Design and Analysis of Near-IR Photodetector Using Machine Learning Approach

Customization of silicon photodiodes (SiPDs) for specific target wavelengths has become increasingly important in the rapidly evolving domains of optical communication, medical devices, and autonomous driving technologies. This study pioneers the use of machine learning (ML) for the structural optimization of SiPDs, leveraging simulation data from Synopsys technology computer-aided design (TCAD) to identify optimal configurations without the need for extensive physical prototyping. We aim to predict photodiode performance across a range of design parameters by employing XGBoost (XGB) and light gradient-boosting machine (LGBM) algorithms, thus streamlining the design and development process for high-performance, cost-effective devices. Our findings indicate that the LGBM algorithm provides a more accurate prediction of photodiode responsivity than XGB, as evidenced by lower root mean square error (RMSE) values. Specifically, the LGBM model achieved an RMSE of
$0.282\times 10^{\mathbf {-{4}}}$
for datasets comprising 50% of the data, markedly outperforming the XGB model, which posted an RMSE of
$0.754\times 10^{\mathbf {-{3}}}$
under comparable conditions. This work highlights the potential of employing ML in the optimization process of SiPDs, offering a path toward reducing the resources required for device design and optimization. The approach demonstrates the effectiveness of ML in accelerating the development of photonic devices, enabling more efficient and targeted optimization of photodiode structures for specific applications.