Agilent Technologies, Inc.

Flying Gybe Inc.

Verigy (singapore) Pte. Ltd.

Nicholas Tufillaro

Lee Alton Barford

Omar Zurita

Ajay Khoche

Ivan Lalovic

John M Heumann

David Lee Gines

Daniel A Usikov

Stanley Ted Jefferson

Robert E Jewett

Ivan Lalović

Linda Argon Kamas

Kevin Gee

David M Walker

Philipp Grötsch

Colin Fox

A hyperspectral sensing device may include an optical collector configured to collect light and to transfer the collected light to a sensor having spectral resolution sufficient for sensing hyperspectral data. In some examples, the sensor comprises a compact spectrometer. The device further comprises a power supply, an electronics module, and an input/output hub enabling the device to transmit acquired data (e.g., to a remote server). In some examples, a plurality of hyperspectral sensing devices are deployed as a network to acquire data over a relatively large area. Methods are disclosed for performing dark-current calibration and/or radiometric calibration on data obtained by the hyperspectral sensing device, and/or another suitable device. Data obtained by the device may be represented in a functional basis space, enabling computations that utilize all of the hyperspectral data without loss of information.

Hyperspectral sensing system and processing methods for hyperspectral data

A method for retrieving a corrected spectrum from a measured spectrum (e.g., retrieving a top-of-water spectrum from a measured top-of-atmosphere spectrum) includes creating a scene-specific model of a region of interest and performing a ray-tracing simulation to simulate rays of light that would reach an airborne (or spaceborne) sensor. The region of interest can be an optically complex area such as an inland or coastal body of water. Based on the ray-tracing simulation, a scene-specific correction for unwanted effects (e.g., adjacency effects, variable atmospheric conditions, and/or other suitable effects) is obtained. A corrected spectrum is obtained by correcting the measured spectrum using the scene-specific correction. The ray-tracing simulation may be performed using a graphical processing unit, allowing the scene-specific correction to be performed in real time or near real time.

Hyperspectral sensing system and methods

An excitation signal generator ('ESG') is described. The ESG generates an minimized excitation signal for use in a test system to generate a functional model of a device under test ('DUT') where extreme values of the minimized excitation signal are increased toward a central value without changing the power spectrum at the DUT.

Excitation signal generator for improved accuracy of model-based testing

In one embodiment, a spectral map for performing nonlinear calibration of a signal path is developed by 1) identifying a set of frequency locations for a set of particular output signal spurs that result from applying one-tone and two-tone input signals covering a bandwidth of interest to the signal path; 2) developing, based on the set of frequency locations, a spectral map for predistorting, in the frequency domain, signals that are applied to, or received from, the signal path in the time domain; and 3) saving the spectral map for performing nonlinear calibration of the signal path.

Methods and apparatus for computing and using a spectral map for performing nonlinear calibration of a signal path

The model-based method tests compliance of production devices with the performance specifications of a device design. The production devices are manufactured in accordance with the device design by a manufacturing process. In the method, a simple model form based on the device design and the performance specifications is developed, a stimulus for testing the production devices is specified and each production device is tested. The model form has a basis function and model form parameters for the basis function. The model form parameters are dependent on the manufacturing process and differ in value among the production devices. A production device is tested by measuring the response of the production device to the stimulus; extracting, using the model form, the values of the model form parameters for the production device from the measured response and the stimulus; and checking compliance of the production device with the performance specifications using the extracted values of the model form parameters.

Model based testing for electronic devices

A tomographic reconstruction method and system incorporating Bayesian estimation techniques to inspect and classify regions of imaged objects, especially objects of the type typically found in linear, areal, or 3-dimensional arrays. The method and system requires a highly constrained model M that incorporates prior information about the object or objects to be imaged, a set of prior probabilities P(M) of possible instances of the object; a forward map that calculates the probability density P(D|M), and a set of projections D of the object. Using Bayesian estimation, the posterior probability p(M|D) is calculated and an estimated model Mof the imaged object is generated. Classification of the imaged object into one of a plurality of classifications may be performed based on the estimated model M, the posterior probability p(M|D) or MAP function, or calculated expectation values of features of interest of the object.

Highly constrained tomography for automated inspection of area arrays

A system and a general method estimate figures of merit based on nonlinear modeling and nonlinear time series analysis. Terms in a nonlinear behavioral model that depend on nonlinear combinations of a fixed input signal value are precomputed, optimizing the behavioral model such that figures of merit are evaluated from a single short stimulus vector. The optimized nonlinear behavioral model can then be applied to evaluate figures of merit for multiple devices under test (DUTs) in a manufacturing line. A process continually verifies and adjusts the nonlinear behavioral model based on sub-sampling multiple DUTs in a manufacturing line and comparing their figures of merit based on nonlinear modeling with those based on conventional measurement procedures.

Method for the rapid estimation of figures of merit for multiple devices based on nonlinear modeling

A method and apparatus that utilize time-domain measurements of a nonlinear device produce or extract a behavioral model from embeddings of these measurements. The method of producing a behavioral model comprises applying an input signal to the nonlinear device, sampling the input signal to produce input data, measuring a response of the device to produce output data, creating an embedded data set, fitting a function to the embedded data set, and verifying the fitted function. The apparatus comprises a signal generator that produces an input signal that is applied to the nonlinear device, the device producing an output signal in response. The apparatus further comprises a data acquisition system that samples and digitizes the input and output signals and a signal processing computer that produces an embedded data set from the digitized signals, fits a function to the embedded data set, and verifies the fitted function.

Method and apparatus for extraction of nonlinear black-box behavioral models from embeddings of the time-domain measurements

A method utilizes time-domain measurements of a nonlinear device to produce or extract a behavioral model from embeddings of these measurements. The resulting behavioral model of the nonlinear device is a black-box model that accommodates nonlinear devices with one or more input ports and one or more output ports. The black-box model is a functional form that is a closed form function of input variables that produces an output as opposed to a structural form. The method of producing a behavioral model comprises the steps of applying an input signal to the nonlinear device, sampling the input signal to produce input data, measuring a response of the device to produce output data, creating an embedded data set, fitting a function to the embedded data set, and verifying the fitted function. The method may apply a CDMA type input signal in the step of applying, and/or may fit a novel radial basis function in the step of fitting a function. The input signal is constructed from a single CDMA signal representation. The method of constructing is not dependent on knowledge of the behavioral model of the nonlinear device. The novel radial basis function may be determined using a modified gaussian function.

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San Francisco, CA, United States of America

Nicholas Tufillaro