ENBIS-22 Trondheim Conference

Session

CONTRIBUTED Design of Experiment 1

27 Jun 2022, 13:30

EL5

Conveners

CONTRIBUTED Design of Experiment 1

• Osmo Kauppila (University of Oulu)

14. Efficient parameter estimation for multiresponse models with different subject characteristics

Juan M. Rodriguez-Diaz (University of Salamanca (Spain))

27/06/2022, 13:30

Design and analysis of experiments

This work will focus on experiments with several variables (responses) to be observed over time. The observations will be taken on different experimental units that may have distinct characteristics, and they may be correlated in several ways, namely intra-correlation between different responses on the same subject at the same time, and inter-correlation between observations of the same...

16. Optimal design to test for heteroschedasticity in a regression model

chiara tommasi (University of Milan)

27/06/2022, 13:50

Design and analysis of experiments

The goal of this study is to design an experiment to detect a specific kind of heteroscedasticity in a non-linear regression model, i.e.
$$ y_i=\eta(x_i;\beta)+\varepsilon_i,\; \varepsilon_i\sim N(0;\sigma^2 h(x_i;\gamma)),\quad i=1,\ldots,n, $$ where $\eta(x_i;\beta)$ is a non-linear mean function, depending on a vector of regression coefficients $\beta\in {\rm I!R}^m$, and $\sigma^2...

44. From cluster tool to processing cells KPI in semiconductor manufacturing: Data-driven findings and insights

Sokol Koço (Ecole des Mines de St Etienne), Dr Valeria Borodin (Ecole des Mines de St Etienne)

27/06/2022, 14:10

Design and analysis of experiments

The set of machines in semiconductor manufacturing is heterogeneous and includes complex machines, namely parallel machines, serial machines and cluster tools. To the best of our knowledge, the impact of the performance of each chamber on the overall performance of a given tool is rarely studied. We believe that understanding and explaining the relationship between chamber and tool...