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This repository contains the notebooks that can reproduce the results presented in the paper "Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode"

License: Creative Commons Attribution 4.0 International

Jupyter Notebook 92.24% Python 7.76%
quantum-machine-learning regression-analysis automated-machine-learning organic-semiconductors photodiode quantum-regression quantum-photonics quanutm-computing quantum-chemistry

classical-and-quantum-regression-analysis-for-the-optoelectronic-performance-of-ntcda's Introduction

Classical and quantum regression analysis for the optoelectronic performance of NTCDA

Dataset Visualization


The optical absorbance of NTCDA thin film on a quartz substrate was measured at normal incidence of the light at room temperature in the spectral range of 190–2500 nm using a double beam spectrophotometer (JASCO model V-570 UV-VIS-NIR). The Photoelectrical properties – of the fabricated Au/NTCDA/p-Si/Al photodiode – were investigated by measuring the I-V characteristic curve at room temperature from +3.5 to -3.4, using Keithley electrometer model 6517B under the influence of UV light of wavelength 194 nm. The intensity of incident light was measured using Radiometer/Photometer model IL1400A.


Environment Setup

These are the packages used in the code development

pip install pennylane==0.10.0 strawberryfields==0.14.0 pennylane-sf==0.9.0 tensorflow==2.2.0 tpot==0.11.5 xgboost==0.90 keras

Notebooks

  1. ANN
  2. KNN
  3. TPOT
  4. QNN

Authors

Ahmed M. El-Mahalawy, Kareem H. El-Safty

How to Cite

If you extend, use this work or the dataset, please cite the paper where it was introduced:

@article{ELMAHALAWY2021167793,
title = {Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode},
journal = {Optik},
pages = {167793},
year = {2021},
issn = {0030-4026},
doi = {https://doi.org/10.1016/j.ijleo.2021.167793},
url = {https://www.sciencedirect.com/science/article/pii/S0030402621013826},
author = {Ahmed M. El-Mahalawy and Kareem H. El-Safty}
}

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0

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