کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5446642 | 1511143 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Al0.2Ga0.8As Solar Cells Monolithically Grown on Si and GaAs by MBE for III-V/Si Tandem Dual-junction Applications
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
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چکیده انگلیسی
Al0.2Ga0.8As photovoltaic solar cells have been monolithically grown on silicon substrates by Molecular Beam Epitaxy. Due to the 4% lattice mismatch between AlGaAs and Si, Threading Dislocations (TDs) nucleate at the III-V/Si interface and propagate to the active region of the cells where they act as recombination centers, reducing the performances of the devices. In order to reduce the Threading Dislocation Density (TDD) in the active layers of the cells, InAlAs Strained Layer Superlattice (SLS) Dislocation Filter Layers (DFLs) have been used. For one of the samples, in-situ Thermal Cycle Annealing (TCA) steps have additionally been performed during growth. For comparison purposes, reference Al0.2Ga0.8As solar cells have been grown lattice-matched on GaAs. For the sample grown on Si without TCA, the TDD has been reduced from over 7Ã109cm-2 at the III-V/Si interface to 3Ã107cm-2 in the base of the cells. With TCA, the TDD has been reduced throughout the sample from over 3Ã109cm-2 in the initial epilayers to 8(±2)Ã106cm-2 in the base of the cells. For the best devices, the Voc improves from 833mV on Si without TCA to 895mV using TCA, compared with 1070mV for the reference sample grown lattice-matched on GaAs. Similarly the fill factor improves from 73.7% on Si without TCA to 74.8% using TCA, compared with 78.4% on GaAs. The high bandgap-voltage offset obtained both on Si and GaAs indicates a non-optimal bulk AlGaAs material quality due to non-ideal growth conditions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Procedia - Volume 92, August 2016, Pages 661-668
Journal: Energy Procedia - Volume 92, August 2016, Pages 661-668
نویسندگان
Arthur Onno, Jiang Wu, Qi Jiang, Siming Chen, Mingchu Tang, Yurii Maidaniuk, Mourad Benamara, Yuriy I. Mazur, Gregory J. Salamo, Nils-Peter Harder, Lars Oberbeck, Huiyun Liu,