Your English writing platform
Discover LudwigSuggestions(1)
Exact(17)
This work presents an automatic wafer-scale defect cluster identifier that uses a multilayer perceptron to detect the defect cluster and mark all of the defective dies.
In addition to defect cluster index (CIM), 12 critical electrical test parameters are also considered simultaneously.
The defect cluster density is greater in the vicinities of dislocation tangles and grain boundaries.
According to this mechanism, saturation of defect cluster density is reached when the rate of defect cluster formation by overlap is equal to the rate of cluster elimination during irradiation.
Additional demonstrations are then shown using two dimensional lattices containing a vacancy point defect and a tri-vacancy defect cluster.
Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).
Similar(43)
Tells about other, similar birth defect clusters among VDT workers.
The defect clustering causes the Poisson-based c-chart to exhibit many false alarms.
In situ studies also show that twin boundaries effectively remove a large number of defect clusters.
The preliminary results show that the defect clusters are predominantly stacking fault tetrahedral (SFT).
Three contributions to the change in hardness were examined: defect clusters, disordering and dissolution.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com