Battelle Memorial Institute

Nicholas Darby

Jeremy Bellay

Jeremiah P Schley

Katie Liszewski

Anthony George

Amir Rahimi

Joshua Baur

Kurtis Wickey

David Collins

Shelly DeForte

Jonathan Scholl

In an approach to CRAI and risk framework for manufacturing applications, a computer-implemented method for causal effect prediction includes identifying, by one or more computer processors, an intervention, where the intervention is selected from the group consisting of threats, failures, corrections, and relevant outputs; collecting, by the one or more computer processors, process dependency data; creating, by the one or more computer processors, an intervention model; combining, by the one or more computer processors, the process dependency data and the intervention model to create a combined process dependency graph; training, by the one or more computer processors, a causal relational artificial intelligence (CRAI) model; and determining, by the one or more computer processors, an estimate of an intervention efficacy.

Causal relational artificial intelligence and risk framework for manufacturing applications

In an approach to non-destructive inspection of IC devices, one or more images of a Device Under Test (DUT) are received from one or more imaging devices. Observed features are detected in the one or more images and producing a first synthetic representation of a part design of the DUT that includes the observed features. The presence of one or more first unobserved features are inferred, where the one or more first unobserved features are inferred using a mapping and inference model (MIM). The one or more first unobserved features are added to the first synthetic representation of the part design of the DUT.

Systems and methods for inspection of IC devices

Systems for determining an end-point of a delayering process using optical end-point detection are described. In embodiments, the systems include a controller with an optical end-pointing (OE) module that is configured to determine an end-point of a delayering process based at least in part on the color or thickness of a surface layer of an integrated circuit (IC). In embodiments, the OE module determines whether an end-point of the delayering process has been reached based at least in part on a comparison of a determined thickness of an IC surface layer to a target thickness. The thickness of the surface layer may be determined by the OE module using a machine learning model, such as a convolutional neural network. Methods of identifying an end-point of a delayering process are also described.

Optical end-pointing for integrated circuit delayering; systems and methods using the same

A method implemented by a software for a multimodal evaluation engine stored on a memory is provided herein. The software is executable by a processor coupled to the memory to cause the method. The method includes receiving multimodal signatures of an object of interest from inspection elements and processing the multimodal signatures to transform the multimodal signatures into formats. The method also includes generating data representations of the formats and detecting whether anomalies are present within the object of interest based on the data representations.

Data handling and machine learning

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Columbus, OH, United States of America

Nicholas Darby