Matches in SemOpenAlex for { <https://semopenalex.org/work/W2901984983> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W2901984983 abstract "In the modern semiconductor manufacturing technology it is essential to control the result of a wafer processing to ensure stability and high production yield. One of the promising techniques, which can provide the information about the result of a process, is predictive modeling based on machine learning models. In this paper, the possibilities of using Tree Ensembles and Artificial Neural Networks for modeling the plasma-chemical process of deep trench etching in the silicon substrate are considered. Mathematical background for machine learning techniques used for modeling is discussed, principles of regression trees generation are presented and formal descriptive algorithm of composing several regression trees in an ensemble is demonstrated. The developed predictive models were tested on physical-technological model of the plasma-chemical etching process. The results have shown that accurate and robust models based on Tree Ensembles and Artificial Neural Networks were developed in order to predict the trench depth." @default.
- W2901984983 created "2018-12-11" @default.
- W2901984983 creator A5014209773 @default.
- W2901984983 creator A5016022051 @default.
- W2901984983 creator A5023884469 @default.
- W2901984983 creator A5061129497 @default.
- W2901984983 creator A5076586206 @default.
- W2901984983 date "2018-10-01" @default.
- W2901984983 modified "2023-09-25" @default.
- W2901984983 title "Plasma-Chemical Etching Process Behavioral Models Based on Tree Ensembles and Neural Network" @default.
- W2901984983 cites W1832301915 @default.
- W2901984983 cites W2000264042 @default.
- W2901984983 cites W2060880752 @default.
- W2901984983 cites W2115571336 @default.
- W2901984983 cites W2147973445 @default.
- W2901984983 cites W2159109921 @default.
- W2901984983 cites W2796968508 @default.
- W2901984983 doi "https://doi.org/10.1109/apeie.2018.8546139" @default.
- W2901984983 hasPublicationYear "2018" @default.
- W2901984983 type Work @default.
- W2901984983 sameAs 2901984983 @default.
- W2901984983 citedByCount "2" @default.
- W2901984983 countsByYear W29019849832020 @default.
- W2901984983 countsByYear W29019849832023 @default.
- W2901984983 crossrefType "proceedings-article" @default.
- W2901984983 hasAuthorship W2901984983A5014209773 @default.
- W2901984983 hasAuthorship W2901984983A5016022051 @default.
- W2901984983 hasAuthorship W2901984983A5023884469 @default.
- W2901984983 hasAuthorship W2901984983A5061129497 @default.
- W2901984983 hasAuthorship W2901984983A5076586206 @default.
- W2901984983 hasConcept C100460472 @default.
- W2901984983 hasConcept C107187091 @default.
- W2901984983 hasConcept C111919701 @default.
- W2901984983 hasConcept C113174947 @default.
- W2901984983 hasConcept C119857082 @default.
- W2901984983 hasConcept C124223222 @default.
- W2901984983 hasConcept C127413603 @default.
- W2901984983 hasConcept C134306372 @default.
- W2901984983 hasConcept C154945302 @default.
- W2901984983 hasConcept C160671074 @default.
- W2901984983 hasConcept C171250308 @default.
- W2901984983 hasConcept C192562407 @default.
- W2901984983 hasConcept C2779227376 @default.
- W2901984983 hasConcept C33923547 @default.
- W2901984983 hasConcept C41008148 @default.
- W2901984983 hasConcept C42360764 @default.
- W2901984983 hasConcept C50644808 @default.
- W2901984983 hasConcept C84525736 @default.
- W2901984983 hasConcept C98045186 @default.
- W2901984983 hasConceptScore W2901984983C100460472 @default.
- W2901984983 hasConceptScore W2901984983C107187091 @default.
- W2901984983 hasConceptScore W2901984983C111919701 @default.
- W2901984983 hasConceptScore W2901984983C113174947 @default.
- W2901984983 hasConceptScore W2901984983C119857082 @default.
- W2901984983 hasConceptScore W2901984983C124223222 @default.
- W2901984983 hasConceptScore W2901984983C127413603 @default.
- W2901984983 hasConceptScore W2901984983C134306372 @default.
- W2901984983 hasConceptScore W2901984983C154945302 @default.
- W2901984983 hasConceptScore W2901984983C160671074 @default.
- W2901984983 hasConceptScore W2901984983C171250308 @default.
- W2901984983 hasConceptScore W2901984983C192562407 @default.
- W2901984983 hasConceptScore W2901984983C2779227376 @default.
- W2901984983 hasConceptScore W2901984983C33923547 @default.
- W2901984983 hasConceptScore W2901984983C41008148 @default.
- W2901984983 hasConceptScore W2901984983C42360764 @default.
- W2901984983 hasConceptScore W2901984983C50644808 @default.
- W2901984983 hasConceptScore W2901984983C84525736 @default.
- W2901984983 hasConceptScore W2901984983C98045186 @default.
- W2901984983 hasLocation W29019849831 @default.
- W2901984983 hasOpenAccess W2901984983 @default.
- W2901984983 hasPrimaryLocation W29019849831 @default.
- W2901984983 hasRelatedWork W1470425429 @default.
- W2901984983 hasRelatedWork W1895947839 @default.
- W2901984983 hasRelatedWork W1994518047 @default.
- W2901984983 hasRelatedWork W1995348671 @default.
- W2901984983 hasRelatedWork W2024255856 @default.
- W2901984983 hasRelatedWork W2062859037 @default.
- W2901984983 hasRelatedWork W2066674158 @default.
- W2901984983 hasRelatedWork W3210877509 @default.
- W2901984983 hasRelatedWork W4249746146 @default.
- W2901984983 hasRelatedWork W4283016678 @default.
- W2901984983 isParatext "false" @default.
- W2901984983 isRetracted "false" @default.
- W2901984983 magId "2901984983" @default.
- W2901984983 workType "article" @default.