Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode
This work addresses the problem of optimizing UV photodiode manufacturing for applications in technology by using AI to model performance, though it is incremental as it applies existing methods to a new dataset.
The study tackled modeling the performance of an Au/NTCDA/p-Si/Al UV photodiode using machine learning, achieving excellent responsivity and detectivity for UV light intensities from 20 to 80 mW/cm², with response times of 408 ms rise and 490 ms fall, and successfully applied classical and quantum neural networks to predict device behavior, reducing the need for repeated fabrication.
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.