Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: Approaches, techniques, and mechanisms are disclosed for the secure distribution of media content to devices having minimal or no Internet connectivity. Computing devices referred to herein as “local servers” are strategically deployed at various locations, such as stores and other public locations. Packages of media contents are stored on the local servers, and may be updated using various online and/or offline data transfer techniques. Portable devices may directly connect to the local servers via various types of proximity-based connections, such as by wireless local area networks, by wired connections over Universal Serial Bus cables, or by sharing of removable media such as Secure Digital cards. The local servers may copy their media contents to authorized devices when such connections are established. The contents on a device may be automatically be updated the next time the device connects to one of the local servers, so as to include newly selected and/or available content.

Abstract: A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Abstract: A method and apparatus for mask optimization is provided. Mask design and production is optimized by providing proper weighting parameters for critical features. The parameters may include information such as parametric information, functional information, and hot spots determination.

Abstract: A method and apparatus for process optimization is provided. Process optimization improves parametric and functional yield post mask manufacturing.

Abstract: Disclosed are a method, a system, and a computer program product for implementing compact manufacturing model during various stages of electronic circuit designs. In some embodiments, the method or the system receives or identifies physics based data. In some embodiments, the method or the system receives or identifies the physics based data for the corresponding manufacturing process by using the golden manufacturing process model. In some embodiments, the method or the system uses the physics based data to fine tune, modify, or adjust the golden manufacturing process model. In some embodiments, the method or the system invokes the just-right module. In some embodiments, the method or the system implements the compact manufacturing model and the correct-by-design module and provides guidelines for the various stages of the electronic circuit design.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Abstract: A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Abstract: Disclosed are a method, a system, and a computer program product for implementing compact manufacturing model during various stages of electronic circuit designs. In some embodiments, the method or the system receives or identifies physics based data. In some embodiments, the method or the system receives or identifies the physics based data for the corresponding manufacturing process by using the golden manufacturing process model. In some embodiments, the method or the system uses the physics based data to fine tune, modify, or adjust the golden manufacturing process model. In some embodiments, the method or the system invokes the just-right module. In some embodiments, the method or the system implements the compact manufacturing model and the correct-by-design module and provides guidelines for the various stages of the electronic circuit design.

Abstract: A method and apparatus for inspection optimization is provided. Inspection optimization improves the parametric and functional yield using optimized inspection lists for in-line semiconductor manufacturing metrology and inspection equipment.

Abstract: A method of generating a library of simulated-diffraction signals (simulated signals) of a periodic grating includes obtaining a measured-diffraction signal (measured signal). Hypothetical parameters are associated with a hypothetical profile. The hypothetical parameters are varied within a range to generate a set of hypothetical profiles. The range to vary the hypothetical parameters is adjusted based on the measured signal. A set of simulated signals is generated from the set of hypothetical profiles.

Abstract: The invention includes a method and a system for generating integrated circuit (IC) simulation information regarding the effect of design and fabrication process decisions. One embodiment includes creating and using a data store of profile-based information comprising metrology signal, structure profile data, process control parameters, and IC simulation attributes. Another embodiment includes creation and use of a simulation data store generated using test gratings that model the geometries of the IC interconnects. The interconnect simulation data store may be used in-line for monitoring electrical and thermal properties of an IC device during fabrication. Other embodiments include methods and systems for generating and using simulation data stores utilizing a metrology simulator and various combinations of a fabrication process simulator, a device simulator, and/or circuit simulator. Information from the simulation data store may be used in-line in-situ during the design or fabrication process steps.

Abstract: A structure formed on a semiconductor wafer is examined by obtaining a first diffraction signal measured using a metrology device. A second diffraction signal is generated using a machine learning system, where the machine learning system receives as an input one or more parameters that characterize a profile of the structure to generate the second diffraction signal. The first and second diffraction signals are compared. When the first and second diffraction signals match within a matching criterion, a feature of the structure is determined based on the one or more parameters or the profile used by the machine learning system to generate the second diffraction signal.

Abstract: A profile model for use in optical metrology of structures in a wafer is selected, the profile model having a set of geometric parameters associated with the dimensions of the structure. The set of geometric parameters is selected to a set of optimization parameters. The number of optimization parameters within the set of optimization parameters is less than the number of geometric parameters within the set of geometric parameters. A set of selected optimization parameters is selected from the set of optimization parameters. The parameters of the set of selected geometric parameters are used as parameters of the selected profile model. The selected profile model is tested against one or more termination criteria.

Abstract: Specific wavelengths to use in optical metrology of an integrated circuit can be selected using one or more selection criteria and termination criteria. Wavelengths are selected using the selection criteria, and the selection of wavelengths is iterated until the termination criteria are met.

Abstract: An optical metrology system includes a photometric device with a source configured to generate and direct light onto a structure, and a detector configured to detect light diffracted from the structure and to convert the detected light into a measured diffraction signal. A processing module of the optical metrology system is configured to receive the measured diffraction signal from the detector to analyze the structure. The optical metrology system also includes a generic interface disposed between the photometric device and the processing module. The generic interface is configured to provide the measured diffraction signal to the processing module using a standard set of signal parameters. The standard set of signal parameters includes a reflectance parameter, a first polarization parameter, a second polarization parameter, and a third polarization parameter.