Advanced manufacturing concepts for crystalline silicon solar cells

Nijs, Johan; Szlufcik, Jozef; Poortmans, Jef; Sivoththaman, Siva; Mertens, R.

doi:10.1109/16.791983

Cited by 101 publications

(40 citation statements)

References 84 publications

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“…An obvious approach towards reducing the cost of silicon photovoltaic modules is to reduce the cost of the silicon substrate, which accounts for 40-55% of the current module cost. 1,2 The PC1D simulation of the effect of bulk lifetime (τ) and back surface recombination velocity (BSRV) in Fig. 1 shows that high efficiency screen-printed solar cells can be fabricated by using relatively low-quality, but thin substrates (100 µm), if a high quality surface passivation treatment is applied.…”

Section: Introductionmentioning

confidence: 99%

Al-enhanced PECVD SiNx induced hydrogen passivation in string ribbon silicon

Yelundur

Rohatgi

Ebong

et al. 2001

Journal of Elec Materi

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Section: Introductionmentioning

confidence: 99%

Al-enhanced PECVD SiNx induced hydrogen passivation in string ribbon silicon

Yelundur

Rohatgi

Ebong

et al. 2001

Journal of Elec Materi

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“…More research is necessary to further develop lowcost manufacturing processes for high-efficiency solar cells (Rohatgi et al 1994). Previous works by several investigators have proposed various techniques to address these issues (Nijs et al 1999). …”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, data driven modeling techniques have been developed and applied to real world problems. Least squares estimation for linear models that is one of the curve fitting methods is employed for solar cell modeling, and as another early research, a statistical method was used for modeling (Green et al 1984;Rohatgi et al 1984;Nijs et al 1999;Mottet 1980). These approaches could give good performance when there is uncertainty in the measurement data (Agostinellia et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

et al. 2009

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This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.

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“…This is in agreement with FTIR results: hydrogen diffuses-out mainly by breaking Si-H bonds (3.1 eV) for Si-rich SiN x films with a low stoichiometry x and less by breaking N-H bonds (4.1 eV) for N-rich SiN x films. The released hydrogen is of importance for deactivating the electrically active defects in solar-grade silicon substrates [35] [36]. The bulk passivation efficiency is not necessarily determined by the amount of desorbed hydrogen during annealing.…”

Section: Structural Properties: Rbs Erda Infrared Measurementsmentioning

confidence: 99%