Abstract: 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.

Abstract: Some implementations provide devices, systems and/or methods for quantifying vulnerability of an artificial neural network (ANN) to poisoning attacks. Some implementations provide devices, systems and/or methods for reducing vulnerability of an artificial neural network (ANN) to poisoning attacks. Some implementations provide devices, systems and/or methods for detecting poisoning attacks in an ANN. An ANN is trained to generate inferences based on a function.

Abstract: A method of designing a robust integrated circuit that is not vulnerable to optical fault injection comprises training a variational autoencoder to identify regions in a target integrated circuit that are vulnerable to optical fault injection and altering the design of the target integrated circuit by altering the design of the vulnerable regions so that the target integrated circuit is no longer vulnerable to optical fault injection, thereby forming the robust integrated circuit.

Abstract: In an approach to CRAI and risk framework for manufacturing applications, there is thus provided a computer-implemented method for causal effect prediction, the computer-implemented method including: identifying, by one or more computer processors, an intervention, wherein 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.

Abstract: 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.

Abstract: 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.