1.73 eV AlGaAs/InGaP heterojunction solar cell grown by MBE with 18.7% efficiency

Slimane, Ahmed Ben; Michaud, Amadeo; Mauguin, O.; Lafosse, Xavier; Bercegol, Adrien; Lombez, Laurent; Harmand, J. C.; Collin, Stéphane

doi:10.1002/pip.3249

Cited by 6 publications

(5 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This number is limited as we used a cell fabricated with an unoptimized process and lacking anti-reflection coating. Solar cells with a similar stack and an optimized process fabricated by our team exhibited more than twice the efficiency, reaching 18.7% 28 .…”

Section: Fabrication Of the Solar Cellmentioning

confidence: 95%

“…This type of solar cell, fabricated following the procedure of ref. 28 (see Methods), with a 1.73 eV bandgap, performs better than conventional siliconbased cells under low-illumination conditions, making it particularly suitable for extreme edge applications. Most solar cells for outdoor application are made of silicon, with a bandgap of 1.1eV nearly optimal for the photovoltaic conversion of sunlight.…”

Section: Powering the System With Harvested Energymentioning

confidence: 99%

“…The fabrication of solar cells in this study was carried out according to the procedures described in ref. 28. The semiconductor stack was grown on a GaAs substrate using molecular beam epitaxy and consisted of the following sequence of layers: p-GaAs:Be (300 nm), p-Al 0.51 GaAs:Be (50 nm), p-AlGaAs:Be with a linear gradient from 51% to 25% Al (100 nm), p-Al 0.25 GaAs:Be (1900 nm), n-Al 0.3 GaAs:Si (100 nm), n-InGaP:Si (50 nm), n-AlInP:Si (20 nm), and n-GaAs:Si (300 nm) (see Fig.…”

Section: Fabrication Of the Solar Cellmentioning

confidence: 99%

See 2 more Smart Citations

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Jebali,

Majumdar,

Turck

et al. 2024

Nat Commun

Self Cite

View full text Add to dashboard Cite

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

show abstract

Section: Fabrication Of the Solar Cellmentioning

confidence: 95%

Section: Powering the System With Harvested Energymentioning

confidence: 99%

Section: Fabrication Of the Solar Cellmentioning

confidence: 99%

See 1 more Smart Citation

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Jebali,

Majumdar,

Turck

et al. 2024

Nat Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…The incorporation of residual oxygen from the reactor chamber or the carrier gas occurs, 34) driven by the solid Al-O bonding. These undesired impurities behave as deep-level recombination centers 35) and would gravely degrade cell performance by reducing the carrier diffusion length, especially when the Al fraction is large, which explains the poor luminescence in Al 0.37 Ga 0.63 As found by both Slimane 17) and Heckelman. 35) A temperature-graded growth of AlGaAs was introduced in the present study for the first time to avoid oxygen incorporation at the AlGaAs surface during temperature adjustment: soon after the growth of the AlGaAs base layer, a TGL AlGaAs layer was grown during temperature ramping from 700 °C to 560 °C, the growth rate of which was reduced to 0.6 μm h −1 to obtain a thin TGL in the 8 min cooling down.…”

Section: Temperature-graded Algaas Growthmentioning

confidence: 99%

Al_0.35Ga_0.65As/InGaP heterojunction solar cell based on temperature-graded growth

Gan

Sodabanlu

Watanabe

et al. 2023

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

The p-Al0.35Ga0.65As/n-InGaP heterojunction solar cells are promising competitors compared with conventional InGaP or AlGaAs solar cells as the heterogeneous combination can overcome the demerits of each material. However, InGaP has an optimized growth temperature much lower than that of AlGaAs in metal-organic vapor phase epitaxy (MOVPE) growth. Therefore, a challenge arises from oxygen contamination at the hetero-interface during temperature adjustment for obtaining MOVPE-grown high-quality p-AlGaAs/n-InGaP heterojunction. Here we report that a temperature-graded layer (TGL) of AlGaAs can solve the issue of growth temperature mismatch, and significantly improve the voltage performance of AlGaAs/InGaP solar cells. Absolute electroluminescence (EL) measurement and atomic force microscopy (AFM) confirmed a smooth interface with less non-radiative recombination after TGL was applied. The temperature-graded growth is verified able to improve the AlGaAs/InGaP interface quality and provides a scalable method for AlGaAs-based heterogeneous growth.

show abstract

“…[9] Ben Slimane et al further worked to conduct InGaP/AlGaAs heterojunction to overcome the DX centers in n-AlGaAs. [10] In the state-of-the-art six-junction cell, Al 0.23 Ga 0.77 As was also engaged to function as one of the top absorbers for its proper bandgap. [6,11]…”

mentioning

confidence: 99%

Low‐Temperature Photoluminescence Investigation of Carbon‐Doped AlGaAs Grown by MOVPE on Vicinal Substrates

Li,

Sodabanlu,

Hino

et al. 2023

Physica Status Solidi (a)

View full text Add to dashboard Cite

AlGaAs is promising absorber material for multi‐junction solar cells (MJSC) for its tunable bandgap to match various bandgap combination designs. However, the path to achieving high‐quality AlGaAs for solar cell application remains to be determined. In this study, we grow and characterize carbon‐doped p‐Al0.375Ga0.625As on exact and vicinal GaAs(0 0 1) substrates, using metal‐organic vapor phase epitaxy (MOVPE). Low‐temperature photoluminescence (PL) measurement is performed in the range from 6 K to 300 K. The Arrhenius plot of PL intensities reveals an increasing trend for non‐radiative recombination in conjunction with increasing miscut angle. The presence of atomic ordering that inversely correlates to the miscut angle is proposed to explain the PL linewidth and peak energy dependency on substrate orientation. The conflicting tendency of non‐radiative recombination and ordering with miscut angle are further validated using solar cell devices. A slightly vicinal substrate is recommended for AlGaAs solar cells according to our observation.

show abstract

1.73 eV AlGaAs/InGaP heterojunction solar cell grown by MBE with 18.7% efficiency

Cited by 6 publications

References 62 publications

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Al_0.35Ga_0.65As/InGaP heterojunction solar cell based on temperature-graded growth

Low‐Temperature Photoluminescence Investigation of Carbon‐Doped AlGaAs Grown by MOVPE on Vicinal Substrates

Contact Info

Product

Resources

About

1.73 eV AlGaAs/InGaP heterojunction solar cell grown by MBE with 18.7% efficiency

Cited by 6 publications

References 62 publications

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Al0.35Ga0.65As/InGaP heterojunction solar cell based on temperature-graded growth

Low‐Temperature Photoluminescence Investigation of Carbon‐Doped AlGaAs Grown by MOVPE on Vicinal Substrates

Contact Info

Product

Resources

About

Al_0.35Ga_0.65As/InGaP heterojunction solar cell based on temperature-graded growth