  Process monitoring and control are essential in modern industries for
ensuring high quality standards and optimizing production performance. These
technologies have a long history of application in production and have had
numerous positive impacts, but also hold great potential when integrated with
Industry 4.0 and advanced machine learning, particularly deep learning,
solutions. However, in order to implement these solutions in production and
enable widespread adoption, the scalability and transferability of deep
learning methods have become a focus of research. While transfer learning has
proven successful in many cases, particularly with computer vision and
homogenous data inputs, it can be challenging to apply to heterogeneous data.
Motivated by the need to transfer and standardize established processes to
different, non-identical environments and by the challenge of adapting to
heterogeneous data representations, this work introduces the Domain Adaptation
Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the
issue of model generalization through domain adaptation, specifically for
heterogeneous data, and enables the transfer and scalability of deep
learning-based statistical control methods in a general manner. Additionally,
the cyclic interactions between the different parts of the model enable DBACS
to not only adapt to the domains, but also match them. To the best of our
knowledge, DBACS is the first deep learning approach to combine adaptation and
matching for heterogeneous data settings. For comparison, this work also
includes subspace alignment and a multi-view learning that deals with
heterogeneous representations by mapping data into correlated latent feature
spaces. Finally, DBACS with its ability to adapt and match, is applied to a
virtual metrology use case for an etching process run on different machine
types in semiconductor manufacturing.
