论文标题
NTCDA/P-SI UV光电二极管的光电性能的经典和量子回归分析
Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode
论文作者
论文摘要
由于紫外光二极管在许多技术应用中的关键作用与机器学习技术在回归和分类问题中所获得的高效率相结合，因此采用了不同的人工智能技术模型，模型的有机/无机异质结uv uv uv uv photodiode的性能。在此，详细说明了制造的AU/NTCDA/P-SI/AL光电二极管的性能，并显示出极好的响应性，并且对强度的紫外线的检测范围为20至80 $ {mw/cm^2} $。制造的光二极管在照明下表现出线性电流 - 辐射关系，最高65 $ {mw/cm^2} $。它还表现出$ {t_ {rise} = 408} $ ms和$ {t_ {fall} = 490} $ ms的良好响应时间。此外，我们不仅拟合了特征I-V曲线，而且还评估了三种经典算法。 K-nearest邻居，人工神经网络和遗传编程除了使用量子神经网络来预测制造设备的行为。这些模型取得了出色的结果，并设法捕获了目标值的趋势。量子神经网络已首次使用对光电二极管进行建模。可以使用模型，而不是重复制造过程。这意味着减少成本和制造时间。
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.
