Hi @ZHANDOUJI,
In your DoE datatable, you can always have a look at the aliasing structure of the design by clicking on the script Evaluate Designs and checking the Alias Matrix and Color Map on Correlations panels.
The Model script in your datatable should also create a model that respects your assumed model and aliasing structure (so you should not see the Singularity Details with the provided analysis script in your DoE table). The aliasing between terms is a consequence of your design (and assumed model), not your (final) model.
In your example, in the first screenshot shows you that interactions A*B and C*D are confounded, so these two terms can't be estimated independantly (hence the missing values for logworth, p-values and biased/zeroed estimates). In the second screenshot, removing one of the terms enable you to estimate the other terms, but taking into consideration that it's not this term alone : in fact you're estimating the estimate for A*B + C*D.
More infos on Singularity Details, Models with Linear Dependencies among Model Terms
Hope this answer will help you,
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)