机器学习辅助的CdSnP2基集成太阳能-光电探测器器件的设计与可解释优化
Machine learning-assisted design and explainable optimization of CdSnP2-based integrated solar-photodetector devices
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中文总结 AI 辅助
该研究采用机器学习辅助的SCAPS-1D框架优化CdSnP2基集成太阳能-光电探测器，确定最优结构并通过引入CGS背表面场层显著提升性能，揭示带隙偏移等对器件性能的影响，为多功能光电器件加速设计提供方法
中文摘要 AI 辅助
采用混合机器学习（ML）辅助的SCAPS-1D框架，对以CdS为窗口层、CuGaSe2（CGS）为背表面场（BSF）层的CdSnP2基集成太阳能电池-光电探测器（SC-PD）器件展开研究。通过改变各层的厚度、掺杂浓度和缺陷密度进行器件优化，利用SCAPS生成的数据训练6个机器学习模型和1个深度学习模型，其中集成算法的预测精度最高。经机器学习引导的优化从13种候选结构中确定n-CdS/p-CdSnP2（CTP）/p+-CGS结构为最优构型。引入200nm厚的CGS背表面场层可显著提升光伏与光电探测性能，使效率从20.67%提升至32.69%，响应度从0.53 AW-1提升至0.72 AW-1，探测率从2.51×10^14 Jones提升至1.78×10^16 Jones。SHapley加性解释（SHAP）分析显示，带隙偏移工程（尤其是窗口层/吸收层及吸收层/背表面场层界面处的带隙偏移）与吸收层特性共同决定器件性能。这些结果表明，CdSnP2及所提出的数据驱动型SCAPS-ML框架在加速设计高效率多功能光电器件方面具有潜力。
英文摘要
CdSnP2-based integrated solar cell-photodetector (SC-PD) devices employing CdS and CuGaSe2 (CGS) as the window and back surface field (BSF) layers, respectively are investigated using a hybrid machine learning (ML)-assisted SCAPS-1D framework. Device optimization is performed by varying the thickness, doping concentration, and defect density of individual layers. SCAPS-generated data are used to train six ML models and one deep learning model, with ensemble-based algorithms exhibiting the highest predictive accuracy. The ML-guided optimization identifies the n-CdS/p-CdSnP2 (CTP)/p+-CGS architecture as the optimum configuration among thirteen candidate structures. Incorporation of a 200 nm CGS BSF layer significantly enhances both photovoltaic and photodetection performance, increasing the efficiency from 20.67% to 32.69%, responsivity from 0.53 AW-1 to 0.72 AW-1, and detectivity from 2.51x1014 Jones to 1.78x1016 Jones. SHapley Additive exPlanations (SHAP) analysis reveales that band-offset engineering, particularly at the window/absorber and absorber/BSF interfaces, together with absorber properties, governs device performance. These findings demonstrate the potential of CdSnP2 and the proposed data-driven SCAPS-ML framework for the accelerated design of high-efficiency multifunctional optoelectronic devices.
发表机构
- University of Rajshahi（拉杰沙希大学）
- Varendra University（文达瑞大学）
- University of Dhaka（达卡大学）
机构由 AI 辅助整理，请以论文原文为准。
