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Abstract Ultrawide‐bandgap semiconductors such as AlN, BN, and diamond hold tremendous promise for high‐efficiency deep‐ultraviolet optoelectronics and high‐power/frequency electronics, but their practical application has been limited by poor current conduction. Through a combined theoretical and experimental study, it is shown that a critical challenge can be addressed for AlN nanostructures by using N‐rich epitaxy. Under N‐rich conditions, the p‐type Al‐substitutional Mg‐dopant formation energy is significantly reduced by 2 eV, whereas the formation energy for N‐vacancy related compensating defects is increased by ≈3 eV, both of which are essential to achieve high hole concentrations of AlN. Detailed analysis of the current−voltage characteristics of AlN p‐i‐n diodes suggests that current conduction is dominated by hole‐carrier tunneling at room temperature, which is directly related to the activation energy of Mg dopants. At high Mg concentrations, the dispersion of Mg acceptor energy levels leads to drastically reduced activation energy for a portion of Mg dopants, evidenced by the small tunneling energy of 67 meV, which explains the efficient current conduction and the very small turn‐on voltage (≈5 V) for the diodes made of nanoscale AlN. This work shows that nanostructures can overcome the dopability challenges of ultrawide‐bandgap semiconductors and significantly increase the efficiency of devices.
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Empirical modeling of dopability in diamond-like semiconductors
Abstract Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across bothp- andn-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.
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- PAR ID:
- 10153904
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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