Organic electronics have great potential due to their flexible structure, high performance, and their ability to build effective and low-cost photodetectors. We investigated the parameters of the P3HT and PCBM layers for device performance and optimization. SCAPS-1D simulations were employed to optimize the thicknesses of the P3HT and PCBM layers, investigate the effects of shallow doping in the P3HT layer, and assess the influence of the back contact electrode’s work function on device performance. Furthermore, this study explored the impact of interface defect layer density on the characteristics of the device. Through systematic analyses, the optimal parameters for enhancing device responsivity were identified. The findings indicate that a P3HT layer thickness of 1200 nm, a PCBM layer thickness of 20 nm, and a back contact electrode with a work function of 4.9 eV achieve the highest responsivity. Notably, at a bias of −0.5 V, the responsivity exceeds 0.4 A/W within the wavelength range of 450 nm to 630 nm. These optimized parameters underscore the significant potential of the developed device as an organic photodetector, particularly for visible light detection.

This study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the electron transport layer, hole transport layer, and perovskite active layer. These coefficients serve as a new figure of merit, facilitating numerical studies on degradation and stability in PSCs, mainly focusing on cesium lead halides. Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R² value of 0.9991, indicating near-perfect predictive accuracy. The comparative analysis of model complexity confirmed that increasing the sophistication of the LSTM model significantly enhances predictive accuracy and generalization capabilities. Identifying crucial design parameters offers actionable insights for optimizing PSC designs, materials selection, and operational conditions, ultimately contributing to enhanced long-term stability and efficiency of PSCs. Future research should prioritize using experimental datasets to achieve more realistic predictions, thereby driving innovation and unlocking the full potential of machine learning and deep learning in optimizing PSC design and performance.

This study investigates the effect of charge transport layers on the efficiency of Poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH_PPV) and Zirconium Disulfide (ZrS₂) solar cells using Scaps-1D software. It was found that by increasing the MEH-PPV thickness and decreasing its acceptor doping concentration, the efficiency (μ%), fill factor (FF), and short-circuit current density (Jsc ) decreased. Conversely, increasing the thickness of the ZrS₂ electron transport layer and decreasing its donor doping density enhanced the efficiency (μ%) and short-circuit current density (Jsc) while maintaining a constant open-circuit voltage (Voc). These results can be attributed to decreased charge separation and collection in MEH-PPV and reduced optical path length in ZrS2. On the other hand, the back contact with work function is below 4.65 eV, the MEH-PPV/ZrS2 solar cells produced the lowest efficiency compared to different types of back contact. Under optimal conditions, MEH-PPV/ZrS2 solar cell shows a high efficiency of 21% when the dopant concentration of MEH-PPV and the value of the neutral defect density at the ZrS2/ MEH-PPV interface are 1022 cm-3 and 109 cm-3 respectively.

This study presents a SCAPS-1D-based numerical optimization of an organic ultraviolet (UV) photodetector employing an FTO/PTB7/Spiro-OMeTAD/Au device architecture. The novelty of this work lies in a simulation-guided, UV-specific optimization strategy that combines thickness engineering, controlled doping, and contact work-function tuning to achieve intrinsic spectral selectivity without external optical filters. We systematically optimize material and device parameters, including active layer thicknesses, donor and acceptor densities, and the metal electrode work function, to enhance responsivity, detectivity, and spectral performance. Simulations identify optimal thicknesses of 1200 nm for PTB7 and 1000 nm for Spiro-OMeTAD, with donor concentrations of 1 × 10²⁰ cm⁻³ and 1 × 10¹⁸ cm⁻³, respectively. A comparative contact analysis demonstrates that replacing aluminum with gold (Au) forms a near-ohmic back contact, leading to improved hole extraction and suppressed dark current due to favorable energy-level alignment. The optimized device achieves a peak external quantum efficiency of approximately 80% in the 300–400 nm ultraviolet range, with a responsivity up to 0.4 A/W. The UV selectivity originates from the absorption characteristics of PTB7 combined with suppressed long-wavelength charge collection, resulting in a negligible response in the visible–near-infrared region. These results confirm the device’s strong potential for high-sensitivity, solar-blind UV photodetection. By integrating practical material selection with physically consistent SCAPS-1D optoelectronic modeling, this work provides a robust design framework to guide the development of next-generation organic UV photodetectors for environmental sensing, biomedical diagnostics, and wearable optoelectronics.