Abstract: A system and computer-implemented method for creating a new model or updating a previously-created model based on a template are described. A template is generated from a previously-created model. The previously-created model specifies a set of parameters associated with a manufacturing process, a process tool or chamber. Variables associated with the manufacturing process are acquired, monitored, and analyzed. A statistical analysis (or multivariate statistical analysis) is employed to analyze the monitored variables and the set of parameters. When any of the monitored variables satisfy a threshold condition, a new model is created or the parameters of the previously-created model are updated, adjusted, or modified based on the template and the monitored variables. A user interface facilitating communication between a user and the systems and display of information is also described.

Abstract: A system and computer-implemented method for creating a new model or updating a previously-created model based on a template are described. A template is generated from a previously-created model. The previously-created model specifies a set of parameters associated with a manufacturing process, a process tool or chamber. Variables associated with the manufacturing process are acquired, monitored, and analyzed. A statistical analysis (or multivariate statistical analysis) is employed to analyze the monitored variables and the set of parameters. When any of the monitored variables satisfy a threshold condition, a new model is created or the parameters of the previously-created model are updated, adjusted, or modified based on the template and the monitored variables. A user interface facilitating communication between a user and the systems and display of information is also described.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps ('Step e“). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (”Step f').

Abstract: A linear process tool comprising at least two deposition modules each comprising one or more plasma spray guns operable to move in a direction approximately orthogonal to the direction of a substrate carrier is configured to deposit at least a first and second layer, in direct contact with each other, wherein a first layer is of first composition and the second layer is of second composition different than the first composition.

Abstract: A method and apparatus for process monitoring are provided. Process monitoring includes (i) generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment; (ii) designating at least one of the monitored parameters as being correlated to maturation of the process environment; (iii) collecting current process data corresponding to the monitored parameters, including the at least one designated parameter; and (iv) scaling the multivariate reference model based on the current process data of the at least one designated parameter to account for maturation of the process environment. The method further includes generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data; and comparing the current process metrics to the scaled reference model to determine whether the current state of the process environment is acceptable.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps ('Step e“). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (”Step f').

Abstract: A method for identifying a predetermined state of a manufacturing process by monitoring the process which involves, monitoring a plurality of variables that vary in value during the manufacturing process. The method also involves mapping as points in a hyperspace the values of each variable at a plurality of times, where the hyperspace has a number of dimensions equal to the number of variables and the number of points is equal to the plurality of times. The method also involves identifying that a manufacturing process has reached the predetermined state when one of the points reaches a closed volume in the hyperspace that is located within a predefined distance from a predefined location in the hyperspace.

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”).

Abstract: A method for monitoring a manufacturing process features acquiring metrology data for semiconductor wafers at the conclusion of a final process step for the manufacturing process (“Step a”). Data is acquired for a plurality of process variables for a first process step for manufacturing semiconductor wafers (“Step b”). A first mathematical model of the first process step is created based on the metrology data and the acquired data for the plurality of process variables for the first process step (“Step c”). Steps b and c are repeated for at least a second process step for manufacturing the semiconductor wafers (“Step d”). An nth mathematical model is created based on the metrology data and the data for the plurality of process variables for each of the n process steps (“Step e”). A top level mathematical model is created based on the metrology data and the models created by steps c, d and e (“Step f”).

Abstract: A system and computer-implemented method for creating a new model or updating a previously-created model based on a template are described. A template is generated from a previously-created model. The previously-created model specifies a set of parameters associated with a manufacturing process, a process tool or chamber. Variables associated with the manufacturing process are acquired, monitored, and analyzed. A statistical analysis (or multivariate statistical analysis) is employed to analyze the monitored variables and the set of parameters. When any of the monitored variables satisfy a threshold condition, a new model is created or the parameters of the previously-created model are updated, adjusted, or modified based on the template and the monitored variables. A user interface facilitating communication between a user and the systems and display of information is also described.

Abstract: A method for identifying a predetermined state of a manufacturing process by monitoring the process which involves, monitoring a plurality of variables that vary in value during the manufacturing process. The method also involves mapping as points in a hyperspace the values of each variable at a plurality of times, where the hyperspace has a number of dimensions equal to the number of variables and the number of points is equal to the plurality of times. The method also involves identifying that a manufacturing process has reached the predetermined state when one of the points reaches a closed volume in the hyperspace that is located within a predefined distance from a predefined location in the hyperspace.

Abstract: Faults that are associated with a set of variables that are each associated with a manufacturing parameter are classified. A fault vector is defined according to a multivariate analysis based on a contribution to a metric of a subset of the variables. The fault vector is compared to a plurality of previously-defined fault vectors to determine a comparison value indicative of a correlation between the fault vector and the plurality of previously-defined fault vectors. The fault vector is associated with at least one of a first set of fault vectors indicative of a known fault or a second set of fault vectors indicative of an unknown fault based in part on the comparison value.

Abstract: A method for process monitoring involves acquiring data samples associated with a plurality of manufacturing related variables for a plurality of outputs of a manufacturing process. The distance of each data sample relative to every other data sample is then calculated. The outputs are then grouped based on the Euclidean distances that satisfy a boundary determining criterion.

Abstract: A method for process monitoring involves acquiring data samples associated with a plurality of manufacturing related variables for a plurality of outputs of a manufacturing process. The distance of each data sample relative to every other data sample is then calculated. The outputs are then grouped based on the Euclidean distances that satisfy a boundary determining criterion.

Abstract: A method and apparatus for process monitoring are provided. Process monitoring includes (i) generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment; (ii) designating at least one of the monitored parameters as being correlated to maturation of the process environment; (iii) collecting current process data corresponding to the monitored parameters, including the at least one designated parameter; and (iv) scaling the multivariate reference model based on the current process data of the at least one designated parameter to account for maturation of the process environment. The method further includes generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data; and comparing the current process metrics to the scaled reference model to determine whether the current state of the process environment is acceptable.

Abstract: A system includes multiple optical Fourier transform cells which simultaneously scan a device under test. The illuminated area for each Fourier transform cell is small to provide high resolution, while the number of cells is large to cover a relatively wide area and keep inspection speed high. The advantages of optical computing performed by Fourier transform optics also keeps the inspection speed high because illuminated areas are large when compared to the resolution and Fourier transforms are linear shift invariant so that optical measurements can be performed during scanning. In one embodiment, Fourier transform cells are offset from each other perpendicular to the scan direction by less that the width of an illuminated area. This provides complete coverage during scanning of a device under test. Because the illumination for the Fourier transform is collimated, the system is insensitive to focusing errors due to fluctuations in working distance.

Abstract: A tester for testing integrated circuits-containing semiconductor wafers or substrates, includes a vertically oriented performance board with. D/A converters mounted and pin connected immediately therebehind. A prober including a vertical array of connector pins mounts a vertical probe card and a vertically-mounted chuck on which a vertically-oriented wafer or substrate is held. One of the tester and prober are moved with respect to the other to dock and latch the tester and prober together. Simultaneously the array of connector pins is electrically connected to electrical connectors on the performance board and probe needles extending from a probe board on the probes are placed into test contact with contact pads on the integrated circuits on the wafer or substrate.

Abstract: High speed pattern and defect detection in flat panel displays, integrated circuits, photo mask reticles, CRT color masks, printed circuit boards, and any other patterned devices, regular or irregular, uses analog optical computing. Using appropriate illumination and optics, the Fourier transform of the image of a device under test is formed. The Fourier transform components of an ideal pattern are compared to the Fourier transform components of a measured pattern, and differences in relative intensities of the spatial components indicate a defect. A spatial separator is used to direct different components of the Fourier transform in different directions for parallel, simultaneous measurement and analysis. Utilizing Statistical Process Control, and properly comparing the different Fourier transform components, the defect is partially classified. Optical image processing is done in real time at the speed of light.