Multiband Photodetection Using TiO2 Thin Film Deposited on Si Substrate Using E-beam Evaporation Technique

Singh, Salam Surjit; Shougaijam, Biraj

doi:10.1007/978-981-19-2308-1_15

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The dataset used in this study consisted of about 1700 samples with 162 features collected from 24 experimental articles focusing on experimental fabrication of photodetectors. [ 30,32,36,38–58 ] The dataset included information on both the active layer and substrate materials of each fabricated photodetector, which encompasses the following features: ‘A_Bandgap(ev)’ : The experimentally measured bandgap energy of the active layer material reported in the related articles. ‘A_Theoretical_Bandgap(ev)’ : The theoretical bandgap energy of the active layer material obtained from the Materials Project database. [ 59 ] ‘Thickness(nm)’ : The thickness of the active layer material reported in nanometers. ‘Wavelength(nm)’ : Refers to the specific wavelength of the incident light utilized during the operation of the photodetector. ‘Responsivity(AW −1 )’ : The responsivity of the photodetector, indicating its sensitivity to incident light power. ‘PowerIntensity(

\mu {\rm Wcm}^{-2}

)’ : The intensity of the incident light measured in microwatts per square centimeter. ‘Bias(V)’ : The absolute amount of negatively bias voltage applied to the photodetector during testing. ‘Aabsint’, ‘AabsX1’, ‘AabsX2’,…, ‘AabsX6’ : Parameters obtained from fitting a polynomial degree six curve to the light absorption spectra of the active layer materials.…”

Section: Methodsmentioning

confidence: 99%

Predicting Photodetector Responsivity through Machine Learning

Arjmandi‐Tash,

Mansourian,

Rahsepar

et al. 2024

Advcd Theory and Sims

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This study introduces a novel methodology for predicting photodetector responsivity, specifically targeting challenging materials like borophene. The synthesis of these materials faces substantial experimental complexities, necessitating reliable performance predictions before fabrication. To address this, a comprehensive approach leveraging advanced machine learning techniques, specifically artificial neural networks (ANN), is developed. Integration of X‐ray diffraction (XRD) and Raman spectra data into AI models enables efficient prediction of photodetector efficiency prior to device fabrication. The innovation lies in strategically incorporating Generative Adversarial Networks (GANs) for dataset augmentation, significantly expanding the dataset size and enhancing the robustness of the ANN model. Sensitivity analyses highlighted influential factors such as bias voltage and spectral coefficients, validating the approach and aligning with recent experimental findings. This methodology not only advances optoelectronics, but also holds promise for materials science and device engineering. Predictions for Wavelength‐Responsivity plots, considering borophene allotropes as active layers and n‐Si as substrates, show peaks around 300–400 nm, ranging from 0.04 to 0.36 AW−1 at bias voltages between 1 and 5 volts. These estimations assume a borophene layer thickness of approximately 1.6 nm and a radiation power intensity of 5000 µ Wcm−2.

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