Condition monitoring and prediction of solution quality during a copper electroplating process

Abstract

This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Keywords Condition monitoring • Fault prognostics • Prognostics and health management (PHM) • Remaining useful life • Copper electroplating process

Artificial intelligence to enable smart prognostics and health management of manufacturing systems for industry 4.0

“…successfully in fault diagnosis of rotating machinery [225], defect identification of stainless steel welds [226], in monitoring and predicting the quality of solutions in electroplating [227], and in several other areas. While these methods provide excellent results in the realworld, they have limitations in terms of the types and formats of the data that can be used.…”