Matches in SemOpenAlex for { <https://semopenalex.org/work/W2030496875> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W2030496875 abstract "Advanced Process Control (APC) is an important research area in Semiconductor Manufacturing (SM) to improve process stability crucial for product quality. In low-volume-high-mixture fabrication plants (fabs), Knowledge Discovery in Databases is extremely challenging due to complex technology mixtures and reduced availability of data for comparable process steps. High Density Plasma Chemical Vapor Deposition (HDP CVD) appears to be a process area in SM predestinated for application of Data Mining (DM). Enhancing physical metrology by predictive models leads to smart future fabs. Actual research focuses on Virtual Metrology (VM) using high sophisticated Machine Learning (ML) methods to model unknown functional interrelations and to predict the thickness of dielectric layers deposited onto a metallization layer of the manufactured wafers. Decision Trees (DT), Neural Networks (NN) and Support Vector Regression (SVR) have been investigated to maximize the accuracy of the regression. For data of various logistical granularities promising results have been achieved by implementing these statistical models." @default.
- W2030496875 created "2016-06-24" @default.
- W2030496875 creator A5006261388 @default.
- W2030496875 creator A5058853834 @default.
- W2030496875 creator A5073502042 @default.
- W2030496875 creator A5090179436 @default.
- W2030496875 date "2013-12-01" @default.
- W2030496875 modified "2023-09-27" @default.
- W2030496875 title "Virtual Metrology in Semiconductor Manufacturing by Means of Predictive Machine Learning Models" @default.
- W2030496875 cites W1964357740 @default.
- W2030496875 cites W2006971615 @default.
- W2030496875 cites W2043201552 @default.
- W2030496875 cites W2060880752 @default.
- W2030496875 cites W2075852334 @default.
- W2030496875 cites W2102445355 @default.
- W2030496875 cites W2111845770 @default.
- W2030496875 cites W2155614680 @default.
- W2030496875 doi "https://doi.org/10.1109/icmla.2013.186" @default.
- W2030496875 hasPublicationYear "2013" @default.
- W2030496875 type Work @default.
- W2030496875 sameAs 2030496875 @default.
- W2030496875 citedByCount "8" @default.
- W2030496875 countsByYear W20304968752014 @default.
- W2030496875 countsByYear W20304968752018 @default.
- W2030496875 countsByYear W20304968752019 @default.
- W2030496875 countsByYear W20304968752021 @default.
- W2030496875 countsByYear W20304968752022 @default.
- W2030496875 countsByYear W20304968752023 @default.
- W2030496875 crossrefType "proceedings-article" @default.
- W2030496875 hasAuthorship W2030496875A5006261388 @default.
- W2030496875 hasAuthorship W2030496875A5058853834 @default.
- W2030496875 hasAuthorship W2030496875A5073502042 @default.
- W2030496875 hasAuthorship W2030496875A5090179436 @default.
- W2030496875 hasConcept C105795698 @default.
- W2030496875 hasConcept C111919701 @default.
- W2030496875 hasConcept C112972136 @default.
- W2030496875 hasConcept C119857082 @default.
- W2030496875 hasConcept C12267149 @default.
- W2030496875 hasConcept C127413603 @default.
- W2030496875 hasConcept C154945302 @default.
- W2030496875 hasConcept C160671074 @default.
- W2030496875 hasConcept C171250308 @default.
- W2030496875 hasConcept C192562407 @default.
- W2030496875 hasConcept C195766429 @default.
- W2030496875 hasConcept C21880701 @default.
- W2030496875 hasConcept C33923547 @default.
- W2030496875 hasConcept C35750839 @default.
- W2030496875 hasConcept C41008148 @default.
- W2030496875 hasConcept C50644808 @default.
- W2030496875 hasConcept C66018809 @default.
- W2030496875 hasConcept C98045186 @default.
- W2030496875 hasConceptScore W2030496875C105795698 @default.
- W2030496875 hasConceptScore W2030496875C111919701 @default.
- W2030496875 hasConceptScore W2030496875C112972136 @default.
- W2030496875 hasConceptScore W2030496875C119857082 @default.
- W2030496875 hasConceptScore W2030496875C12267149 @default.
- W2030496875 hasConceptScore W2030496875C127413603 @default.
- W2030496875 hasConceptScore W2030496875C154945302 @default.
- W2030496875 hasConceptScore W2030496875C160671074 @default.
- W2030496875 hasConceptScore W2030496875C171250308 @default.
- W2030496875 hasConceptScore W2030496875C192562407 @default.
- W2030496875 hasConceptScore W2030496875C195766429 @default.
- W2030496875 hasConceptScore W2030496875C21880701 @default.
- W2030496875 hasConceptScore W2030496875C33923547 @default.
- W2030496875 hasConceptScore W2030496875C35750839 @default.
- W2030496875 hasConceptScore W2030496875C41008148 @default.
- W2030496875 hasConceptScore W2030496875C50644808 @default.
- W2030496875 hasConceptScore W2030496875C66018809 @default.
- W2030496875 hasConceptScore W2030496875C98045186 @default.
- W2030496875 hasLocation W20304968751 @default.
- W2030496875 hasOpenAccess W2030496875 @default.
- W2030496875 hasPrimaryLocation W20304968751 @default.
- W2030496875 hasRelatedWork W1581553923 @default.
- W2030496875 hasRelatedWork W1590547681 @default.
- W2030496875 hasRelatedWork W2094925146 @default.
- W2030496875 hasRelatedWork W2100664717 @default.
- W2030496875 hasRelatedWork W2129431599 @default.
- W2030496875 hasRelatedWork W2146435486 @default.
- W2030496875 hasRelatedWork W2147229891 @default.
- W2030496875 hasRelatedWork W2327254200 @default.
- W2030496875 hasRelatedWork W2742335923 @default.
- W2030496875 hasRelatedWork W3211291967 @default.
- W2030496875 isParatext "false" @default.
- W2030496875 isRetracted "false" @default.
- W2030496875 magId "2030496875" @default.
- W2030496875 workType "article" @default.