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An investigation on enhanced surface passivation in the existing industrial process line of large area n-type silicon (Si) Passivated Emitter Rear Totally diffused ( n- PERT) solar cell has been performed. The Rapid Thermal Process (RTP) optimization for 20 min is conducted in the temperature range of 500–900°C and device evaluation is carried out with respect to regularly processed n-PERT solar cell. The impact of pre-metallization annealing is studied with the support of cell parameters like shunt resistance, reverse saturation current density determined from current-voltage measurements. The enhanced surface passivation via hydrogenation from silicon nitride (SiNx) layer during annealing is established with the help of external quantum efficiency, spectral response measurements and Fourier transform infrared spectroscopy analysis. The addition of optimized annealing resulted in improvement by 550% (from 38 to 247 µs), 7.73% (from 630.7 to 678.8 mV) and 84.77% (from 223.3 to 34 cm/s) in effective minority carrier lifetime, implied open circuit voltage and surface recombination velocity respectively. Finally, RTP technique for optimized process line has been successfully incorporated in industrial high-volume batch of 140898 CZ n-type Si wafers, which predicts conceptual validation of the study in mass scale production line with an increment in average efficiency of the device by 0.35%.

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Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.