CHARACTERIZATION OF ARTIFICIALLY GENERATED 2D MATERIALS USING CONVOLUTIONAL NEURAL NETWORKS

Year 2022, Volume: 23 Issue: 3, 223 - 232, 27.09.2022

Cahit Perkgöz Mehmet Zahit Angi

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.

Keywords

MoS2, Two-dimensional Materials, Fresnel Equations, Characterization, Deep Learning

Supporting Institution

Eskişehir Teknik Üniversitesi

Project Number

22ADP144

Thanks

This work was supported by Eskişehir Technical University Research Project no: 22ADP144.

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