We report on the tunnel oxide passivated contact (TOPCon) using a crystalline nanostructured silicon-based layer via an experimental and numerical simulation study. The minority carrier lifetime and implied open-circuit voltage reveals an ameliorated passivation property, which gives the motivation to run a simulation. The passivating contact of an ultra-thin silicon oxide (1.2 nm) capped with a plasma enhanced chemical vapor deposition (PECVD) grown 30 nm thick nanocrystalline silicon oxide (nc-SiO_x), provides outstanding passivation properties with low recombination current density (J_o) (~1.1 fA/cm²) at a 950 °C annealing temperature. The existence of a thin silicon oxide layer (SiO₂) at the rear surface with superior quality (low pinhole density, D_ph < 1 × 10⁻⁸ and low interface trap density, D_it ≈ 1 × 10⁸ cm⁻² eV⁻¹), reduces the recombination of the carriers. The start of a small number of transports by pinholes improves the fill factor (FF) up to 83%, reduces the series resistance (Rs) up to 0.5 Ω cm², and also improves the power conversion efficiency (PEC) by up to 27.4%. The TOPCon with a modified nc-SiO_x exhibits a dominant open circuit voltage (V_oc) of 761 mV with a supreme FF of 83%. Our simulation provides an excellent match with the experimental results and supports excellent passivation properties. Overall, our study proposed an ameliorated knowledge about tunnel oxide, doping in the nc-SiO_x layer, and additionally about the surface recombination velocity (SRV) impact on TOPCon solar cells.

In addition to the different technologies of silicon solar cells in crystalline form, TOPCon solar cells have an exceptionally great efficiency of 26%, accomplished by the manufacturing scale technique for industrialization, and have inordinate cell values of 732.3 mV open-circuit voltage (V_oc) and a fill factor (FF) of 84.3%. The thickness of tunnel oxide, which is less than 2 nm in the TOPCon cell, primarily affects the electrical properties and efficiency of the cell. In this review, various techniques of deposition were utilized for the layer of SiO_x tunnel oxide, such as thermal oxidation, ozone oxidation, chemical oxidation, and plasma-enhanced chemical vapor deposition (PECVD). To monitor the morphology of the surface, configuration of annealing, and rate of acceleration, a tunnel junction structure of oxide through a passivation quality of better V_oc on a wafer of n-type cell might be accomplished. The passivation condition of experiments exposed to rapid thermal processing (RTP) annealing at temperatures more than 900 °C dropped precipitously. A silicon solar cell with TOPCon technology has a front emitter with boron diffusion, a tunnel-SiO_x/n⁺-poly-Si/ SiN_x:H configuration on the back surface, and electrodes on both sides with screen printing technology. The saturation current density (J₀) for such a configuration on a refined face remains at 1.4 fA/cm² and is 3.8 fA/cm² when textured surfaces of the cell are considered, instead of printing with silver contacts. Following the printing of contacts with Ag, the J₀ of the current configuration improves to 50.8 fA/cm² on textured surface of silicon, which is moderately lesser for the metal contact. Tunnel oxide layers were deposited using many methods such as chemical, ozone, thermal, and PECVD oxidation are often utilized to deposit the thin SiO_x layer in TOPCon solar cells. The benefits and downsides of each approach for developing a SiO_x thin layer depend on the experiment. Thin SiO_x layers may be produced using HNO₃:H₂SO₄ at 60 °C. Environmentally safe ozone oxidation may create thermally stable SiO_x layers. Thermal oxidation may build a tunnel oxide layer with low surface recombination velocity (10 cm/s). PECVD oxidation can develop SiO_x on several substrates at once, making it cost-effective.

Calcium fluoride (CaF₂) is deposited via vacuum thermal evaporation on borosilicate glass to produce an anti-reflection coating for use in solar modules. Macleod’s essential simulation is used to optimize the thickness of the CaF₂ coating on the glass. Experimentally, a 120 ± 4 nm-thin CaF₂ film on glass shows an average increase of ~4% in transmittance and a decrease of ~3.2% in reflectance, respectively, when compared to that of uncoated glass (Un CG), within the wavelength spectrum of approximately 350 to 1100 nm. The electrical PV performance of CaF₂-coated glass (CaF₂-CG) was analyzed for conventional and lightweight photovoltaic module applications. An improvement in the short-circuit current (J_sc) from 38.13 to 39.07 mA/cm² and an increase of 2.40% in the efficiency (η) was obtained when CaF₂-CG glass was used instead of Un CG in a conventional module. Furthermore, J_sc enhancement from 35.63 to 36.44 mA/cm² and η improvement of 2.32% was observed when a very thin CaF₂-CG was placed between the polymethyl methacrylate (PMMA) and solar cell in a lightweight module.

A 60-cell photovoltaic (PV) module was analyzed by optimizing the interconnection parameters of the solar cells to enhance the efficiency and increase the power of the PV module setup. The cell-to-module (CTM) losses and gains varied substantially during the various simulation iterations. Optimization was performed to inspect and augment the gain and loss parameters for the 60-cell PV module. The power and efficiency of the module were improved by refining several parameters, such as number of busbars, size of the contact pads, interconnected ribbon width, thickness of the core, and distance between the solar cells and strings, to obtain the maximum efficiency of 21.09%; the CTM efficiency achieved was 94.19% for the proposed strategy related to the common interconnection setup of the ribbon-based system. The CTM efficiency was improved by optimizing the geometrical, optical, and electrical parameters precisely, the power enhancement was up to 325.3 W, and a CTM power of 99.1% was achieved from a standard PV module with rectangular ribbon interconnections.

The need for innovative design and materials is increasing for various types of photovoltaic (PV) installations in building integrated PV, agricultural, and floating systems. It is crucial to reduce the weight of the PV module to maximize its use in such applications. For this purpose, the front surface must be made of a polymer-based material instead of tempered glass. This study focuses on the analysis of the optical and ultraviolet (UV) reliability properties of various lightweight polymer front sheets. The results show that the transmittance and UV properties of the front material are good. Moreover, a PV module with a polymer front sheet rather than glass was constructed, and a characteristic investigation as well as UV reliability test were performed. The transmittance of the polycarbonate (PC) front sheet decreased by only <3% and the module fabricated with PC exhibited only an approximately 6% power drop after the UV reliability test; hence, the PC is suitable for use in the PV module industry.

The thickness of the indium tin oxide (ITO) layer is a critical parameter affecting the performance of solar cells. Traditional measurement methods require sample collection, leading to manufacturing interruptions and potential quality issues. In this paper, we propose a real-time, non-contact approach using deep learning and optical interference phenomena to estimate the thickness of ITO layers in solar cells. We develop a convolutional neural network (CNN) model that processes microscopic images of solar cells and predicts the ITO layer thickness. In addition, mean absolute error (MAE) and mean squared error (MSE) loss functions are combined to train the model. Experimental results demonstrate the effectiveness of our approach in accurately estimating the ITO layer thickness. The integration of computer vision and deep learning techniques provides a valuable tool for non-destructive testing and quality control in the manufacturing of solar cells. The loss of the model after training is reduced to 0.83, and the slope of the test value in the scatter plot with the true value of the ellipsometer is approximately equal to 1, indicating the high reliability of the model.