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. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

A light source unit generates an optical signal out of a bend-insensitive (“BI”) optical fiber that is compliant with a desired encircled flux (“EF”). The unit includes a light source to generate an optical light signal and a conventional multimode optical fiber coupled to receive the optical light signal from the light source at a first end. A modal conditioner is arranged to condition the optical light signal propagating along different modes of the conventional multimode fiber. A first bend-insensitive (BI) multimode optical fiber has an input end, the input end of the first BI multimode optical fiber being coupled at a second end of the conventional multimode optical fiber to receive the conditioned optical light signal from the conventional multimode fiber. The output from the first BI multimode optical fiber outputs an optical signal having the desired EF.

Methods for estimating the Effective Modal Bandwidth (EMB) of laser optimized Multimode Fiber (MMF) at a specified wavelength, λ.sub.S, based on the measured EMB at a first reference measurement wavelength, λ.sub.M. In these methods the Differential Mode Delay (DMD) of a MMF is measured and the Effective Modal Bandwidth (EMB) is computed at a first measurement wavelength. By extracting signal features such as centroids, peak power, pulse widths, and skews, as described in this disclosure, the EMB can be estimated at a second specified wavelength with different degrees of accuracy. The first method estimates the EMB at the second specified wavelength based on measurements at the reference wavelength. The second method predicts if the EMB at the second specified wavelength is equal or greater than a specified bandwidth limit.

A hybrid automated testing equipment (ATE) system can simultaneously test electrical and optical components of a device under test, such as an optical transceiver. The device under test can be a multilane optical transceiver that transmits different channels of data on different lanes. The hybrid ATE system can include one or more light sources and optical switches in an optical test lane selector to selectively test and calibrate each optical and electrical components of each lane of the device under test.

A test instrument for providing an optics troubleshooting technique of an optical transceiver is disclosed. The test instrument may comprise a processor and a memory, which when executed by the processor, performs the optics troubleshooting technique. The optics troubleshooting technique may include identifying a test signal from the optical transceiver. The optics troubleshooting technique may include determining signal power associated with the signal. The optics troubleshooting technique may further include applying one or more expert mode settings. In some examples, the one or more expert mode settings may be applied in a predefined order until an acceptable BER result is achieved over a predefined test period. In this way, test instrument may determine which of the one or more expert mode settings is responsible for the acceptable BER result.

A semiconductor wafer includes a semiconductor chip that includes a photonic device. The semiconductor chip includes an optical fiber attachment region in which an optical fiber alignment structure is to be fabricated. The optical fiber alignment structure is not yet fabricated in the optical fiber attachment region. The semiconductor chip includes an in-plane fiber-to-chip optical coupler positioned at an edge of the optical fiber attachment region. The in-plane fiber-to-chip optical coupler is optically connected to the photonic device. A sacrificial optical structure is optically coupled to the in-plane fiber-to-chip optical coupler. The sacrificial optical structure includes an out-of-plane optical coupler configured to receive input light from a light source external to the semiconductor chip. At least a portion of the sacrificial optical structure extends through the optical fiber attachment region.

A method comprising: receiving optical output data of an optical device; supplying the optical output data to a trained neural network configured to transform optical output data to optical performance metrics; and executing the trained neural network to transform the supplied optical output data to optical performance metrics for the optical device.

There is herein provided a method for measuring the GOSNR that can be implemented using commercial-grade transceivers and which accounts for linear optical impairments (e.g. PMD, PDL and CD) and transceiver intrinsic impairments. The method may be implemented using an Optical Spectrum Analyzer (OSA) and either the system transceivers or other commercial-grade transceivers. The proposed measurement method is based on mixed optical and electronic technologies, using an OSA and a transceiver pair. By measuring a signal quality metric Q.sub.m and the OSNR under varied power and ASE noise conditions, a constant value R.sub.BW that relates the GOSNR to the signal quality metric Q.sub.m is derived. The GOSNR is then obtained from these results.

An information handling system includes a plurality of network nodes and a processor. Each network node includes an optical link and a reflectometry analyzer. The reflection analyzers provide a plurality of reflectometry results that each provide a characterization of physical properties of the optical link. The processor receives the reflectometry results, analyzes the reflectometry results to define a fingerprint of the physical properties of the optical link, and determines a status for each of the optical links based upon the associated fingerprints. The status for each of the optical links includes one of a plurality of graded statuses. Each graded status represents a qualitative measure of the physical properties of the associated optical link. A first graded status represents a better qualitative measure than a second graded status. The processor further receives a request to route a data flow from a first one of the network nodes to a second one of the network nodes. The data flow is associated with a service level agreement that defines that the data flow is to be routed on optical links that have the first graded status. The processor further determines a path between the first network node and the second network node where each of optical links in the path have the first graded status.

A data communication network includes a network node and a processor. The network node includes an optical link and a reflectometry analyzer. The reflection analyzer provides a plurality of reflectometry results that each provide a characterization of physical and operational properties of the optical link at the time of the reflectometry result. The processor receives a first set of the reflectometry results, analyzes the first set of reflectometry results to define a fingerprint of the physical and operational properties of the optical link, receives a second set of the reflectometry results, compares the second set of reflectometry results with the fingerprint, and determines whether or not the optical link is secure based upon the comparison of the second set of reflectometry results with the fingerprint.