Abstract: A method of recycling ink used in non-printing nozzle dispensing operations in an ink jet printer, comprises collecting the ink from the non-printing operation in a collection tray, draining the ink from the collection tray into a closed cell, draining the ink from the closed cell, filtering the ink draining from the closed cell, and adding the ink back to a main ink supply after filtering, the main ink supply containing fresh ink. To maintain the quality of the ink supply the proportion of recycled to fresh ink may be limited to a maximum.

Abstract: A printing plate loading apparatus (10) configured to load plates into a printing plate imaging device (120) includes a plate pallet loading system (100) for loading plates from originally packed plate pallet into the printing plate imaging device. Additionally a plate cassette unit (110, 310) connected to the plate pallet loading system, which is configured to receive a single cassette (112) from the plate cassette unit and to load plates from the single cassette directly into the printing plate imaging device.

Abstract: A printing plate loading apparatus (10) configured to load plates into a printing plate imaging device (120) includes a plate pallet loading system (100) for loading plates from originally packed plate pallet into the printing plate imaging device. Additionally a plate cassette unit (110, 310) connected to the plate pallet loading system, which is configured to receive a single cassette (112) from the plate cassette unit and to load plates from the single cassette directly into the printing plate imaging device.

Abstract: Disclosed are apparatus and methods for obtaining and analyzing various unique metrics or “target diagnostics” from one or more semiconductor overlay targets. In one embodiment, an overlay target is measured to obtain one or both of two specific types of target diagnostic information, systematic error metrics and/or random noise metrics. The systematic error metrics generally quantify asymmetries of the overlay target, while the random noise metrics quantify and/or qualify the spatial noise that is proximate to or associated with the overlay target.

Abstract: Disclosed are apparatus and methods for obtaining and analyzing various unique metrics or “target diagnostics” from one or more semiconductor overlay targets. In one embodiment, an overlay target is measured to obtain one or both of two specific types of target diagnostic information, systematic error metrics and/or random noise metrics. The systematic error metrics generally quantify asymmetries of the overlay target, while the random noise metrics quantify and/or qualify the spatial noise that is proximate to or associated with the overlay target.

Abstract: Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target.

Abstract: Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target.

Abstract: Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target.

Abstract: Disclosed are methods and apparatus for analyzing the quality of overlay targets. In one embodiment, a method of extracting data from an overlay target is disclosed. Initially, image information or one or more intensity signals of the overlay target are provided. An overlay error is obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. A systematic error metric is also obtained from the overlay target by analyzing the image information or the intensity signal(s) of the overlay target. For example, the systematic error may indicate an asymmetry metric for one or more portions of the overlay target. A noise metric is further obtained from the overlay target by applying a statistical model to the image information or the intensity signal(s) of the overlay target. Noise metric characterizes noise, such as a grainy background, associated with the overlay target.