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Souma, <i>et al.<\/i>: \u201cSimulation-based design of a strained graphene field effect transistor incorporating the pseudo magnetic field effect,\u201d Appl. Phys. Lett. <b>104<\/b> (2014) 213505 (DOI: 10.1063\/1.4880579).","DOI":"10.1063\/1.4880579"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] S. Souma, <i>et al.<\/i>: \u201cEffect of lateral strain on gate induced control of electrical conduction in single layer graphene device,\u201d J. Comput. Electron. <b>12<\/b> (2013) 170 (DOI: 10.1007\/s10825-013-0451-1).","DOI":"10.1007\/s10825-013-0451-1"},{"key":"8","unstructured":"[8] A. Carvalho, <i>et al.<\/i>: \u201cPhosphorene: From theory to applications,\u201d Nat. Rev. Mat. <b>1<\/b> (2016) 16061 (DOI: 10.1038\/natrevmats.2016.61)."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] R. Ganatra and Q. Zhang: \u201cFew-layer MoS2: A promising layered semiconductor,\u201d ACS Nano <b>8<\/b> (2014) 4074 (DOI: 10.1021\/nn405938z).","DOI":"10.1021\/nn405938z"},{"key":"10","unstructured":"[10] S. Datta: <i>Electronic Transport in Mesoscopic Systems<\/i> (Cambridge University Press, UK, 1997)."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] F. Djeffal, <i>et al.<\/i>: \u201cAn approach based on neural computation to simulate the nanoscale CMOS circuits: Application to the simulation of CMOS inverter,\u201d Solid-State Electron. <b>51<\/b> (2007) 48 (DOI: 10.1016\/j.sse.2006.12.004).","DOI":"10.1016\/j.sse.2006.12.004"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] F. Djeffal, <i>et al.<\/i>: \u201cA neural approach to study the scaling capability of the undoped double-gate and cylindrical gate all around MOSFETs,\u201d Mater. Sci. Eng. B <b>147<\/b> (2008) 239 (DOI: 10.1016\/j.mseb.2007.08.034).","DOI":"10.1016\/j.mseb.2007.08.034"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] K. Tamersit and F. Djeffal: \u201cA computationally efficient hybrid approach based on artificial neural networks and the wavelet transform for quantum simulations of graphene nanoribbon FETs,\u201d J. Comput. Electron. <b>18<\/b> (2019) 813 (DOI: 10.1007\/s10825-019-01350-2).","DOI":"10.1007\/s10825-019-01350-2"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] J. Zhang, <i>et al.<\/i>: \u201cPrior knowledge input neural network method for GFET description,\u201d J. Comput. Electron. <b>15<\/b> (2016) 911 (DOI: 10.1007\/s10825-016-0842-1).","DOI":"10.1007\/s10825-016-0842-1"},{"key":"15","unstructured":"[15] Z. Rajabi, <i>et al.<\/i>: \u201cThe non-equilibrium Green\u2019s function (NEGF) simulation of nanoscale lightly doped drain and source double gate MOSFETs,\u201d 2012 International Conference on Devices, Circuits and Systems, ICDCS (2012) 25 (DOI: 10.1109\/ICDCSyst.2012.6188669)."},{"key":"16","unstructured":"[16] Y. M. Sabry, <i>et al.<\/i>: \u201cUncoupled mode-space simulation validity for double gate MOSFETs,\u201d Proc. of International Conference on Microelectronics (2007) 351 (DOI: 10.1109\/ICM.2007.4497727)."},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] R. Venugopal, <i>et al.<\/i>: \u201cSimulating quantum transport in nanoscale transistors: Real versus mode-space approaches,\u201d J. Appl. Phys. <b>92<\/b> (2002) 3730 (DOI: 10.1063\/1.1503165).","DOI":"10.1063\/1.1503165"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] S. Datta: \u201cNanoscale device modeling: The Green\u2019s function method,\u201d Superlattices Microstruct. <b>28<\/b> (2000) 253 (DOI: 10.1006\/spmi.2000.0920).","DOI":"10.1006\/spmi.2000.0920"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] R. Hosseini: \u201cUncoupled mode space approach for analysis of nanoscale strained junctionless double-gate MOSFET,\u201d J. Comput. Electron. <b>15<\/b> (2016) 787 (DOI: 10.1007\/s10825-016-0826-1).","DOI":"10.1007\/s10825-016-0826-1"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] H. Fitriawan, <i>et al.<\/i>: \u201cQuantum electron transport modeling in uniaxially strained silicon channel of double-gate MOSFETs,\u201d Phys. Stat. Sol. (c) <b>5<\/b> (2008) 74 (DOI: 10.1002\/pssc.200776542).","DOI":"10.1002\/pssc.200776542"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] H. Fitriawan, <i>et al.<\/i>: \u201cMultiband simulation of nano-scale MOSFETs based on a non-equilibrium Green\u2019s function method,\u201d IEICE Trans. Electron. <b>E91-C<\/b> (2008) 105 (DOI: 10.1093\/ietele\/e91-c.1.105).","DOI":"10.1093\/ietele\/e91-c.1.105"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Y. LeCun, <i>et al.<\/i>: \u201cDeep learning,\u201d Nature <b>521<\/b> (2015) 436 (DOI: 10.1038\/nature14539).","DOI":"10.1038\/nature14539"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Schmidhuber: \u201cDeep learning in neural networks: An overview,\u201d Neural Netw. <b>61<\/b> (2015) 85 (DOI: 10.1016\/j.neunet.2014.09.003).","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] A. Krizhevsky, <i>et al.<\/i>: \u201cImageNet classification with deep convolutional neural networks,\u201d Commun. ACM <b>60<\/b> (2017) 84 (DOI: 10.1145\/3065386).","DOI":"10.1145\/3065386"},{"key":"25","unstructured":"[25] J. Gu, <i>et al.<\/i>: \u201cRecent advances in convolutional neural networks,\u201d arXiv preprint (2015) arXiv:1512.07108."},{"key":"26","unstructured":"[26] M. Tschannen, <i>et al.<\/i>: \u201cRecent advances in autoencoder-based representation learning,\u201d arXiv preprint (2018) arXiv:1812.05069."},{"key":"27","unstructured":"[27] V. Turchenko, <i>et al.<\/i>: \u201cA deep convolutional auto-encoder with pooling-unpooling layers in Caffe,\u201d arXiv preprint (2017) arXiv:1701.0494."},{"key":"28","unstructured":"[28] Keras: Deep learning library for Theano and TensorFlow (2015) https:\/\/keras.io\/."},{"key":"29","unstructured":"[29] D. P. Kingma and J. Ba: \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint (2014) arXiv:1412.6980."},{"key":"30","unstructured":"[30] Frugally-deep library, https:\/\/github.com\/Dobiasd\/frugally-deep."}],"container-title":["IEICE Electronics Express"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/elex\/17\/4\/17_17.20190739\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T03:34:11Z","timestamp":1582947251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/elex\/17\/4\/17_17.20190739\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/elex.17.20190739","relation":{},"ISSN":["1349-2543"],"issn-type":[{"value":"1349-2543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}