  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.
