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Year 2022, Volume 23, Issue 3, 223 - 232, 27.09.2022
https://doi.org/10.18038/estubtda.1149416

Abstract

References

  • [1] Chang L, Frank DJ, Montoye RK, Koester SJ, Ji BL, Coteus PW, et al. Practical strategies for power-efficient computing technologies. Proceedings of the IEEE. 2010;98:215-36.
  • [2] Park JH, Jang GS, Kim HY, Seok KH, Chae HJ, Lee SK, et al. Sub-kT/q subthreshold-slope using negative capacitance in low-temperature polycrystalline-silicon thin-film transistor. Scientific reports. 2016;6:1-9.
  • [3] Attia KM, El-Hosseini MA, Ali HA. Dynamic power management techniques in multi-core architectures: A survey study. Ain Shams Engineering Journal. 2017;8:445-56.
  • [4] Chhowalla M, Jena D, Zhang H. Two-dimensional semiconductors for transistors. Nature Reviews Materials. 2016;1:1-15.
  • [5] Kong W, Kum H, Bae S-H, Shim J, Kim H, Kong L, et al. Path towards graphene commercialization from lab to market. Nature nanotechnology. 2019;14:927-38.
  • [6] Novoselov KS, Geim AK, Morozov SV, Jiang D-e, Zhang Y, Dubonos SV, et al. Electric field effect in atomically thin carbon films. science. 2004;306:666-9.
  • [7] Yi M, Shen Z. A review on mechanical exfoliation for the scalable production of graphene. Journal of Materials Chemistry A. 2015;3:11700-15.
  • [8] Bonaccorso F, Lombardo A, Hasan T, Sun Z, Colombo L, Ferrari AC. Production and processing of graphene and 2d crystals. Materials Today. 2012;15:564-89.
  • [9] Zhang Y, Yao Y, Sendeku MG, Yin L, Zhan X, Wang F, et al. Recent progress in CVD growth of 2D transition metal dichalcogenides and related heterostructures. Advanced materials. 2019;31:1901694.
  • [10] Aras FG, Yilmaz A, Tasdelen HG, Ozden A, Ay F, Perkgoz NK, et al. A review on recent advances of chemical vapor deposition technique for monolayer transition metal dichalcogenides (MX2: Mo, W; S, Se, Te). Materials Science in Semiconductor Processing. 2022;148:106829.
  • [11] Liu D, Chen X, Yan Y, Zhang Z, Jin Z, Yi K, et al. Conformal hexagonal-boron nitride dielectric interface for tungsten diselenide devices with improved mobility and thermal dissipation. Nature communications. 2019;10:1188.
  • [12] Lin Z, McCreary A, Briggs N, Subramanian S, Zhang K, Sun Y, et al. 2D materials advances: from large scale synthesis and controlled heterostructures to improved characterization techniques, defects and applications. 2D Materials. 2016;3:042001.
  • [13] Özden A, Şar H, Yeltik A, Madenoğlu B, Sevik C, Ay F, et al. CVD grown 2D MoS2 layers: A photoluminescence and fluorescence lifetime imaging study. physica status solidi (RRL)–Rapid Research Letters. 2016;10:792-6.
  • [14] Zhang J, Yu Y, Wang P, Luo C, Wu X, Sun Z, et al. Characterization of atomic defects on the photoluminescence in two‐dimensional materials using transmission electron microscope. InfoMat. 2019;1:85-97.
  • [15] Yorulmaz B, Özden A, Şar H, Ay F, Sevik C, Perkgöz NK. CVD growth of monolayer WS2 through controlled seed formation and vapor density. Materials Science in Semiconductor Processing. 2019;93:158-63.
  • [16] Lin X, Si Z, Fu W, Yang J, Guo S, Cao Y, et al. Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy. Nano Research. 2018;11:6316-24.
  • [17] Saito Y, Shin K, Terayama K, Desai S, Onga M, Nakagawa Y, et al. Deep-learning-based quality filtering of mechanically exfoliated 2D crystals. npj Computational Materials. 2019;5:1-6.
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  • [19] Shinde PP, Shah S. A Review of Machine Learning and Deep Learning Applications. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)2018. p. 1-6.
  • [20] Shorten C, Khoshgoftaar TM, Furht B. Deep Learning applications for COVID-19. Journal of Big Data. 2021;8:1-54.
  • [21] Masubuchi S, Machida T. Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning. npj 2D Materials and Applications. 2019;3:1-7.
  • [22] Bharati S, Podder P, Mondal M. Artificial neural network based breast cancer screening: a comprehensive review. arXiv preprint arXiv:200601767. 2020.
  • [23] Masubuchi S, Watanabe E, Seo Y, Okazaki S, Sasagawa T, Watanabe K, et al. Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials. npj 2D Materials and Applications. 2020;4:1-9.
  • [24] Yao G, Lei T, Zhong J. A review of convolutional-neural-network-based action recognition. Pattern Recognition Letters. 2019;118:14-22.
  • [25] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data. 2021;8:1-74.
  • [26] Bhuvaneswari V, Priyadharshini M, Deepa C, Balaji D, Rajeshkumar L, Ramesh M. Deep learning for material synthesis and manufacturing systems: a review. Materials Today: Proceedings. 2021;46:3263-9.
  • [27] Blake P, Hill E, Castro Neto A, Novoselov K, Jiang D, Yang R, et al. Making graphene visible. Applied physics letters. 2007;91:063124.
  • [28] Zhang W, Zhao Q, Puebla S, Wang T, Frisenda R, Castellanos-Gomez A. Optical microscopy–based thickness estimation in thin GaSe flakes. Materials Today Advances. 2021;10:100143.
  • [29] Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat's striate cortex. The Journal of physiology. 1959;148:574.
  • [30] Fukushima K, Miyake S. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets: Springer; 1982. p. 267-85.
  • [31] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 1998;86:2278-324.
  • [32] Refractive index database https://refractiveindex.info/

CHARACTERIZATION OF ARTIFICIALLY GENERATED 2D MATERIALS USING CONVOLUTIONAL NEURAL NETWORKS

Year 2022, Volume 23, Issue 3, 223 - 232, 27.09.2022
https://doi.org/10.18038/estubtda.1149416

Abstract

İki boyutlu (2B) malzemeler, bu malzemelerin ürettiği cihazların yüksek performans göstermesi nedeniyle birçok araştırmacının ilgisini çekmiştir. 2B malzeme elde etmek için solüsyon tabanlı kimyasal sentez, kimyasal buhar biriktirme (KBB), moleküler ışın epitaksisi, atomik katman biriktirme, darbeli lazer biriktirme (DLB) gibi işlemler sırasında saatler gerektiren farklı üretim yöntemleri bulunmaktadır. 2B yapılar elde edildikten sonra, kusurları da dahil olmak üzere özellikleri farklı karakterizasyon araçları ile ortaya çıkarılmalıdır. Karakterizasyon süreci de üretim süreci gibi zaman ve uzmanlık gerektirir. Bu açıdan, üretilen örneklerin pratik ve hızlı sınıflandırılması için Evrişimli Sinir Ağları (ESA) gibi derin öğrenme yöntemleri bir çözüm olabilir. Ancak, yukarıda bahsedilen uzun deneysel süreçler nedeniyle araştırma laboratuvarlarının çoğunda yeterli sayıda örnek bulunmamaktadır. Bu çalışmada, farklı elektronik uygulamalarda yüksek potansiyele sahip, yaygın olarak çalışılan bir 2B yarı iletken olan MoS2'nin yapay olarak oluşturulmuş görüntülerini kullanan bir CNN algoritmasının performansı sunulmaktadır. Normal ve kusurlu MoS2 pullarını içeren sentetik optik mikroskobik görüntüler, Fresnel Denklemleri kullanılarak farklı malzemeler üzerine gelen ışığın yoğunlukları ile oluşturulur. Normal ve kusurlu örnekleri tespit etmek için derin bir CNN algoritması oluşturulmuştur. Doğruluk ölçümlerinin sonuçları, CNN'nin gelecekte yeterli sayıda gerçek görüntü ile iki boyutlu malzemelerin karakterizasyonu için kullanılabileceğini göstermiştir.

References

  • [1] Chang L, Frank DJ, Montoye RK, Koester SJ, Ji BL, Coteus PW, et al. Practical strategies for power-efficient computing technologies. Proceedings of the IEEE. 2010;98:215-36.
  • [2] Park JH, Jang GS, Kim HY, Seok KH, Chae HJ, Lee SK, et al. Sub-kT/q subthreshold-slope using negative capacitance in low-temperature polycrystalline-silicon thin-film transistor. Scientific reports. 2016;6:1-9.
  • [3] Attia KM, El-Hosseini MA, Ali HA. Dynamic power management techniques in multi-core architectures: A survey study. Ain Shams Engineering Journal. 2017;8:445-56.
  • [4] Chhowalla M, Jena D, Zhang H. Two-dimensional semiconductors for transistors. Nature Reviews Materials. 2016;1:1-15.
  • [5] Kong W, Kum H, Bae S-H, Shim J, Kim H, Kong L, et al. Path towards graphene commercialization from lab to market. Nature nanotechnology. 2019;14:927-38.
  • [6] Novoselov KS, Geim AK, Morozov SV, Jiang D-e, Zhang Y, Dubonos SV, et al. Electric field effect in atomically thin carbon films. science. 2004;306:666-9.
  • [7] Yi M, Shen Z. A review on mechanical exfoliation for the scalable production of graphene. Journal of Materials Chemistry A. 2015;3:11700-15.
  • [8] Bonaccorso F, Lombardo A, Hasan T, Sun Z, Colombo L, Ferrari AC. Production and processing of graphene and 2d crystals. Materials Today. 2012;15:564-89.
  • [9] Zhang Y, Yao Y, Sendeku MG, Yin L, Zhan X, Wang F, et al. Recent progress in CVD growth of 2D transition metal dichalcogenides and related heterostructures. Advanced materials. 2019;31:1901694.
  • [10] Aras FG, Yilmaz A, Tasdelen HG, Ozden A, Ay F, Perkgoz NK, et al. A review on recent advances of chemical vapor deposition technique for monolayer transition metal dichalcogenides (MX2: Mo, W; S, Se, Te). Materials Science in Semiconductor Processing. 2022;148:106829.
  • [11] Liu D, Chen X, Yan Y, Zhang Z, Jin Z, Yi K, et al. Conformal hexagonal-boron nitride dielectric interface for tungsten diselenide devices with improved mobility and thermal dissipation. Nature communications. 2019;10:1188.
  • [12] Lin Z, McCreary A, Briggs N, Subramanian S, Zhang K, Sun Y, et al. 2D materials advances: from large scale synthesis and controlled heterostructures to improved characterization techniques, defects and applications. 2D Materials. 2016;3:042001.
  • [13] Özden A, Şar H, Yeltik A, Madenoğlu B, Sevik C, Ay F, et al. CVD grown 2D MoS2 layers: A photoluminescence and fluorescence lifetime imaging study. physica status solidi (RRL)–Rapid Research Letters. 2016;10:792-6.
  • [14] Zhang J, Yu Y, Wang P, Luo C, Wu X, Sun Z, et al. Characterization of atomic defects on the photoluminescence in two‐dimensional materials using transmission electron microscope. InfoMat. 2019;1:85-97.
  • [15] Yorulmaz B, Özden A, Şar H, Ay F, Sevik C, Perkgöz NK. CVD growth of monolayer WS2 through controlled seed formation and vapor density. Materials Science in Semiconductor Processing. 2019;93:158-63.
  • [16] Lin X, Si Z, Fu W, Yang J, Guo S, Cao Y, et al. Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy. Nano Research. 2018;11:6316-24.
  • [17] Saito Y, Shin K, Terayama K, Desai S, Onga M, Nakagawa Y, et al. Deep-learning-based quality filtering of mechanically exfoliated 2D crystals. npj Computational Materials. 2019;5:1-6.
  • [18] LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521:436-44.
  • [19] Shinde PP, Shah S. A Review of Machine Learning and Deep Learning Applications. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)2018. p. 1-6.
  • [20] Shorten C, Khoshgoftaar TM, Furht B. Deep Learning applications for COVID-19. Journal of Big Data. 2021;8:1-54.
  • [21] Masubuchi S, Machida T. Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning. npj 2D Materials and Applications. 2019;3:1-7.
  • [22] Bharati S, Podder P, Mondal M. Artificial neural network based breast cancer screening: a comprehensive review. arXiv preprint arXiv:200601767. 2020.
  • [23] Masubuchi S, Watanabe E, Seo Y, Okazaki S, Sasagawa T, Watanabe K, et al. Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials. npj 2D Materials and Applications. 2020;4:1-9.
  • [24] Yao G, Lei T, Zhong J. A review of convolutional-neural-network-based action recognition. Pattern Recognition Letters. 2019;118:14-22.
  • [25] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data. 2021;8:1-74.
  • [26] Bhuvaneswari V, Priyadharshini M, Deepa C, Balaji D, Rajeshkumar L, Ramesh M. Deep learning for material synthesis and manufacturing systems: a review. Materials Today: Proceedings. 2021;46:3263-9.
  • [27] Blake P, Hill E, Castro Neto A, Novoselov K, Jiang D, Yang R, et al. Making graphene visible. Applied physics letters. 2007;91:063124.
  • [28] Zhang W, Zhao Q, Puebla S, Wang T, Frisenda R, Castellanos-Gomez A. Optical microscopy–based thickness estimation in thin GaSe flakes. Materials Today Advances. 2021;10:100143.
  • [29] Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat's striate cortex. The Journal of physiology. 1959;148:574.
  • [30] Fukushima K, Miyake S. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets: Springer; 1982. p. 267-85.
  • [31] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 1998;86:2278-324.
  • [32] Refractive index database https://refractiveindex.info/

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Cahit PERKGÖZ> (Primary Author)
ESKISEHIR TECHNICAL UNIVERSİTY
0000-0003-0424-7046
Türkiye


Mehmet Zahit ANGİ>
ESKİŞEHİR TEKNİK ÜNİVERSİTESİ
0000-0002-7019-1637
Türkiye

Supporting Institution Eskişehir Teknik Üniversitesi
Project Number 22ADP144
Thanks This work was supported by Eskişehir Technical University Research Project no: 22ADP144.
Publication Date September 27, 2022
Published in Issue Year 2022, Volume 23, Issue 3

Cite

AMA Perkgöz C. , Angi M. Z. CHARACTERIZATION OF ARTIFICIALLY GENERATED 2D MATERIALS USING CONVOLUTIONAL NEURAL NETWORKS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. 2022; 23(3): 223-232.