Relationships between Diffusion Parameters and Phosphorus Precipitation during the POCl3 Diffusion Process

Dastgheib-Shirazi, Amir; Steyer, M.; Micard, Gabriel; Wagner, Hannes; Altermatt, Pietro P.; Hahn, Giso

doi:10.1016/j.egypro.2013.07.275

Cited by 48 publications

(40 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the value of n ieff = n i = 8.27 × 10 9 cm −3 (see Table I) in the bulk consistently throughout this work for conductive boundary modeling, neglecting the small amount of bandgap planar with some scattering, modeled by Phong reflection with R 0 = 0.7 and w = 4 [45], [46] metal grid front fingers 75 fingers with 2.08-mm-pitch, 60-μm-wide, and 12-μm-high rectangular shape Ag-paste with 4.5-μΩ · cm resistivity contact resistivity 2-mΩ · cm 2 lumped series resistance: 0.312 Ω · cm 2 front busbars 3 busbars, 1300-μm-wide and 15-μm-high Ag-paste with 4.5-μΩ · cm resistivity lumped series resistance: neglected rear metal full area Al-paste with 35-μΩ · cm resistivity, 30-μm-high contact resistivity 5-mΩ · cm 2 lumped series resistance: neglected electrical properties bulk 2-Ω · cm p-type mc mid-gap SRH lifetimes τ n = τ p = 75 μs front n + diffusion lumped inputs sheet resistance 75 Ω (70-90) recombination parameter J 0 passivated: 201 fA/cm 2 (150-300) contacted: 204 fA/cm 2 (200-600) depth 0.39 μm collection efficiency 0.85 (0.8-0.9). detailed inputs doping profile total and active concentration from [38], see Appendix B surface SRH passivated: S p = 6.63 × 10 6 cm/s, S n = 1 × 10 7 cm/s (derived from [39] with texture multiplier of 1.73) contacted: S p = S n = 1 × 10 7 cm/s (thermal velocity) volume SRH modeled via inactive phosphorus profile according to [39] and a texture multiplier of 1.73 rear p + region lumped inputs sheet resistance 30 Ω (20-40) recombination parameter J 0 = 517 fA/cm 2 (300-700) depth 7-μm collection efficiency 0.7 detailed inputs doping profile measured active concentration from [40] with incomplete ionization model from [45], see Appendix B surface SRH S p = S n = 1 × 10 7 cm/s (thermal velocity) volume SRH modeled as in [41] with parameters in [42] narrowing. J 0 values derived with a different assumption of n ieff are scaled according to J 0 /n ieff 2 = const [22].…”

Section: General Input Parametersmentioning

confidence: 99%

Input Parameters for the Simulation of Silicon Solar Cells in 2014

Fell

McIntosh²,

Altermatt

et al. 2015

IEEE J. Photovoltaics

Self Cite

153

View full text Add to dashboard Cite

Within the silicon photovoltaics (PV) community, there are many approaches, tools, and input parameters for simulating solar cells, making it difficult for newcomers to establish a complete and representative starting point and imposing high requirements on experts to tediously state all assumptions and inputs for replication. In this review, we address these problems by providing complete and representative input parameter sets to simulate six major types of crystalline silicon solar cells. Where possible, the inputs are justified and up-to-date for the respective cell types, and they produce representative measurable cell characteristics. Details of the modeling approaches that can replicate the simulations are presented as well. The input parameters listed here provide a sensible and consistent reference point for researchers on which to base their refinements and extensions.

show abstract

Section: General Input Parametersmentioning

confidence: 99%

Input Parameters for the Simulation of Silicon Solar Cells in 2014

Fell

McIntosh²,

Altermatt

et al. 2015

IEEE J. Photovoltaics

Self Cite

153

View full text Add to dashboard Cite

show abstract

“…(a) 0 measurements (red crosses) as a function of POCl 3 flow during the diffusion process, from Ref [30] and later reevaluated in Ref. [29].…”

Section: Figmentioning

confidence: 99%

PC1Dmod 6.1 – state-of-the-art models in a well-known interface for improved simulation of Si solar cells

Haug

Greulich

Kimmerle

et al. 2015

Solar Energy Materials and Solar Cells

View full text Add to dashboard Cite

In this paper, we present a new, updated version of the commonly used semiconductor device simulator PC1D named PC1Dmod 6.1. The new program is based on the previously published command line version cmd-PC1D 6.0, which has implemented several new options related to the device physics, but now uses an updated version of the original PC1D graphical user interface. The program thus provides the possibility for using Fermi-Dirac statistics and a selection of state-of-the-art models for crystalline silicon, including injection-dependent band gap narrowing, carrier mobility and Auger recombination, in a familiar setting.Version 6.1 also has implemented the recently published band gap narrowing model by Yan and Cuevas, which is based on empirical studies of a large selection of both n + and p + emitters, in addition to Schenk's model. It has also implemented the mobility model for compensated material by Schindler et al. Finally, the maximum number of nodes, time steps and wavelengths has been increased in order to reduce unnecessary constraints on simulations and external files.The results from the PC1Dmod 6.1 simulations have been compared with those of other simulation tools and with previously published data to verify the correct implementation of the new models. Emitter saturation currents calculated using PC1Dmod 6.1 showed an excellent agreement with those obtained using the emitter recombination calculator EDNA 2, and the new program was able to successfully reproduce previously published experimental data and previous implementations of the models. Both PC1Dmod 6.1 and the command line version cmd-PC1D 6.1 are open source software, and are freely available for download.

show abstract

“…While there are many demands on a phosphorus diffusion profile designed for a high-performance emitter [3], such as achieving a low emitter saturation current, high V oc , and good blue response, iron typically dominates the minoritycharge carrier lifetime in the bulk in p-type mc-Si [4]. Current PDG of contaminated mc-Si solar cell materials generally fails to remove significant amounts of precipitated iron [5] and produces material with minority-carrier lifetimes that do not approach that of phosphorus-diffused monocrystalline wafers [6].…”

mentioning

confidence: 99%

Investigation of Lifetime-Limiting Defects After High-Temperature Phosphorus Diffusion in High-Iron-Content Multicrystalline Silicon

Fenning

Zuschlag

Hofstetter

et al. 2014

IEEE J. Photovoltaics

Self Cite

View full text Add to dashboard Cite

Abstract-Phosphorus diffusion gettering of multicrystalline silicon solar cell materials generally fails to produce material with minority-carrier lifetimes that approach that of gettered monocrystalline wafers, due largely to higher levels of contamination with metal impurities and a higher density of structural defects. Higher gettering temperatures should speed the dissolution of precipitated metals by increasing their diffusivity and solubility in the bulk, potentially allowing for improved gettering. In this paper, we investigate the impact of gettering at higher temperatures on low-purity multicrystalline samples. To analyze the gettering response, we measure the spatially resolved lifetime and interstitial iron concentration by microwave photoconductance decay and photoluminescence imaging, and the structural defect density by Sopori etching and large-area automated quantification. Higher temperature phosphorus diffusion gettering is seen to improve metal-limited multicrystalline materials dramatically, especially in areas of low etch pit density. In areas of high as-grown dislocation density in the multicrystalline materials, it appears that higher temperature phosphorus diffusion gettering reduces the etch pit density, but leaves higher local concentrations of interstitial iron, which degrade lifetime.

show abstract

Relationships between Diffusion Parameters and Phosphorus Precipitation during the POCl3 Diffusion Process

Cited by 48 publications

References 8 publications

Input Parameters for the Simulation of Silicon Solar Cells in 2014

Input Parameters for the Simulation of Silicon Solar Cells in 2014

PC1Dmod 6.1 – state-of-the-art models in a well-known interface for improved simulation of Si solar cells

Investigation of Lifetime-Limiting Defects After High-Temperature Phosphorus Diffusion in High-Iron-Content Multicrystalline Silicon

Contact Info

Product

Resources

About