of kickout energy from the sampling circuit. The energy reducing circuit may include or be combined with a pick-off circuit. Methods are also described.
Abstract:
Method and systems are described for estimating signal impairments, in particular jitter that includes uncorrelated, non-periodic signal impairments. One system may take the form of an oscilloscope. The estimates may take the form of a probability density function (PDF) for uncorrelated signal impairments that has been modified to replace low probability regions with a known approximation and an extrapolation of the known approximation.
Abstract:
A test and measurement system has one or more inputs connectable to a device under test (DUT), and one or more processors configured to execute code that causes the one or more processors to: gather a set of training waveforms by acquiring one or more waveforms from one or more DUTs or from simulated waveforms, remove noise from the set of training waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network. A method of training a neural network having receiving one or more waveforms from one or more DUTs, or generating one or more waveforms from a waveform simulator, removing noise from a set of training waveforms gathered from the one or more waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network.
Abstract:
Methods and systems are described for analyzing signal impairments using a test and measurement instrument. A method may include decomposing aggregate signal impairments into signal impairments that are correlated and uncorrelated to an acquired data pattern. The uncorrelated signal impairments may be further decomposed into periodic signal impairments (e.g., PJ) and non-periodic uncorrelated signal impairments. A PDF of the non-periodic uncorrelated signal impairments may be mathematically integrated, thereby producing an estimated cumulative distribution function (CDF) curve. Random signal impairments may be estimated as an unbound Gaussian distribution. The CDF curve of the non-periodic uncorrelated signal impairments and the unbound Gaussian distribution may be plotted in Q-space on a display device. Non-periodic bounded uncorrelated signal impairments (e.g., NP-BUJ) PDF may then be isolated. Bounded uncorrelated signal impairments PDF may then be synthesized. Complete uncorrelated signal impairments PDF may be synthesized. A synthesis of the decomposed components can be performed at a user-defined bit error rate to generate the total estimated jitter (e.g., TJ@BER or TN@BER).