Predicting Photodetector Responsivity through Machine Learning

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. This study introduces a methodology for predicting photodetector responsivity. A comprehensive approach leveraging advanced machine learning techniques is developed. 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.