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

• Published in: arXiv.org 2021-04
• Main Authors: El-Mahalawy, Ahmed M, El-Safty, Kareem H
• Format: Article
• Language: English
• Subjects: Aluminum; Artificial intelligence; Artificial neural networks; Computer simulation; Continuity (mathematics); Genetic algorithms; Heterojunctions; Illumination; Irradiance; Learning theory; Luminous intensity; Machine learning; Neural networks; Optoelectronics; Photodiodes; Regression analysis; Silicon; Squeezed states (quantum theory); Ultraviolet radiation
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2004.01257

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 model the performance of organic/inorganic heterojunction UV photodiode. Herein, the performance of a fabricated Au/NTCDA/p-Si/Al photodiode was explained in details and showed an excellent responsivity, and detectivity for UV light of intensities ranges from 20 to 80 \({mW/cm^2}\). The fabricated photodiodes exhibited a linear current-irradiance relationship under illumination up to 65 \({mW/cm^2}\). It also exhibits good response times of \({t_{rise} = 408}\) ms and \({t_{fall} = 490}\) ms. 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 behaviour of the fabricated 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. The models can be used instead of repeating the fabrication process. This means a reduction in cost and manufacturing time.