Machine learning-assisted design and explainable optimization of CdSnP2-based integrated solar-photodetector devices

Abstract

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.