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<p>This study presents an investigation into the interface-driven optoelectronic performance of Cu/BB/p-Si photodiode, combining experimental characterization with machine learning (ML) insights. The Brilliant Blue (BB) organic interlayer was found to play a dual role in passivating the Si surface and manipulating the interlayer carrier extraction process, which led to notable increase in the barrier height (Φ<sub>b</sub>) from 0.541 to 0.571&#xa0;eV and modulating the ideality factor (n) from 2.260 to 2.761. Under a simulated AM1.5 full-spectrum solar illumination of 100 mW/cm<sup>2</sup>, the Cu/BB/p-Si sensor demonstrated a high broadband detectivity of 1.467 × 10<sup>12</sup> Jones, a remarkable responsivity of 2.082&#xa0;A/W and a linear dynamic range of 50.92 dB. To validate these physical observations, four ML algorithms were developed using Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Random Forest Regression (RFR) and Support Vector Regression (SVR) based on experimental current-voltage (I-V) data under dark and varying illumination intensities (40–100 mW/cm<sup>2</sup>). All ML models were evaluated, where the GPR model achieved the closest agreement with experimental data. The model’s robustness, highlighted by a perfect a10-index of 1.0, stems from its superior ability to capture the non-linear dependencies and interface-governed transport mechanisms facilitated by the BB layer. These results demonstrate that the synergy of interface engineering and data-driven modeling provides a powerful framework for optimizing next-generation surface-based optoelectronic technologies.</p>

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Deciphering the optoelectronic evolution and interfacial kinetics of brilliant blue/p-Si photodetectors: synergistic experimental and machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Harun Kıran,
  • Fikriye Ataman,
  • Arife Gencer Imer,
  • Abdulkadir Korkut

摘要

This study presents an investigation into the interface-driven optoelectronic performance of Cu/BB/p-Si photodiode, combining experimental characterization with machine learning (ML) insights. The Brilliant Blue (BB) organic interlayer was found to play a dual role in passivating the Si surface and manipulating the interlayer carrier extraction process, which led to notable increase in the barrier height (Φb) from 0.541 to 0.571 eV and modulating the ideality factor (n) from 2.260 to 2.761. Under a simulated AM1.5 full-spectrum solar illumination of 100 mW/cm2, the Cu/BB/p-Si sensor demonstrated a high broadband detectivity of 1.467 × 1012 Jones, a remarkable responsivity of 2.082 A/W and a linear dynamic range of 50.92 dB. To validate these physical observations, four ML algorithms were developed using Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Random Forest Regression (RFR) and Support Vector Regression (SVR) based on experimental current-voltage (I-V) data under dark and varying illumination intensities (40–100 mW/cm2). All ML models were evaluated, where the GPR model achieved the closest agreement with experimental data. The model’s robustness, highlighted by a perfect a10-index of 1.0, stems from its superior ability to capture the non-linear dependencies and interface-governed transport mechanisms facilitated by the BB layer. These results demonstrate that the synergy of interface engineering and data-driven modeling provides a powerful framework for optimizing next-generation surface-based optoelectronic technologies.