Buried screen-printed contacts for silicon solar cells

A Simple way to improve solar cell efficiency is to enhance the absorption of light and reduce the shading losses. One of the main objectives for the photovoltaic roadmap is the reduction of metalized area on the front side of solar cell by fin lines. Industrial solar cell production uses screen-printing of metal pastes with a limit in line width of 70-80 μm. This paper will show a combination of the technique of laser grooved buried contact (LGBC) and Screen-printing is able to improve in fine lines and higher aspect ratio. Laser grooving is a technique to bury the contact into the surface of silicon wafer. Metallization is normally done with electroless or electrolytic plating method, which a high cost. To decrease the relative cost, more complex manufacturing process was needed, therefore in this project the standard process of buried contact solar cells has been optimized in order to gain a laser grooved buried contact solar cell concept with less processing steps. The laser scribing process is set at the first step on raw mono-crystalline silicon wafer. And then the texturing etch; phosphorus diffusion and SiNx passivation process was needed once. While simultaneously optimizing the laser scribing process did to get better results on screen-printing process with fewer difficulties to fill the laser groove. This project has been done to make the whole production of buried contact solar cell with fewer steps and could present a cost effective opportunity to solar cell industries.

Performance determinants For Swedish Financial Monetary Institutions: Panel Data evidence between 2010-2014.

Due to the rapid changes that governs the Swedish financial sector such as financial deregulations and technological innovations, it is imperative to examine the extent to which the Swedish Financial institutions had performed amid these changes. For this to be accomplish, the work investigates what are the determinants of performance for Swedish Financial Monetary Institutions? Assumptions were derived from theoretical and empirical literatures to investigate the authenticity of this research question using seven explanatory variables. Two models were specified using Returns on Asset (ROA) and Return on Equity (ROE) as the main performance indicators and for the sake of reliability and validity, three different estimators such as Ordinary Least Square (OLS), Generalized Least Square (GLS) and Feasible Generalized Least Square (FGLS) were employed. The Akaike Information Criterion (AIC) was also used to verify which specification explains performance better while performing robustness check of parameter estimates was done by correcting for standard errors. Based on the findings, ROA specification proves to have the lowest Akaike Information Criterion (AIC) and Standard errors compared to ROE specification. Under ROA, two variables; the profit margins and the Interest coverage ratio proves to be statistically significant while under ROE just the interest coverage ratio (ICR) for all the estimators proves significant. The result also shows that the FGLS is the most efficient estimator, then follows the GLS and the last OLS. when corrected for SE robust, the gearing ratio which measures the capital structure becomes significant under ROA and its estimate become positive under ROE robust. Conclusions were drawn that, within the period of study three variables (ICR, profit margins and gearing) shows significant and four variables were insignificant. The overall findings show that the institutions strive to their best to maximize returns but these returns were just normal to cover their costs of operation. Much should be done as per the ASC theory to avoid liquidity and credit risks problems. Again, estimated values of ICR and profit margins shows that a considerable amount of efforts with sound financial policies are required to increase performance by one percentage point. Areas of further research could be how the individual stochastic factors such as the Dupont model, repo rates, inflation, GDP etc. can influence performance.