Automated AC Voltammetric Sensor for Early Fault Detection and Diagnosis in Monitoring of Electroplating Processes

摘要

An in situ sensor employing AC-voltammetry techniques was designed to provide a response strongly affected by the presence of specific disturbances like foreign contaminants, accumulated degradation products, severely out-of-target concentrations of electroplating bath constituents and out-of-target physical conditions of the plating process (i.e. temperature). Soft Independent Modelling of Class Analogy (SIMCA) is a pattern recognition method which describes each class separately in eigenvector space. In this supervised classification technique the projected new measurements are evaluated to determine whether they belong to a certain class or not. An automated analytical system was developed capable of collecting on-line AC voltammetric data and investigating similarities between measurements in proper conditions and measurements with upset behavior with known disturbances which can be utilized to recognize a likely pattern of behavior. The shape differences between deformed and reference set voltammograms are quantified by Mahalanobis Distance (MD)-SIMCA. In addition to the numerical approach, a graphical projection is utilized to diagnose the root cause of the detected process disturbances.