Improving the First Pass Yield of an industrial electroplating line through a combined Design of Experiments and Causal Inference integrated with Deep Learning algorithms (DLs)

The performance of an industrial electroplating line is evaluated using the First Pass Yield (FPY).
Improving the FPY of an industrial electroplating line is complex and depends on lots of environmental
and technical parameters. These parameters have different nature that makes it hard to assess the
interaction between them and consequently to detect the causes of plating defects. In a bid to tackle this
problem, we will apply a casual reasoning model equipped with deep learning to find the underlying
causes of process failures. Moreover, we will take advantage of Design of Experiments (DoE) techniques
to learn more about the weight of each parameter in the result of the plating process, leading to a lowvalue
FPY.

Faculty Supervisor:

Usef Faghihi;Laurent Cormier

Student:

Partner:

Héroux Devtek Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

Université du Québec à Trois-Rivières

Program: