FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2973062998> ?p ?o ?g. }

• W2973062998 endingPage "130179" @default.
• W2973062998 startingPage "130168" @default.
• W2973062998 abstract "With the fast scaling-down and evolution of integrated circuit (IC) manufacturing technology, the fabrication process becomes highly complex, and the experimental cost of the processes is significantly elevated. Therefore, in many cases, it is very costly to obtain a sufficient amount of experimental data. To develop an efficient method to predict the results of semiconductor experiments with a small amount of known data, we use a novel method based on Bayesian framework with the prior distribution constructed by technology computer-aided-design (TCAD) physical models. This method combines the advantages of statistical models and physical models in the aspect that TCAD can provide visionary guidance on an experiment when a limited amount of experimental data is available, and a machine learning model can account for subtle anomalous effects. Specifically, we use aspect ratio dependent etching (ARDE) phenomenon as an example and use variational inference with Kullback-Leibler divergence minimization to achieve the approximation to the posterior distribution. The relation between etching process input parameters and etching depth is learned using the Bayesian neural network with TCAD priors. Using this method with 35 neurons per hidden layer, mean square error (MSE) in the test set is reduced from 0.2896 to 0.0175, 0.058 to 0.0183, 0.0563 to 0.0188, 0.058 to 0.019 for partition=10, 20, 30, 40, respectively, reference to the baseline BNN where a regular normal distribution prior with zero mean and unity standard deviation N(0,1) is used." @default.
• W2973062998 created "2019-09-19" @default.
• W2973062998 creator A5007413992 @default.
• W2973062998 creator A5024992159 @default.
• W2973062998 creator A5026630035 @default.
• W2973062998 creator A5066155946 @default.
• W2973062998 creator A5076182136 @default.
• W2973062998 creator A5080801116 @default.
• W2973062998 date "2019-01-01" @default.
• W2973062998 modified "2023-10-18" @default.
• W2973062998 title "Physics-Prior Bayesian Neural Networks in Semiconductor Processing" @default.
• W2973062998 cites W1485550703 @default.
• W2973062998 cites W1494192115 @default.
• W2973062998 cites W1517993545 @default.
• W2973062998 cites W1532011399 @default.
• W2973062998 cites W1534477342 @default.
• W2973062998 cites W1567512734 @default.
• W2973062998 cites W1594662352 @default.
• W2973062998 cites W1887369574 @default.
• W2973062998 cites W1995281324 @default.
• W2973062998 cites W1995341919 @default.
• W2973062998 cites W2004054625 @default.
• W2973062998 cites W2006132502 @default.
• W2973062998 cites W2027330308 @default.
• W2973062998 cites W2031142546 @default.
• W2973062998 cites W2038767312 @default.
• W2973062998 cites W2057997183 @default.
• W2973062998 cites W2065579159 @default.
• W2973062998 cites W2081646486 @default.
• W2973062998 cites W2084090004 @default.
• W2973062998 cites W2085040216 @default.
• W2973062998 cites W2096649264 @default.
• W2973062998 cites W2099080510 @default.
• W2973062998 cites W2104524378 @default.
• W2973062998 cites W2111595891 @default.
• W2973062998 cites W2115356364 @default.
• W2973062998 cites W2121796745 @default.
• W2973062998 cites W2130534563 @default.
• W2973062998 cites W2131006320 @default.
• W2973062998 cites W2131682353 @default.
• W2973062998 cites W2132828732 @default.
• W2973062998 cites W2137587467 @default.
• W2973062998 cites W2142596017 @default.
• W2973062998 cites W2157469798 @default.
• W2973062998 cites W2162256707 @default.
• W2973062998 cites W2169282617 @default.
• W2973062998 cites W2326392690 @default.
• W2973062998 cites W2344478748 @default.
• W2973062998 cites W2759373267 @default.
• W2973062998 cites W2769240689 @default.
• W2973062998 cites W2794439894 @default.
• W2973062998 cites W2794868398 @default.
• W2973062998 cites W2890019192 @default.
• W2973062998 cites W2903433270 @default.
• W2973062998 cites W2963413693 @default.
• W2973062998 cites W2967779101 @default.
• W2973062998 cites W4230900352 @default.
• W2973062998 cites W4234690279 @default.
• W2973062998 doi "https://doi.org/10.1109/access.2019.2940130" @default.
• W2973062998 hasPublicationYear "2019" @default.
• W2973062998 type Work @default.
• W2973062998 sameAs 2973062998 @default.
• W2973062998 citedByCount "8" @default.
• W2973062998 countsByYear W29730629982019 @default.
• W2973062998 countsByYear W29730629982020 @default.
• W2973062998 countsByYear W29730629982021 @default.
• W2973062998 countsByYear W29730629982022 @default.
• W2973062998 countsByYear W29730629982023 @default.
• W2973062998 crossrefType "journal-article" @default.
• W2973062998 hasAuthorship W2973062998A5007413992 @default.
• W2973062998 hasAuthorship W2973062998A5024992159 @default.
• W2973062998 hasAuthorship W2973062998A5026630035 @default.
• W2973062998 hasAuthorship W2973062998A5066155946 @default.
• W2973062998 hasAuthorship W2973062998A5076182136 @default.
• W2973062998 hasAuthorship W2973062998A5080801116 @default.
• W2973062998 hasBestOaLocation W29730629981 @default.
• W2973062998 hasConcept C105795698 @default.
• W2973062998 hasConcept C107673813 @default.
• W2973062998 hasConcept C11413529 @default.
• W2973062998 hasConcept C119857082 @default.
• W2973062998 hasConcept C138885662 @default.
• W2973062998 hasConcept C139945424 @default.
• W2973062998 hasConcept C154945302 @default.
• W2973062998 hasConcept C160234255 @default.
• W2973062998 hasConcept C160671074 @default.
• W2973062998 hasConcept C171250308 @default.
• W2973062998 hasConcept C177769412 @default.
• W2973062998 hasConcept C192562407 @default.
• W2973062998 hasConcept C207390915 @default.
• W2973062998 hasConcept C22679943 @default.
• W2973062998 hasConcept C33923547 @default.
• W2973062998 hasConcept C41008148 @default.
• W2973062998 hasConcept C41895202 @default.
• W2973062998 hasConcept C50644808 @default.
• W2973062998 hasConcept C66018809 @default.
• W2973062998 hasConceptScore W2973062998C105795698 @default.
• W2973062998 hasConceptScore W2973062998C107673813 @default.
• W2973062998 hasConceptScore W2973062998C11413529 @default.

• next