Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs
Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-...
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| Published in: | IEEE access Vol. 9; pp. 73467 - 73481 |
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| Format: | Journal Article |
| Language: | English |
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2021
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally. |
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| AbstractList | Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally. |
| Author | Sung, Wen-Li Akbar, Chandni Li, Yiming |
| Author_xml | – sequence: 1 givenname: Chandni surname: Akbar fullname: Akbar, Chandni organization: Parallel and Scientific Computing Laboratory, National Yang Ming Chiao Tung University, Hsinchu, Taiwan – sequence: 2 givenname: Yiming orcidid: 0000-0001-7374-0964 surname: Li fullname: Li, Yiming email: ymli@faculty.nctu.edu.tw organization: Parallel and Scientific Computing Laboratory, National Yang Ming Chiao Tung University, Hsinchu, Taiwan – sequence: 3 givenname: Wen-Li surname: Sung fullname: Sung, Wen-Li organization: Parallel and Scientific Computing Laboratory, National Yang Ming Chiao Tung University, Hsinchu, Taiwan |
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| References | ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref53 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 ref50 ref46 ref48 lin (ref21) 2012; 42 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 partin-vaisband (ref37) 2019 ref5 suzuki (ref45) 2017; 35 ref40 ref35 ref34 lin (ref67) 2017 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref71 ref70 ref73 ref72 ref68 ref24 ref23 ref26 ref69 ref25 ref64 ref20 ref63 ref66 ref22 ref65 ref28 ref27 ref29 pedregosa (ref51) 2011; 12 ref60 ref62 ref61 |
| References_xml | – ident: ref41 doi: 10.1016/S0957-4174(98)00017-7 – volume: 35 start-page: 212 year: 2017 ident: ref45 article-title: Survey of deep learning applications to medical image analysis publication-title: Med Imag Technol – ident: ref68 doi: 10.1016/j.ejor.2017.11.054 – ident: ref56 doi: 10.23919/JCC.2019.10.014 – ident: ref63 doi: 10.1016/j.compbiomed.2017.08.022 – volume: 42 start-page: 421 year: 2012 ident: ref21 article-title: Machine learning in financial crisis prediction: A survey publication-title: IEEE Trans Syst Man Cybern C Appl Rev doi: 10.1109/TSMCC.2011.2170420 – ident: ref60 doi: 10.1016/j.compbiomed.2020.103912 – ident: ref16 doi: 10.1109/TED.2014.2383352 – ident: ref17 doi: 10.1007/s10825-020-01572-9 – ident: ref34 doi: 10.1007/s43236-019-00028-z – ident: ref55 doi: 10.1016/j.cmpb.2019.105198 – ident: ref6 doi: 10.35848/1347-4065/ab99db – ident: ref44 doi: 10.1109/SURV.2008.080406 – ident: ref39 doi: 10.1038/nature14541 – ident: ref65 doi: 10.1109/CVPR.2019.00289 – ident: ref69 doi: 10.1016/j.compind.2018.12.016 – ident: ref11 doi: 10.1109/IEDM.2012.6479091 – year: 2019 ident: ref37 article-title: A single-MOSFET MAC for confidence and resolution (CORE) driven machine learning classification publication-title: arXiv 1910 09597 – ident: ref4 doi: 10.1109/IEDM.2003.1269163 – ident: ref7 doi: 10.1109/LED.2012.2194690 – ident: ref31 doi: 10.1109/TSM.2018.2825482 – ident: ref50 doi: 10.1109/TSM.2010.2056710 – ident: ref62 doi: 10.1016/j.ymssp.2020.106885 – ident: ref48 doi: 10.1109/TED.2019.2937786 – ident: ref32 doi: 10.1109/TSM.2017.2753251 – ident: ref27 doi: 10.1016/j.mee.2005.12.001 – ident: ref10 doi: 10.1109/IEDM.2006.346838 – ident: ref8 doi: 10.1109/TED.2014.2335192 – ident: ref64 doi: 10.1109/ICEngTechnol.2017.8308186 – ident: ref66 doi: 10.1007/978-3-319-46475-6_25 – ident: ref2 doi: 10.1364/OME.3.000035 – ident: ref71 doi: 10.1109/ICPHM.2017.7998311 – ident: ref59 doi: 10.1016/j.comnet.2019.107034 – ident: ref14 doi: 10.1109/TED.2016.2574328 – ident: ref52 doi: 10.1016/j.apradiso.2020.109239 – ident: ref29 doi: 10.1109/66.983448 – ident: ref24 doi: 10.1016/j.rser.2020.110287 – ident: ref25 doi: 10.1016/j.copbio.2019.08.010 – ident: ref40 doi: 10.1023/B:MACH.0000019802.64038.6c – ident: ref23 doi: 10.1016/j.neucom.2008.11.035 – ident: ref22 doi: 10.1145/1162678.1162679 – ident: ref54 doi: 10.1016/j.matcom.2020.05.010 – ident: ref30 doi: 10.1109/64.193054 – ident: ref5 doi: 10.1016/j.mssp.2019.104681 – ident: ref35 doi: 10.1109/JIOT.2019.2896174 – ident: ref61 doi: 10.1016/j.neunet.2019.12.024 – ident: ref19 doi: 10.1109/TED.2018.2822484 – start-page: 1 year: 2017 ident: ref67 article-title: Towards accurate binary convolutional neural network publication-title: Proc Conf Neural Inf Process Syst (NeurIPS) – volume: 12 start-page: 2825 year: 2011 ident: ref51 article-title: Scikit-learn: Machine learning in Python publication-title: J Mach Learn Res – ident: ref33 doi: 10.1088/1757-899X/533/1/012011 – ident: ref58 doi: 10.1016/j.chaos.2020.110121 – ident: ref72 doi: 10.1016/j.jpowsour.2019.227149 – ident: ref46 doi: 10.1371/journal.pcbi.0030116 – ident: ref1 doi: 10.1007/978-1-4419-7276-7_1 – ident: ref20 doi: 10.1371/journal.pcbi.0030116 – ident: ref15 doi: 10.1109/ACCESS.2013.2260816 – ident: ref73 doi: 10.1109/JEDS.2020.3046608 – ident: ref36 doi: 10.1109/ITEC.2019.8790564 – ident: ref43 doi: 10.1016/j.enbuild.2008.06.013 – ident: ref3 doi: 10.1109/ISAOM.2001.916555 – ident: ref26 doi: 10.1109/MSP.2020.3003836 – ident: ref28 doi: 10.1016/j.mee.2008.09.015 – ident: ref12 doi: 10.1109/LED.2014.2310119 – ident: ref13 doi: 10.1109/IEDM.2015.7409827 – ident: ref18 doi: 10.1109/TED.2009.2036309 – ident: ref38 doi: 10.1007/s10586-018-2289-6 – ident: ref70 doi: 10.1007/978-3-319-46478-7_47 – ident: ref42 doi: 10.1109/TGRS.2017.2756911 – ident: ref49 doi: 10.1109/TED.2020.2971784 – ident: ref47 doi: 10.1109/LED.2019.2931839 – ident: ref57 doi: 10.1016/j.asoc.2019.105963 – ident: ref9 doi: 10.1109/LED.2011.2169645 – ident: ref53 doi: 10.1007/JHEP06(2020)114 |
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| SubjectTerms | Algorithms artificial neural network Artificial neural networks Computer simulation Computing costs convolutional neural network Deep learning Electric variables Errors Field effect transistors Fluctuations Gallium arsenide gate-all-around Learning theory long short term memory Machine learning Metal oxide semiconductors MOSFET MOSFETs nanosized metal grain nanowire Nanowires Neural networks Predictive models root mean squared error Semiconductor devices Silicon Simulation statistical device simulation Threshold voltage Transistors Work function fluctuation Work functions |
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| Title | Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs |
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