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.

中文翻译：

---

NTCDA/p-Si紫外光电二极管光电性能的经典和量子回归分析

由于紫外光电二极管在许多技术应用中的关键作用以及机器学习技术在回归和分类问题中实现的高效率，因此采用了不同的人工智能技术来模拟有机/无机异质结紫外光电二极管的性能。在本文中，详细解释了制造的 Au/NTCDA/p-Si/Al 光电二极管的性能，并显示出出色的响应性，对强度范围为 20 至 80 ${mW/cm^2}$ 的紫外光的检测率。制造的光电二极管在高达 65 ${mW/cm^2}$ 的光照下表现出线性电流-辐照度关系。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 IV 曲线，还评估了三种经典算法；除了使用量子神经网络来预测制造设备的行为之外，还有 k-最近邻、人工神经网络和遗传编程。模型取得了优异的成绩，并成功捕捉到了目标值的趋势。量子神经网络首次用于对光电二极管进行建模。可以使用模型代替重复制造过程。这意味着成本和制造时间的减少。可以使用模型代替重复制造过程。这意味着成本和制造时间的减少。可以使用模型代替重复制造过程。这意味着成本和制造时间的减少。