Abstract: Embodiments disclosed herein include methods for reducing or eliminating the impact of tuning disturbances during prediction of lamp failure. In one embodiment, the method comprises monitoring data of a lamp module for a process chamber using one or more physical sensors disposed at different locations within the lamp module, creating virtual sensors based on monitoring data of the lamp module, and providing a prediction model for the lamp module using the virtual sensors as inputs.

Abstract: A method is provided for determining one or more causes for variability in data. The method includes selecting a first range of a multivariate model output data on a user interface and employing a computing system, operatively coupled to the user interface, to determine one or more process data causing a variability of the multivariate model output data in the first range when compared to a second range of the multivariate model output data. At least some of the process data includes data derived from a physical measurement of a process variable.

Abstract: Embodiments disclosed herein include methods for reducing or eliminating the impact of tuning disturbances during prediction of lamp failure. In one embodiment, the method comprises monitoring data of a lamp module for a process chamber using one or more physical sensors disposed at different locations within the lamp module, creating virtual sensors based on monitoring data of the lamp module, and providing a prediction model for the lamp module using the virtual sensors as inputs.

Abstract: A method is provided for determining one or more causes for variability in data. The method includes selecting a first range of a multivariate model output data on a user interface and employing a computing system, operatively coupled to the user interface, to determine one or more process data causing a variability of the multivariate model output data in the first range when compared to a second range of the multivariate model output data. At least some of the process data includes data derived from a physical measurement of a process variable.

Abstract: A data analyzing method for a fault detection and classification system. The method includes extracting a set of raw data from a fault detection and classification system, separating the raw data for generating another set of classified data via a predetermined filtering condition, and utilizing a predetermined statistical method for analyzing the classified data.

Abstract: A method and related system for semiconductor equipment early warning management. The method includes recording process parameters of each piece of equipment, recording equipment parameters when each piece of equipment is processing, evaluating and recording the quality of semiconductor products and corresponding testing parameters, and analyzing a relationship between the corresponding process parameters, the corresponding equipment parameters, and the quality of semiconductor products of each piece of equipment.

Abstract: A method for analyzing in-line QCtest parameters is used to analyze a plurality of lots of products, each lot of products having a lot number and being formed using a plurality of equipments. At least one wafer of each lot of products is tested by at least one in-line QC test item to generate an in-line QC test parameter. The in-line QC test item, a sample test item and a wafer test item related to the in-line QC test item are stored in a database. The database further stores the in-line QC test parameter and data of a plurality of lots of high-yield product stocks, such as various test items and test parameters.

Abstract: A method and related system for semiconductor equipment early warning management. The method includes recording process parameters of each piece of equipment, recording equipment parameters when each piece of equipment is processing, evaluating and recording the quality of semiconductor products and corresponding testing parameters, and analyzing a relationship between the corresponding process parameters, the corresponding equipment parameters, and the quality of semiconductor products of each piece of equipment.

Abstract: A method for analyzing in-line QCtest parameters is used to analyze a plurality of lots of products, each lot of products having a lot number and being formed using a plurality of equipments. At least one wafer of each lot of products is tested by at least one in-line QC test item to generate an in-line QC test parameter. The in-line QC test item, a sample test item and a wafer test item related to the in-line QC test item are stored in a database. The database further stores the in-line QC test parameter and data of a plurality of lots of high-yield product stocks, such as various test items and test parameters.

Abstract: The claimed invention method is for analyzing defect inspection parameters. The method includes searching for the defect inspection parameters of a plurality of lots of products from a database, classifying the plurality of lots of products into at least a qualified group and a failed group according to the defect inspection parameters, searching for a process step correlated to a defect inspection item from the database, searching for manufacturing equipment through which the qualified group has passed in the process step and the manufacturing equipment through which the failed group has passed in the process step, and determining the manufacturing equipment through which the probability that the failed group having passed which is greater than that of the qualified group.

Abstract: The claimed invention method is for analyzing defect inspection parameters. The method includes searching for the defect inspection parameters of a plurality of lots of products from a database, classifying the plurality of lots of products into at least a qualified group and a failed group according to the defect inspection parameters, searching for a process step correlated to a defect inspection item from the database, searching for manufacturing equipment through which the qualified group has passed in the process step and the manufacturing equipment through which the failed group has passed in the process step, and determining the manufacturing equipment through which the probability that the failed group having passed which is greater than that of the qualified group.