Ultra-Thin GaAs Double-Junction Solar Cell With Carbon-Doped Emitter

Ren, Zekun; Thway, Maung; Liu, Zhe; Wang, Yue; Ke, Changjian; Yaung, Kevin Nay; Wang, Bing; Tan, Chuan Seng; Lin, Fen; Aberle, Armin G.; Buonassisi, Tonio; Peters, Ian Marius

doi:10.1109/jphotov.2018.2870721

Cited by 3 publications

(2 citation statements)

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“…For example, in a very recent report, Ling et al reported an untra-thin GaAs solar cell with 19.9% efficiency [14]. Ultra-thin solar cells are usually only few hundred nanometers thick and have several advantages such as reduced material consumption, increased fabrication throughput, lower radiation damage, high defect tolerance, reduced bulk recombination, and photon recycling [10][11][12][13][15][16][17][18]. So far, researchers have only used conventional solar cell structures for fabrication of these ultra-thin III-V solar cells.…”

Section: Introductionmentioning

confidence: 99%

“…So far, researchers have only used conventional solar cell structures for fabrication of these ultra-thin III-V solar cells. In a conventional solar cell structure, a base (intrinsic) layer is sandwiched between heavily doped n + and p + layers to achieve charge carrier separation, along with a heavily doped wide bandgap window layer for surface passivation and carrier selectivity [10][11][12][13][15][16][17][18]. These doped layers, along with the window layer, add to the complexity as well as the final cost of the III-V solar cell.…”

Section: Introductionmentioning

confidence: 99%

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Design of Ultrathin InP Solar Cell Using Carrier Selective Contacts

Raj

Rougieux

et al. 2020

IEEE J. Photovoltaics

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Most recently, III-V based ultra-thin solar cells have attracted much attention for their inherent advantages such as increased tolerance to defect recombination, efficient charge carrier separation, photon recycling, flexibility, and reduced material consumption. However, so far, almost all reported devices make use of conventional doped p-i-n kind of structures with a wide bandgap III-V lattice-matched epitaxial window layer, for passivation and reduced contact recombination. Here, we show that a high-efficiency device can be obtained utilizing an InP thin film of thickness as low as 280 nm, without the requirement of a conventional p-n homojunction or epitaxial window layer. This is achieved by utilizing a wide bandgap electron and hole selective contacts for electrons and holes transport, respectively. Under ideal conditions (assuming Interface Recombination Velocity (IRV) = 10 3 cm/s and bulk lifetime =1 us), the proposed solar cell structure can achieve efficiency as high as 28%. Although, in the presence of bulk and interface SRH recombination, the efficiency reduces, still for bulk minority carrier lifetime as low as 2ns and an IRV as high as 10 5 cm/s, an efficiency of ~22% can be achieved with InP thickness as low as 280 nm. The proposed device structure will be beneficial in cases where the growth of controlled p-n homojunction and window layer can be tedious as in case of low-cost deposition techniques, such as thin-film vapour-liquidsolid (TF-VLS) and close-spaced vapour transport (CSVT).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Ultrathin InP Solar Cell Using Carrier Selective Contacts

Raj

Rougieux

et al. 2020

IEEE J. Photovoltaics

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Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

et al. 2020

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Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, without full transparency of the underlying physics, and with user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond global black-box 2 optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device-performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time-consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layerby-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.

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