Abstract: A semiconductor device includes a semiconductor fin formed on a substrate, a first gate formed around the semiconductor fin, and a second gate formed around the semiconductor fin below the first gate and separated from the first gate.

Abstract: An approach is provided in which a fault-injecting system injects a natural language fault into a first text segment to produce a second text segment that are both written in a natural language. The fault-injecting system receives a third text segment from a reviewer that includes at least one correction to the second text segment. The fault-injecting system compares the third text segment against the first text segment and generates an efficacy score. The efficacy score indicates whether the correction in the third text segment corrects the natural language fault. In turn, the fault-injecting system sends the efficacy score to an author of the first text segment.

Abstract: A method includes receiving a training data set for a first plurality of assemblies, wherein the training data set includes a plurality of components for each of two or more types of components of the first plurality of assemblies. The method analyzes the training data set for the first plurality of assemblies. Responsive to receiving a set of product component information for a second plurality of assemblies, the method creates a graded product cluster template for the second plurality of assemblies based on the analyzed training data for the first plurality of assemblies. The method sends the graded product cluster template to an automated manufacturing device, wherein the automated manufacturing device manufactures a first assembly from the second plurality of assemblies based on the graded product cluster template.

Abstract: A first execution of an application is performed on a virtual machine. A set of virtual machine parameters associated with the first execution is determined. One or more command lines of the application are mapped to the set of virtual machine parameters. The mapping is stored in a cache. A second execution of the application is performed on the virtual machine. The second execution retrieves the set of virtual machine parameters from the cache.

Abstract: A method for reducing chemo-epitaxy directed-self assembly (DSA) defects of a layout of a guiding pattern, the method comprising: inserting a first external dummy along an external edge of the guiding pattern in a vertical direction; and inserting a second external dummy at a fixed distance from a second edge of the first external dummy, wherein the second external dummy includes a two-dimensional shape such that at least two edges of the second external dummy are parallel to the second edge of the first external dummy.

Abstract: Examples of techniques for statistical static timing analysis of an integrated circuit are disclosed. In one example according to aspects of the present disclosure, a computer-implemented method is provided. The method comprises performing an initial statistical static timing analysis of the integrated circuit to create a parameterized model of the integrated circuit for a plurality of paths using a plurality of timing corners to calculate a timing value for each of the plurality of paths, each of the plurality of timing corners representing a set of timing performance parameters. The method further comprises determining at least one worst timing corner from the parameterized model for each of the plurality of paths based on the initial statistical static timing analysis and calculated timing value for each of the plurality of paths. The method also comprises performing a subsequent analysis of the integrated circuit using the at least one worst timing corner.

Abstract: Data processing in a data processing system including a plurality of processing nodes coupled by a communication link includes receiving a first command from a first processing node. A link stall of the communication link is detected by a first link layer of the first processing node. A stop command is received at a first transaction layer of the first processing node from the first link layer. The first command is truncated by the first transaction layer into a first truncated command responsive to receiving the stop command. A command arbiter is instructed to stop issuing new commands. The first truncated command is forwarded to an asynchronous crossing buffer of the first processing node.

Abstract: Embodiments are provided herein for component composition of a disaggregated computing system. A plurality of general purpose links connecting a computing element to other hardware elements are provided within the disaggregated computing system. Each of the plurality of general purpose links comprise a point-to-point connection to at least one of the other hardware elements such that the plurality of general purpose links conform to a configuration used by the other hardware elements regardless of a type of data being transferred through the plurality of general purpose links.

Abstract: Embodiments are provided herein for efficient component communication and resource utilization in a disaggregated computing system. An application programming interface (API) is used to submit a communications request to a communications manager, the communications request associated with a connection between a first plurality of computing elements of a first type and a second plurality of computing elements of a second type. The connection between ones of the first plurality of computing elements and the second plurality of computing elements is dynamically rewired according to a grouping scheme computed using information contained within the communications request.

Abstract: Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A general purpose link is provided to connect a computing element to a plurality of other computing elements of the disaggregated computing system. The general purpose link is dynamically switched between a plurality of different hardware protocols to communicate with the other computing elements, where respective ones of the other computing elements comprise different types of hardware elements.

Abstract: Embodiments for efficient resource placement in a disaggregated computing environment. Responsive to receiving an alert of a threshold breach of one or more events associated with a suspicious resource of a first resource type while executing a workload, a known good resource is selected from an available resource pool of the first resource type, where the selecting is performed to optimize a usage of the first resource type. The known good resource from the available resource pool of the first resource type is assigned to the workload such that execution of the workload is transferred from being performed by the suspicious resource to the known good resource.

Abstract: Embodiments for implementing intelligent audio composition guidance for a video by a processor. One or more acoustic characteristics used in a plurality of video segments may be identified, from a corpus, as having similar acoustic, linguistic, and visual characteristics of a selected video segment.

Abstract: Embodiments are provided herein for dynamic memory-based communication in a disaggregated computing system. A pool of similar computing elements is configured as a large address space, the large address space segmented by an identifier. Data travel distances are optimized depending on a historical or expected use of a data object by using a grouping and amortization algorithm to relocate the data object within the pool of similar computing elements at a particular address within the large address space according to the historical or expected use.

Abstract: Embodiments are provided herein for facilitating high link bandwidth utilization in a disaggregated computing system. A plurality of general purpose links are used to connect respective pluralities of computing elements. A traffic pattern between respective ones of a first plurality of computing elements of a first type and respective ones of a second plurality of computing elements of a second type is detected. The first and second pluralities of computing elements are dynamically connected through the respective ones of the plurality of general purpose links according to the detected traffic pattern.

Abstract: Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A general purpose link is provided between a plurality of devices in the disaggregated computing system. The general purpose link is used to connect the plurality of devices which respectively comprise different types of devices. A first data object is stored within one of the plurality of devices according to a past use or an expected use of the data object by at least a respective one of a set of processors within the disaggregated computing system, and the general purpose link is dynamically rewired according to a location of the first data object and a location of the at least one of the respective set of processors.

Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Responsive to detecting a threshold breach of a recurrent event associated with a first resource of a first resource type executing a workload, an alert is generated; and responsive to receiving the alert, the execution of the workload on the first resource is ceased. Health check diagnostics are identified and invoked on the first resource based on the alert and a server telemetry. Results of the health check diagnostics are mapped to a set of learned failure patterns; and a potential failure of the first resource is predicted based on the mapping.

Abstract: Embodiments for preemptive substitution of resources in a disaggregated computing environment. Failure patterns and mitigation actions are analyzed for specific failures of respective resources within the disaggregated computing environment. Responsive to determining a failure threshold has been reached for a first resource of a first type of the respective resources, a mitigation action is performed according to the analyzed failure patterns. A result of the mitigation action is determined and the result is used to improve the failure pattern analyzation.

Abstract: A method for providing device specific security measures in an Internet of Things (IoT) environment includes analyzing a first set of commands received at an IoT device (device) for an unauthorized attempt to gain access to the device. The embodiment further includes in the method generating, for the device, a first device specific code, the first device specific code corresponding to a first set of command options to be performed by the device. The embodiment further includes in the method generating, for the device, a first action map, the first action map corresponding to a first flowchart of steps to be performed upon selection of at least one of the command options. The embodiment further includes in the method transmitting, to the device, the first device specific code and the first action map.

Abstract: Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Respective resources from respective pools of resources of different types are assigned to compose a disaggregated server. A workload is executed by the respective resources within the disaggregated server while the respective resources of the disaggregated server are monitored by a monitoring task. Responsive to a first resource of the respective resources generating an alert from the monitoring task, the workload is instantiated to be concurrently performed by the first resource and a second resource of the respective resources while initiating a health check diagnostic operation on the first resource.

Abstract: Embodiments for component lifecycle optimization in a disaggregated computing environment. A monitoring and machine learning process is performed to learn a respective lifecycle of different resource types as the different resource types are assigned to respective workloads. The monitoring and machine learning process is used to develop a set of learned failure patterns for determining a mitigation action to perform as new faults are encountered within each of the different resource types while executing the respective workloads. The mitigation action is performed to optimize a remaining lifecycle of respective ones of the different resource types according to the set of learned failure patterns.