For an accurate prediction of carbon and oxygen impurities in multicrystalline silicon material for solar cells, global simulation of coupled oxygen and carbon transport in a unidirectional solidification furnace was implemented. Both the gas flow and silicon melt flow were considered. Five chemical reactions were included during the transportation of impurities. The simulation results agreed well with experimental data. The effects of flow rate and pressure on the impurities were examined. An increase in the flow rate can reduce both carbon and oxygen impurities in the crystal, though the reduction of carbon is more obvious. An increase in gas pressure can also obviously reduce the oxygen impurity but has only a small effect on the carbon impurity.Multicrystalline silicon has now become the main material in the photovoltaic market because of its low production cost and high conversion efficiency. The unidirectional solidification method is a cost-effective technique for the large-scale production of multicrystalline silicon material. Similar to the Czochralski method for crystal growth, the unidirectional solidification method is also related to the transport of impurities. 1 The main impurities in crystal are oxygen and carbon. An effective control of oxygen and carbon concentrations in a crystal is required for the production of a high quality crystal. Experimental exploration 2-4 has been carried out, but it is a time-consuming and high cost work, and it is also complex to analyze the experimental data. Developments in computer technology have made it possible to simulate the global environments of crystal growth and to find techniques for improving the purity of crystals. Many simulations of impurity transport have been done; 5-15 however, most of them were local simulations 5-11 that neglected gas transport of impurities. There have been a few studies using global simulations. 12-15 However, the oxygen and carbon impurities in the silicon melt were neglected in one of those studies, 12 and the carbon impurity in both gas and silicon melt was neglected in other studies. [13][14][15] There have been no simulations that took into account not only the oxygen impurity but also the carbon impurity in both cooling gas and silicon melt. We therefore developed a set of analysis system that includes all of the processes in crystal growth. This set of analysis system incorporates the silicon melt flow into the global simulation of Bornside and Brown. 12 The original boundary assumption of a constant SiO concentration at the melt surface 12 is replaced by a dynamic update of SiO concentration. Therefore, this set of analysis system enables the prediction of oxygen impurity in a crystal. Another assumption of the equilibrium system in the melt, 12 i.e., the carbon flux from the gas into the melt being equal to that from the melt into the crystal, is also replaced by a local nonequilibrium consideration. The carbon flux at the gas/melt interface is calculated locally and, thus, carbon accumulation in the melt is included...

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To effectively reduce basal plane dislocations (BPDs) during SiC physical vapor transport growth, a three-dimensional model for tracking the multiplication of BPDs has been developed. The distribution of BPDs inside global crystals has been shown. The effects of the convexity of the growth surface and the cooling rate have been analyzed. The results show that the convexity of the growth surface is unfavorable and can cause a large multiplication of BPDs when the crystal grows. Fast cooling during the cooling process is beneficial for the reduction of BPDs because fast cooling can result in a smaller radial flux at the high-temperature region. In addition, fast cooling can reduce the generation of stacking faults during the cooling process. Therefore, to reduce BPDs and stacking faults, it is better to maintain or reduce the convexity of the growth surface and increase the cooling rate during the cooling process.

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into "defect" and "non-defect" classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

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