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<title>Abstract</title> <p> All-inorganic cesium lead halide perovskite/MoS₂ heterostructure photodetectors offer exceptional potential for broadband UV-Visible detection, yet the systematic optimization of graded halide composition profiles for simultaneous maximization of responsivity, detectivity, and quantum efficiency has not been achieved through artificial intelligence methods. This study presents the first XGBoost-multi-objective Bayesian Optimization (MOBO)-SHAP framework for functionally graded CsPbBr₃₋ₓClₓ/MoS₂ perovskite-2D heterostructure photodetector optimization. A high-fidelity dataset of 2,200 SCAPS-1D device simulations is generated using Latin Hypercube Sampling across six design variables: Cl gradient steepness, perovskite thickness, MoS₂ thickness, HTL thickness, defect density, and annealing temperature. The XGBoost surrogate achieves R² &gt; 0.995 for simultaneous prediction of UV responsivity (R <sub>UV</sub> ), Visible responsivity (R <sub>Vis</sub> ), specific detectivity (D*), external quantum efficiency (EQE), response time (τ), and dark current density (J <sub>dark</sub> ). MOBO with Expected Hypervolume Improvement identifies a Pareto-optimal functionally graded Cl composition profile achieving R <sub>UV</sub> = 1.2 × 10⁴ A/W at 365 nm, R <sub>Vis</sub> = 8.4 × 10³ A/W at 520 nm, D* = 6.8 × 10¹³ Jones, EQE = 91.4%, and τ = 0.82 ms — surpassing all published CsPbBr₃/MoS₂ photodetector benchmarks. SHAP analysis identifies Cl gradient steepness as the dominant parameter for UV responsivity (mean |SHAP| = 0.44) through bandgap engineering that extends absorption to 300 nm via a quasi-electric field of 4.2 × 10⁴ V/cm, while MoS₂ thickness governs dark current suppression (0.42) through enhanced band alignment and reduced interface recombination. The XGBoost-MOBO-SHAP framework reduces optimization computational cost by 96.8% relative to full SCAPS-1D parametric sweeps, providing practical CVD/spin-coating process guidelines for next-generation broadband perovskite-2D photodetector arrays. </p>

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optimization responsivity thickness graded photodetector

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