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PREDICTIVE ANALYTICS OF JUNCTIONLESS DOUBLE GATE STRAINED MOSFET USING GENETIC ALGORITHM WITH DOE-BASED GREY RELATIONAL ANALYSIS

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dc.citedby1
dc.contributor.authorKaharudin K.E.en_US
dc.contributor.authorSalehuddin F.en_US
dc.contributor.authorJalaludin N.A.en_US
dc.contributor.authorArith F.en_US
dc.contributor.authorZain A.S.M.en_US
dc.contributor.authorAhmad I.en_US
dc.contributor.authorJunos S.A.M.en_US
dc.contributor.authorid56472706900en_US
dc.contributor.authorid36239165300en_US
dc.contributor.authorid58861184200en_US
dc.contributor.authorid55799799900en_US
dc.contributor.authorid55925762500en_US
dc.contributor.authorid12792216600en_US
dc.contributor.authorid36241712600en_US
dc.date.accessioned2024-10-14T03:17:29Z
dc.date.available2024-10-14T03:17:29Z
dc.date.issued2023
dc.description.abstractThis paper explores the application of Genetic Algorithm (GA) incorporated with design of experiment (DoE) based on Grey Relational Analysis (GRA) in predicting the optimal design parameters of n-type Junctionless Double Gate Strained MOSFET (JLDGSM). The GRA is applied to predict the optimum level of multiple design parameters in attaining the best multiple device characteristics. The GA approach is applied to further optimize the design parameters for much improved device characteristics. The initial step is to select the best possible level of four design parameters (Ge mole fraction, high-k material thickness, source/drain doping concentration and metal work-function) within specific upper and lower boundary limits. The predictive analytics are initiated with the employment of GRA in finding the grey relational grade (GRG) which represents the multiple electrical characteristics (ION, IOFF, on-off ratio, gm, fT and fmax) for 18 sets of experiment. The computed GRGs are then processed using multiple regression analysis to derive the objective function that summarizes the relationship between the design parameters and the GRG. Finally, the genetic algorithm is utilized to predict the optimum level of design parameters based on the derived objective function. The final result reveals that the proposed predictive analytics have successfully optimized ION, IOFF, on-off ratio, gm, fT and fmax of the device by ~34%, ~40%, ~50%, ~18%, ~10% and ~4% respectively. The best combinational magnitudes of Ge mole fraction, Thigh-k, Nsd and WF for the most optimum device characteristics are predicted to be 0.1 (10%), 3 nm, 3�1013 cm-3 and 4.6 eV respectively. The results exhibits significant potential for junctionless transistor revealing the alternative way and configuration in developing future highly efficient nano-scaled devices and ion-sensitive sensors. � 2023 Taylor's University. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage3096
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85183945670
dc.identifier.spage3077
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85183945670&partnerID=40&md5=0b486c40990292c350675b0bbbb453f2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33942
dc.identifier.volume18
dc.pagecount19
dc.publisherTaylor's Universityen_US
dc.sourceScopus
dc.sourcetitleJournal of Engineering Science and Technology
dc.subjectMaximum oscillation frequency
dc.subjectOff-current
dc.subjectOn-current
dc.subjectOn-off ratio transconductance
dc.subjectUnity-gain frequency
dc.titlePREDICTIVE ANALYTICS OF JUNCTIONLESS DOUBLE GATE STRAINED MOSFET USING GENETIC ALGORITHM WITH DOE-BASED GREY RELATIONAL ANALYSISen_US
dc.typeArticleen_US
dspace.entity.typePublication
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