Accurate measurements of the silicon intrinsic carrier density from 78 to 340 K

Μισιακός, Κωνσταντίνος; Tsamakis, D.

doi:10.1063/1.354551

Cited by 118 publications

(58 citation statements)

References 3 publications

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“…4(b). It implies that BGN starts to have an effect at very low dopant densities, so Sproul and Green's experiment yielded n i,eff instead of n i , and n i turned out to be n i = 9.65(3) × 10 9 cm −3 [83], consistent with the value of Misiakos and Tsamakis [82]. The fact that Schenk's BGN model was successful had two further implications.…”

Section: Models For Band Parameterssupporting

confidence: 65%

“…4(b), so the average was taken, which is n i = 1.00(3) × 10 10 cm −3 [81] (the digit in the parentheses represents the estimated onestandard-deviation uncertainty in the last digit of the previous value). However, this value was significantly higher than 9.7(1) × 10 9 cm −3 as determined with admittance measurements by Misiakos and Tsamakis [82] and is shown as square symbol in Fig. 4(b).…”

Section: Models For Band Parametersmentioning

confidence: 80%

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Models for numerical device simulations of crystalline silicon solar cells—a review

2011

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Current issues of numerical modeling of crystalline silicon solar cells are reviewed. Numerical modeling has been applied to Si solar cells since the early days of computer modeling and has recently become widely used in the photovoltaics (PV) industry. Simulations are used to analyze fabricated cells and to predict effects due to device changes. Hence, they may accelerate cell optimization and provide quantitative data e.g. of potentially possible improvements, which may form a base for the decision making on development strategies. However, to achieve sufficiently high prediction capabilities, several models had to be refined specifically to PV demands, such as the intrinsic carrier density, minority carrier mobility, recombination at passivated surfaces, and optical models. Currently, the most unresolved issue is the modeling of the emitter layer on textured surfaces, the hole minority carrier mobility, Auger recombination at low dopant densities and intermediate injection levels, and fine-tuned band parameters as a function of temperature. Also, it is recommended that the widely used software in the PV community, PC1D should be extended to FermiDirac statistics.

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Section: Models For Band Parameterssupporting

confidence: 65%

Section: Models For Band Parametersmentioning

confidence: 80%

Models for numerical device simulations of crystalline silicon solar cells—a review

2011

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“…To achieve high-efficiency screen-printed n-type silicon solar cells, an optimal boron emitter is needed to balance emitter sheet resistance, contact resistance and emitter saturation current density J oe shown in Eq. (13). This can be achieved by flexibly controlling implant energy, boron ion dose and post-implantation annealing conditions.…”

Section: Boron Emitter Formationmentioning

confidence: 99%

“…where the intrinsic carrier concentration n _ i = 8.3 × 10^9 cm^( − 3) [13]. N D is the doping density of n-type silicon bulk region, i.e., N _ D = 9.2 × 10^14 cm^( − 3) for a bulk resistivity of 5 Ω cm.…”

Section: ( )mentioning

confidence: 99%

Screen‐Printed Front Junction n‐Type Silicon Solar Cells

Tao¹

2016

Printed Electronics - Current Trends and Applications

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This chapter aims to provide students/engineers/scientists in the field of photovoltaics with the basic information needed to understand the operating principles of screenprinted front junction n-type silicon solar cells. The relevant device fabrication processing is described, from texturing, diffusion, passivation and antireflection coating, to screenprinted and fired-through metallization as well as the technologies that are currently used for most industrially produced solar cells. A brief description of the characterisation approaches is given and discussed for an understanding and analysis of the loss mechanisms in a finished cell, including resistance loss, recombination loss, and optical loss. The application of advanced cell concepts and the improved technologies for further increasing cell efficiency, such as selectively doping structure and tunnel oxide passivated contact, are addressed for screen-printed front junction n-type silicon solar cells.

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“…(5.5) temperature dependence can be modeled by including the n i vs. temperature relation in Eq. (5.5) as described by [101]:…”

Section: Temperature Dependencementioning

confidence: 99%

Field-plate assisted RESURF power devices : gradient based optimalization, degradation and analysis

Boksteen¹

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Accurate measurements of the silicon intrinsic carrier density from 78 to 340 K

Cited by 118 publications

References 3 publications

Models for numerical device simulations of crystalline silicon solar cells—a review

Models for numerical device simulations of crystalline silicon solar cells—a review

Screen‐Printed Front Junction n‐Type Silicon Solar Cells

Field-plate assisted RESURF power devices : gradient based optimalization, degradation and analysis

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