Eliminating temperature variation effects to improve accuracy of electroplating bath monitoring

Abstract

A process for creating a predictive data set predicting the amount of target constituents are in an electrolyte solution at varying temperatures is provided. The process includes (a) obtaining a sample set in which each sample comprises an electrolyte solution of known composition; (b) obtaining an electroanalytical response for each sample in the sample set to produce a electroanalytical response data set at a pre-determined, chosen target temperature; (c) obtaining a plurality of training sets, each training set being measured at a different pre-selected temperature in a range of low to high temperatures that comprises the sample set and corresponds to the electroanalytical response data set; (d) analyzing each of the training sets individually using decomposition and multivariate regression methods to produce a plurality of regression data sets, each at different temperature; and (e) validating the plurality of training data sets to produce a predictive data set for predictive calibration models, each at different temperatures in the pre-selected range of low to high temperatures.