Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312193199> ?p ?o ?g. }
- W4312193199 endingPage "746" @default.
- W4312193199 startingPage "737" @default.
- W4312193199 abstract "While applying machine learning (ML) to semiconductor manufacturing is prevalent, an efficient way to sample the search space has not been explored much in key processes such as lithography, annealing, deposition, and etching. The aim is to use the fewest experimental trials to construct an accurate predictive model. Here, we proposed a technology computer added design (TCAD)-assisted meta-learned sampling approach. The meta-learner adjusts the way of sampling in terms of how to hybridize the TCAD with ML when selecting the next sampling point. While an advanced semiconductor process is expensive, efficient sampling is indispensable. Using laser annealing as an example, we demonstrate the effectiveness of the proposed algorithm where the mean square error (MSE) at the first 100 sampling steps using TCAD-assisted meta-learned sampling is significantly lower than the pure ML approach. Besides, with reference to the pure TCAD approach, the TCAD-assisted sampling prevents the MSE degradation at 200–400 sampling steps. The proposed approach can be used in other manufacturing or even any applied machine intelligence fields." @default.
- W4312193199 created "2023-01-04" @default.
- W4312193199 creator A5000507299 @default.
- W4312193199 creator A5024992159 @default.
- W4312193199 creator A5040582519 @default.
- W4312193199 creator A5057668648 @default.
- W4312193199 creator A5078767948 @default.
- W4312193199 creator A5082964726 @default.
- W4312193199 creator A5088566252 @default.
- W4312193199 date "2022-12-22" @default.
- W4312193199 modified "2023-10-18" @default.
- W4312193199 title "Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing" @default.
- W4312193199 cites W1528361845 @default.
- W4312193199 cites W1968200975 @default.
- W4312193199 cites W1973339006 @default.
- W4312193199 cites W1981563607 @default.
- W4312193199 cites W1990050935 @default.
- W4312193199 cites W1993202648 @default.
- W4312193199 cites W2020460778 @default.
- W4312193199 cites W2021367230 @default.
- W4312193199 cites W2026249211 @default.
- W4312193199 cites W2033732023 @default.
- W4312193199 cites W2085837170 @default.
- W4312193199 cites W2090118264 @default.
- W4312193199 cites W2113766312 @default.
- W4312193199 cites W2115305054 @default.
- W4312193199 cites W2142275721 @default.
- W4312193199 cites W2156444843 @default.
- W4312193199 cites W2342249984 @default.
- W4312193199 cites W2735175329 @default.
- W4312193199 cites W2750875381 @default.
- W4312193199 cites W2767232705 @default.
- W4312193199 cites W2781055133 @default.
- W4312193199 cites W2794868398 @default.
- W4312193199 cites W2903433270 @default.
- W4312193199 cites W2910355480 @default.
- W4312193199 cites W2913204915 @default.
- W4312193199 cites W2924763123 @default.
- W4312193199 cites W2963413693 @default.
- W4312193199 cites W2967779101 @default.
- W4312193199 cites W2973062998 @default.
- W4312193199 cites W3008902695 @default.
- W4312193199 cites W3073865269 @default.
- W4312193199 cites W3099878876 @default.
- W4312193199 cites W3161939764 @default.
- W4312193199 cites W3173017111 @default.
- W4312193199 cites W3189110740 @default.
- W4312193199 cites W3200364303 @default.
- W4312193199 cites W4205770907 @default.
- W4312193199 cites W4206643169 @default.
- W4312193199 cites W4211117330 @default.
- W4312193199 cites W4213051588 @default.
- W4312193199 cites W4230030242 @default.
- W4312193199 cites W4233873697 @default.
- W4312193199 cites W4239706645 @default.
- W4312193199 cites W4283814060 @default.
- W4312193199 doi "https://doi.org/10.1021/acsomega.2c06000" @default.
- W4312193199 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36643440" @default.
- W4312193199 hasPublicationYear "2022" @default.
- W4312193199 type Work @default.
- W4312193199 citedByCount "0" @default.
- W4312193199 crossrefType "journal-article" @default.
- W4312193199 hasAuthorship W4312193199A5000507299 @default.
- W4312193199 hasAuthorship W4312193199A5024992159 @default.
- W4312193199 hasAuthorship W4312193199A5040582519 @default.
- W4312193199 hasAuthorship W4312193199A5057668648 @default.
- W4312193199 hasAuthorship W4312193199A5078767948 @default.
- W4312193199 hasAuthorship W4312193199A5082964726 @default.
- W4312193199 hasAuthorship W4312193199A5088566252 @default.
- W4312193199 hasBestOaLocation W43121931991 @default.
- W4312193199 hasConcept C105795698 @default.
- W4312193199 hasConcept C106131492 @default.
- W4312193199 hasConcept C11413529 @default.
- W4312193199 hasConcept C126980161 @default.
- W4312193199 hasConcept C127413603 @default.
- W4312193199 hasConcept C140779682 @default.
- W4312193199 hasConcept C159985019 @default.
- W4312193199 hasConcept C160671074 @default.
- W4312193199 hasConcept C170593435 @default.
- W4312193199 hasConcept C192562407 @default.
- W4312193199 hasConcept C19499675 @default.
- W4312193199 hasConcept C24326235 @default.
- W4312193199 hasConcept C2777855556 @default.
- W4312193199 hasConcept C31972630 @default.
- W4312193199 hasConcept C33923547 @default.
- W4312193199 hasConcept C41008148 @default.
- W4312193199 hasConcept C49040817 @default.
- W4312193199 hasConcept C52740198 @default.
- W4312193199 hasConcept C66018809 @default.
- W4312193199 hasConceptScore W4312193199C105795698 @default.
- W4312193199 hasConceptScore W4312193199C106131492 @default.
- W4312193199 hasConceptScore W4312193199C11413529 @default.
- W4312193199 hasConceptScore W4312193199C126980161 @default.
- W4312193199 hasConceptScore W4312193199C127413603 @default.
- W4312193199 hasConceptScore W4312193199C140779682 @default.
- W4312193199 hasConceptScore W4312193199C159985019 @default.
- W4312193199 hasConceptScore W4312193199C160671074 @default.
- W4312193199 hasConceptScore W4312193199C170593435 @default.