Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: A method, computer system, and a computer program product for host validation is provided. The present invention may include receiving a job from a user. The present invention may include selecting, by a scheduler, a host in a hybrid cloud environment to run the received job. The present invention may include classifying, by a learning component, the selected host's subsystems. The present invention may include determining, based on the classification, that the selected host can run the received job.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: Aspects of the disclosure provide a method for wafer result prediction. The method includes determining predictor parameters of a semiconductor process using domain knowledge that includes knowledge of the semiconductor process, a processing tool associated with the semiconductor process, a metrology tool, and/or the wafer. The method also includes removing collinearity among the predictor parameters to obtain key predictor parameters, and selecting a subset of the key predictor parameters based on metrology data of the wafer obtained from the metrology tool. The method further includes building a virtual metrology (VM) model on the subset of the key predictor parameters and may include predicting wafer results using the VM model.

Abstract: A scheduler node in a blockchain network may receive data associated with a machine learning model. The scheduler node may measure a drift of the machine learning model for a first aspect of the data. The scheduler node may determine if the drift of the machine learning model is greater than a threshold. The scheduler node may schedule, in response to the drift being greater than the drift threshold, a retraining transaction for the machine learning model.

Abstract: In one embodiment, a method includes obtaining wafer measurements of a characteristic of a semiconductor wafer at each of a plurality of process steps during a semiconductor wafer fabrication process, where each of the wafer measurements is associated with a spatial location on the semiconductor wafer from which the measurement is obtained. The method may further include creating a process step fingerprint from the obtained wafer measurements for each process step. The method may further include correlating the process step fingerprint of one of the plurality of process steps to the process step fingerprint of another one of the plurality of process steps to produce a transfer function.

Abstract: Methods and systems for phonological clustering are disclosed. A method includes: segmenting, by a computing device, a sentence into a plurality of tokens; determining, by the computing device, a plurality of phoneme variants corresponding to the plurality of tokens; clustering, by the computing device, the plurality of phoneme variants; creating, by the computing device, an initial vectorization of the plurality of phoneme variants based on the clustering; embedding, by the computing device, the initial vectorization of the plurality of phoneme variants into a deep learning model; and determining, by the computing device, a radial set of phoneme variants using the deep learning model.

Abstract: In one embodiment, a method includes obtaining wafer measurements of a characteristic of a semiconductor wafer at each of a plurality of process steps during a semiconductor wafer fabrication process, where each of the wafer measurements is associated with a spatial location on the semiconductor wafer from which the measurement is obtained. The method may further include creating a process step fingerprint from the obtained wafer measurements for each process step. The method may further include correlating the process step fingerprint of one of the plurality of process steps to the process step fingerprint of another one of the plurality of process steps to produce a transfer function.

Abstract: Methods and systems for phonological clustering are disclosed. A method includes: segmenting, by a computing device, a sentence into a plurality of tokens; determining, by the computing device, a plurality of phoneme variants corresponding to the plurality of tokens; clustering, by the computing device, the plurality of phoneme variants; creating, by the computing device, an initial vectorization of the plurality of phoneme variants based on the clustering; embedding, by the computing device, the initial vectorization of the plurality of phoneme variants into a deep learning model; and determining, by the computing device, a radial set of phoneme variants using the deep learning model.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Described herein are techniques related to a semiconductor fabrication process that facilitates the enhancement of systemic conformities of patterns of the fabricated semiconductor wafer. A semiconductor wafer with maximized systemic conformities of patterns will maximize the electrical properties and/or functionality of the electronic devices formed as part of the fabricated semiconductor wafer. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: Methods and systems for PS-CAR photoresist simulation are described. In an embodiment, a method includes calibrating initial conditions for a simulation of at least one process parameter of a lithography process using a radiation-sensitive material. In such an embodiment, the radiation-sensitive material includes a first light wavelength activation threshold that controls the generation of acid to a first acid concentration in the radiation-sensitive material and controls generation of photosensitizer molecules in the radiation-sensitive material, and a second light wavelength activation threshold that can excite the photosensitizer molecules in the radiation-sensitive material that results in the acid comprising a second acid concentration that is greater than the first acid concentration, the second light wavelength being different from the first light wavelength. Further, the method may include performing a lithography process using the previously-determined at least one process parameter.

Abstract: The present invention refers to a portable device for identification of surgical items with magnetic markers, method for identifying surgical objects with magnetic markers and system for the prevention of retention of surgical items with magnetic markers. The present invention can be used in surgical centers, with the aim of detecting surgical elements/objects (5) retained in the patient after surgery. The present invention aims to provide instrumental support in object location surgical (5) retained inside the body cavities for detecting artifacts forgotten after a surgical procedure, by means of device and specific objects and method and system for their identification.