Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis
This 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...
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| Format: | Article |
| Language: | en |
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Taylor's University
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28672/2/18_6_26.pdf http://eprints.utem.edu.my/id/eprint/28672/ https://jestec.taylors.edu.my/Vol%2018%20Issue%206%20December%202023/18_6_26.pdf |
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| author | Salehuddin, Fauziyah Kaharudin, Khairil Ezwan Ahmad Jalaludin, Nabilah Arith, Faiz Mohd Zain, Anis Suhaila Ahmad, Ibrahim Md Junos@Yunus, Siti Aisah |
| author_facet | Salehuddin, Fauziyah Kaharudin, Khairil Ezwan Ahmad Jalaludin, Nabilah Arith, Faiz Mohd Zain, Anis Suhaila Ahmad, Ibrahim Md Junos@Yunus, Siti Aisah |
| author_sort | Salehuddin, Fauziyah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This 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. |
| format | Article |
| id | my.utem.eprints-28672 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2023 |
| publisher | Taylor's University |
| record_format | eprints |
| spelling | my.utem.eprints-286722025-04-11T12:00:43Z http://eprints.utem.edu.my/id/eprint/28672/ Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis Salehuddin, Fauziyah Kaharudin, Khairil Ezwan Ahmad Jalaludin, Nabilah Arith, Faiz Mohd Zain, Anis Suhaila Ahmad, Ibrahim Md Junos@Yunus, Siti Aisah This 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. Taylor's University 2023-12 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/28672/2/18_6_26.pdf Salehuddin, Fauziyah and Kaharudin, Khairil Ezwan and Ahmad Jalaludin, Nabilah and Arith, Faiz and Mohd Zain, Anis Suhaila and Ahmad, Ibrahim and Md Junos@Yunus, Siti Aisah (2023) Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis. Journal Of Engineering Science And Technology, 18 (6). pp. 3077-3096. ISSN 1823-4690 https://jestec.taylors.edu.my/Vol%2018%20Issue%206%20December%202023/18_6_26.pdf |
| spellingShingle | Salehuddin, Fauziyah Kaharudin, Khairil Ezwan Ahmad Jalaludin, Nabilah Arith, Faiz Mohd Zain, Anis Suhaila Ahmad, Ibrahim Md Junos@Yunus, Siti Aisah Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title | Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title_full | Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title_fullStr | Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title_full_unstemmed | Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title_short | Predictive analytics of Junctionless Double Gate Strained MOSFET using genetic algorithm with doe-based grey relational analysis |
| title_sort | predictive analytics of junctionless double gate strained mosfet using genetic algorithm with doe-based grey relational analysis |
| url | http://eprints.utem.edu.my/id/eprint/28672/2/18_6_26.pdf http://eprints.utem.edu.my/id/eprint/28672/ https://jestec.taylors.edu.my/Vol%2018%20Issue%206%20December%202023/18_6_26.pdf |
| url_provider | http://eprints.utem.edu.my/ |