Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: In one embodiment, an edge compute node comprises processing circuitry to: receive an incoming video stream captured by a camera, wherein the incoming video stream comprises a plurality of video segments; store the plurality of video segments in a receive buffer in a memory; perform a visual computing task on a first video segment in the receive buffer; detect a resource overload on the edge compute node; receive load information corresponding to a plurality of peer compute nodes; select a peer compute node to perform the visual computing task on a second video segment in the receive buffer; replicate the second video segment from the edge compute node to the peer compute node; and receive a compute result from the peer compute node, wherein the compute result is based on the peer compute node performing the visual computing task on the second video segment.

Abstract: A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Abstract: A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Abstract: A technique is described herein for allocating tasks within a distributed system. The distributed system includes plural computing devices. Each computing device, in turn, provides a bucket manager and at least one application instance. The bucket managers allocate work to the computing devices in units of buckets, in a manner governed by a set of application-specific initialization parameters. The bucket managers also perform their assignment function in cooperation with a separate storage and notification (S&N) system. The S&N system stores data nodes in a hierarchical data structure that reflect the assignment-related state of the distributed system. The S&N system also notifies the bucket managers upon changes in the data nodes. According to one provision, the bucket managers include logic that reduces expensive context changes when computing devices join and leave the distributed system.

Abstract: A method where deviations of a characteristic of an image simulated by two different process models or deviations of the characteristic simulated by a process model and measured by a metrology tool, are used for various purposes such as to reduce the calibration time, improve the accuracy of the model, and improve the overall manufacturing process.

Abstract: Examples described herein relate to a network interface device comprising circuitry to receive an access request with a target logical block address (LBA) and based on a target media of the access request storing at least one object, translate the target LBA to an address and access content in the target media based on the address. In some examples, translate the target LBA to an address includes access a translation entry that maps the LBA to one or more of: a physical address or a virtual address. In some examples, translate the target LBA to an address comprises: request a software defined storage (SDS) stack to provide a translation of the LBA to one or more of: a physical address or a virtual address and store the translation into a mapping table for access by the circuitry. In some examples, at least one entry that maps the LBA to one or more of: a physical address or a virtual address is received before receipt of an access request.

Abstract: A method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern, and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Abstract: Embodiments of the present disclosure are directed towards techniques and configurations for an apparatus configured to provide I/O classification information in a distributed cloud storage system, in accordance with some embodiments. In one embodiment, the apparatus may include a partition scanner, to scan an image of a virtual disk associated with the storage system, to determine one or more partitions associated with the virtual disk; a file system scanner coupled with the partition scanner, to identify file systems associated with the determined partitions, to access files stored in the identified file systems; and I/O classifier coupled with the file system scanner, to generate I/O classification information associated with the accessed files. The I/O classification information provides characteristics of input-output operations performed on the virtual disk. Other embodiments may be described and/or claimed.

Abstract: An item such as a fabric-based item may have a layer of fabric such as a layer of woven fabric. The fabric layer may include warp and weft strands. The fabric may cover keys in a keyboard or may be used in forming other structures in the fabric-based item. Each key may have an illuminated key label. Portions of the fabric may be processed by pressing heated protrusions on a textured mold into polymer optical fibers in the fabric. The protrusions form corresponding light-scattering recesses in cladding portions of the optical fibers. Light-emitting diodes or other light sources may be coupled to respective end surfaces of the optical fibers. The light-emitting diodes emit light that is emitted from the thermally imprinted light-emitting regions formed by pressing the heated protrusions into the optical fibers.

Abstract: Methods and apparatus for end-to-end data plane offloading for distributed storage using protocol hardware and Protocol Independent Switch Architecture (PISA) devices. Hardware-based data plane forwarding is implemented in compute and storage switches that comprise smart server switches running software executing in a kernel and user space. The compute switch is coupled to one or more compute servers/nodes and the storage server is coupled to one or more storage servers or storage arrays. The hardware-based data plane forwarding facilitates an end-to-end data plane between the computer server(s) and storage server(s)/array(s) that is offloaded to hardware. In one example the software comprises Ceph components used to implement control plane operations in connection with hardware offloaded data plane operations, and storage traffic employs the NVMe-oF protocol and the kernels include NVMe-oF modules. In one aspect the hardware-based data plane forwarding is implemented using programmable P4switch chips.

Abstract: In one embodiment, an edge compute node comprises processing circuitry to: receive an incoming video stream captured by a camera, wherein the incoming video stream comprises a plurality of video segments; store the plurality of video segments in a receive buffer in a memory; perform a visual computing task on a first video segment in the receive buffer; detect a resource overload on the edge compute node; receive load information corresponding to a plurality of peer compute nodes; select a peer compute node to perform the visual computing task on a second video segment in the receive buffer; replicate the second video segment from the edge compute node to the peer compute node; and receive a compute result from the peer compute node, wherein the compute result is based on the peer compute node performing the visual computing task on the second video segment.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: A technique is described herein for allocating tasks within a distributed system. The distributed system includes plural computing devices. Each computing device, in turn, provides a bucket manager and at least one application instance. The bucket managers allocate work to the computing devices in units of buckets, in a manner governed by a set of application-specific initialization parameters. The bucket managers also perform their assignment function in cooperation with a separate storage and notification (S&N) system. The S&N system stores data nodes in a hierarchical data structure that reflect the assignment-related state of the distributed system. The S&N system also notifies the bucket managers upon changes in the data nodes. According to one provision, the bucket managers include logic that reduces expensive context changes when computing devices join and leave the distributed system.

Abstract: Methods of training machine learning models related to a patterning process, including a method for training a machine learning model configured to predict a mask pattern. The method including obtaining (i) a process model of a patterning process configured to predict a pattern on a substrate, wherein the process model comprises one or more trained machine learning models, and (ii) a target pattern, and training the machine learning model configured to predict a mask pattern based on the process model and a cost function that determines a difference between the predicted pattern and the target pattern.

Abstract: A method performed by a networking switch in an object storage system. The method includes receiving a first packet from a network comprising an object ID and a data object. The method includes generating a replica for the data object. The method includes generating an object ID for the replica of the data object. The method includes determining a destination storage node for the replica of the data object. The method includes sending a second packet from the networking switch to the destination storage node. The second packet includes the object ID for the replica of the data object and the replica of the data object.

Abstract: A method is described. The method includes performing the following with a storage end transaction agent within a storage sled of a rack mounted computing system: receiving a request to perform storage operations with one or more storage devices of the storage sled, the request specifying an all-or-nothing semantic for the storage operations; recognizing that all of the storage operations have successfully completed; after all of the storage operations have successfully completed, reporting to a CPU side transaction agent that sent the request that all of the storage operations have successfully completed.

Abstract: Technologies for managing replica caching in a distributed storage system include a storage manager device. The storage manager device is configured to receive a data write request to store replicas of data. Additionally, the storage manager device is configured to designate one of the replicas as a primary replica, select a first storage node to store the primary replica of the data in a cache storage and at least a second storage node to store a non-primary replica of the data in a non-cache storage. The storage manager device is further configured to include a hint in a first replication request to the first storage node that the data is to be stored in the cache storage of the first storage node as the primary replica. Further, the storage manager device is configured to transmit replication requests to the respective storage nodes. Other embodiments are described and claimed.

Abstract: A method includes obtaining a sub-layout having an area that is a performance limiting spot, adjusting colors of patterns in the area, and determining whether the area is still a performance limiting spot. Another method includes decomposing patterns in a design layout into multiple sub-layouts; determining for at least one area in one of the sub-layouts, the likelihood of that a figure of merit is beyond its allowed range; and if the likelihood is above a threshold, that one sub-layout has a performance limiting spot. Another method includes: obtaining a design layout having a first group of patterns and a second group of patterns, wherein colors of the first group of patterns are not allowed to change and colors of the second group of patterns are allowed to change; and co-optimizing at least the first group of patterns, the second group of patterns and an illumination of a lithographic apparatus.