Methods and apparatus for detecting an endpoint of a seasoning process

A method for detecting an endpoint of a seasoning process in a process chamber, the method comprising:

• obtaining seasoning progress data indicating a progress of a seasoning process for each substrate of a first plurality of substrates;
• obtaining historical parameter values measured from a plurality of sensors selected from a set of sensors coupled to a process chamber when processing each substrate of the first plurality of substrates;
• normalizing the historical parameter values for each substrate of the first plurality of substrates with respect to a plurality of the historical parameter values for a particular substrate of the first plurality of substrates;
• generating a multi-variate analysis (MVA) model by applying a set of coefficients to the normalized historical parameter values for each substrate of the first plurality of substrates;
• determining the set of coefficients by performing a regression operation on outputs of a logistic equation, where inputs of the logistic equation are outputs of the MVA model, with the normalized historical parameter values for each substrate of the first plurality of substrates;
• receiving a plurality of substantially real-time parameter values from the plurality of sensors when processing each substrate of a second plurality of substrates;
• and
• determining an end point of the seasoning process using the MVA model with the plurality of substantially real-time parameter values.

A method for detecting an endpoint of a seasoning process, the method comprising:

• obtaining seasoning progress data indicating a progress of a seasoning process for each substrate of a first plurality of substrates;
• obtaining historical parameter values measured from a subset of a plurality of sensors coupled to a process chamber, each sensor in the plurality of sensors measuring a parameter of the seasoning process when processing each substrate of the first plurality of substrates;
• normalizing the historical parameter values with respect to a subset of the historical parameter values associated with a particular substrate of the first plurality of substrates;
• normalizing the seasoning progress data for each substrate of the first plurality of substrates with respect to seasoning progress data associated with a completed seasoning process;
• generating a multi-variate analysis (MVA) model by applying a coefficient to each normalized historical parameter values for the first plurality of substrates, wherein the coefficients are determined by performing a regression operation on a plurality of outputs of a logistic equation, where inputs of the logistic equation are outputs of the MVA model;
• receiving a plurality of substantially real-time parameter values from the subset of sensors when processing each substrate of a second plurality of substrates;
• and
• determining an end point of the seasoning process using the MVA model with the plurality of substantially real-time parameter values.