Abstract: A method for detecting an endpoint of a seasoning process in a process chamber includes obtaining seasoning progress data indicating a progress of the seasoning process for each substrate of a first plurality of substrates, and collecting historical parameter values from a plurality of sensors disposed in the process chamber. The historical parameter values for each substrate of the first plurality of substrates are normalized with respect to a plurality of parameter values for a particular substrate in the first plurality of substrates. An MVA model is generated by applying a set of coefficients to the normalized parameter values for each substrate of the first plurality of substrates, and the set of coefficients are regressed based on the seasoning progress data. An end point of the seasoning process is determined using the MVA model with a plurality of substantially real-time parameter values measured when performing a seasoning process over each substrate of a second plurality of substrates.

Abstract: A method for detecting an endpoint of a seasoning process in a process chamber includes obtaining seasoning progress data indicating a progress of the seasoning process for each substrate of a first plurality of substrates, and collecting historical parameter values from a plurality of sensors disposed in the process chamber. The historical parameter values for each substrate of the first plurality of substrates are normalized with respect to a plurality of parameter values for a particular substrate in the first plurality of substrates. An MVA model is generated by applying a set of coefficients to the normalized parameter values for each substrate of the first plurality of substrates, and the set of coefficients are regressed based on the seasoning progress data. An end point of the seasoning process is determined using the MVA model with a plurality of substantially real-time parameter values measured when performing a seasoning process over each substrate of a second plurality of substrates.

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: 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.