Abstract: Fault detection of a semiconductor processing tool employs several techniques to improve accuracy. One technique is sensor grouping, wherein a fault detection index is calculated from a group of tool operational parameters that correlate with one another. Another technique is sensor ranking, wherein sensors are accorded different weights in calculating the fault detection index. Improved accuracy in fault detection may be accomplished by employing a variety of sensor types to predict behavior of the semiconductor processing tool. Examples of such sensor types include active sensors, cluster sensors, passive/inclusive sensors, and synthetic sensors.

Abstract: A method is provided that includes (1) receiving information about a substrate processed within a low K dielectric deposition subsystem from an integrated inspection system of the low K dielectric deposition subsystem; (2) determining an etch process to perform within an etch subsystem based at least in part on the information received from the inspection system of the low K dielectric deposition subsystem; and (3) directing the etch subsystem to etch at least one low K dielectric layer on the substrate based on the etch process. Other methods, systems, apparatus, data structures and computer program products are provided.

Abstract: A process for controlling a substrate processing operation such as a plasma etch operation. One embodiment of the method of the present invention forms a plasma within a substrate processing chamber to etch a wafer disposed within the chamber. The plasma emission is used by the process as a broadband light source. During the plasma etch process, a plurality of wavelengths of radiation reflected from the surface of the wafer being etched are measured with a spectrometer. These measurements are then compared using pattern recognition techniques to previous measurements taken during a previous plasma etch operation. Certain embodiments of the invention use principal component analysis (PCA) techniques to perform pattern recognition while other embodiment use programmed neural net pattern recognition techniques.

Abstract: A process for controlling a substrate processing operation such as a plasma etch operation. One embodiment of the method of the present invention forms a plasma within a substrate processing chamber to etch a wafer disposed within the chamber. The plasma emission is used by the process as a broadband light source. During the plasma etch process, a plurality of wavelengths of radiation reflected from the surface of the wafer being etched are measured with a spectrometer. These measurements are then compared using pattern recognition techniques to previous measurements taken during a previous plasma etch operation. Certain embodiments of the invention use principal component analysis (PCA) techniques to perform pattern recognition while other embodiment use programmed neural net pattern recognition techniques.

Abstract: A process for controlling a substrate processing operation such as a plasma etch operation. One embodiment of the method of the present invention forms a plasma within a substrate processing chamber to etch a wafer disposed within the chamber. The plasma emission is used by the process as a broadband light source. During the plasma etch process, a plurality of wavelengths of radiation reflected from the surface of the wafer being etched are measured with a spectrometer. These measurements are then compared using pattern recognition techniques to previous measurements taken during a previous plasma etch operation. Certain embodiments of the invention use principal component analysis (PCA) techniques to perform pattern recognition while other embodiment use programmed neural net pattern recognition techniques.

Abstract: Fault detection of a semiconductor processing tool employs several techniques to improve accuracy. One technique is sensor grouping, wherein a fault detection index is calculated from a group of tool operational parameters that correlate with one another. Another technique is sensor ranking, wherein sensors are accorded different weights in calculating the fault detection index. Improved accuracy in fault detection may be accomplished by employing a variety of sensor types to predict behavior of the semiconductor processing tool. Examples of such sensor types include active sensors, cluster sensors, passive/inclusive sensors, and synthetic sensors.