ENBIS-22 Trondheim Conference

Session

CONTRIBUTED Process 2

28 Jun 2022, 11:30

EL5

Conveners

CONTRIBUTED Process 2

• Alessandro Di Bucchianico (Eindhoven University of Technology)

31. R2R Control of a Chemical-Mechanical-Polishing Process Based on Machine Learning Techniques

Lucile Terras (EMSE (Ecole des Mines de Saint-Etienne))

28/06/2022, 11:30

Process

The high level of automation, the process miniaturization, the multiple consecutive operation steps, and the permanent entrant flows make the semiconductor manufacturing one of the most complex industrial processes. In this context, the development of a Run-to-Run (R2R) controller that automatically adjust recipe parameters to compensate for process variations becomes a top priority.

Since...

32. Multivariate data analysis for faster root cause identification in semiconductor industry

Ilham Rabhi (Ecole Mines Saint-Etienne)

28/06/2022, 11:50

Process

In semiconductor industry, Statistical Process Control (SPC) is a mandatory methodology to keep a high production quality. It has two main objectives: the detection of out-of-controls and the identification of potential root causes in order to correct them. Contrary to the first objective which is generally well covered by the different techniques already developed, the root cause analysis is...

117. Methods for variable time-delay estimation in industrial data

Marco Cattaldo (Nofima)

28/06/2022, 12:10

Process

In many industrial applications, the goal is to predict (possibly in real-time) some target property based on a set of measured process variables. Process data always need some sort of preprocessing and restructuring before modelling. In continuous processes, an important step in the pipeline is to adjust for the time delay between target and input variables.
Time delay can generally be...