Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode

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@Article{ELMAHALAWY2021167793,

• author = "Ahmed M. El-Mahalawy and Kareem H. El-Safty",
• title = "Classical and quantum regression analysis for the optoelectronic performance of {NTCDA/p-Si UV} photodiode",
• journal = "Optik",
• year = "2021",
• volume = "246",
• pages = "167793",
• keywords = "genetic algorithms, genetic programming, Organic Semiconductors, Heterojunction Photodiode, Machine Learning, Quantum Machine Learning",
• ISSN = "0030-4026",
• URL = "https://www.sciencedirect.com/science/article/pii/S0030402621013826",
• DOI = "doi:10.1016/j.ijleo.2021.167793",
• size = "21 pages",
• abstract = "Due to the pivotal role of UV photodiodes in many technological applications in tandem with the high efficiency achieved by machine learning techniques in regression and classification problems, different artificial intelligence techniques are adopted to simulate and model the performance of organic/inorganic heterojunction UV photodiode. Herein, the performance of a fabricated Au/NTCDA/p-Si/Al photodiode is explained in a detailed manner and has shown an excellent responsivity and detectivity for UV light of intensities ranging from 20 to 80mW/cm2. A linear current irradiance relationship is exhibited by the fabricated photodiode under illumination up to 65mW/cm2. It also shows good response times of trise=408ms and tfall=490ms. Furthermore, we have not only fitted the characteristic I-V curve but also evaluated three classical algorithms; K-Nearest Neighbour, Artificial Neural Network, and Genetic Programming besides using a Quantum Neural Network to predict the behavior of the device. The models have achieved outstanding results and managed to capture the trend of the target values. The Quantum Neural Network has been used for the first time to model the photodiode characteristics. The trained models are of great significance since they can be used to reduce the characterization and measurement times.",
• notes = "Thin Film Laboratory, Physics Department, Faculty of Science, Suez Canal University, Ismailia, Egypt",

}

Genetic Programming entries for Ahmed M El-Mahalawy Kareem H El-Safty

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