Numerical Studies of I-V Characteristics in Resonant Tunneling Diodes: A Survey of Convergence

Resonant tunneling diodes (RTDs) exhibit a distinctive characteristic known as negative resistance. Accurately calculating the tunneling bias energy is indispensable for the design of quantum devices. This paper conducts a thorough investigation into the current-voltage (I-V) characteristics of RTDs utilizing various numerical methods. Through a series of numerical experiments, we verified that the transfer matrix method ensures robust convergence in I-V curves and proficiently determines the tunneling bias for energy potential functions with discontinuities. Our numerical analysis underscores the significant impact of variations in effective mass on I-V curves, emphasizing the need to consider this effect. Furthermore, we observe that increasing the doping concentration results in a reduction in tunneling bias and an enhancement in peak current. Leveraging the unique features of the I-V curve, we employ shallow neural networks to accurately fit the I-V curves, yielding satisfactory results with limited data.

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Publication Details

Journal
Journal of Computational Mathematics
Published
2024-12-04
DOI
https://doi.org/10.4208/jcm.2410-m2024-0037
Primary Topic
Semiconductor Quantum Structures and Devices
Type
article
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article

Numerical Studies of I-V Characteristics in Resonant Tunneling Diodes: A Survey of Convergence

Haiyan Jiang, Xingming Gao
Journal of Computational Mathematics
Semiconductor Quantum Structures and Devices
article

Numerical Studies of I-V Characteristics in Resonant Tunneling Diodes: A Survey of Convergence

Haiyan Jiang, Xingming Gao
article en

Abstract

Resonant tunneling diodes (RTDs) exhibit a distinctive characteristic known as negative resistance. Accurately calculating the tunneling bias energy is indispensable for the design of quantum devices. This paper conducts a thorough investigation into the current-voltage (I-V) characteristics of RTDs utilizing various numerical methods. Through a series of numerical experiments, we verified that the transfer matrix method ensures robust convergence in I-V curves and proficiently determines the tunneling bias for energy potential functions with discontinuities. Our numerical analysis underscores the significant impact of variations in effective mass on I-V curves, emphasizing the need to consider this effect. Furthermore, we observe that increasing the doping concentration results in a reduction in tunneling bias and an enhancement in peak current. Leveraging the unique features of the I-V curve, we employ shallow neural networks to accurately fit the I-V curves, yielding satisfactory results with limited data.

Journal of Computational MathematicsVol. 44(1)
Beijing Institute of Technology (CN), Peking University (CN), South China University of Technology (CN)
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Openalex Percentile: Top 80%
Semiconductor Quantum Structures and Devices
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Numerical Studies of I-V Characteristics in Resonant Tunneling Diodes: A Survey of Convergence — Haiyan Jiang, Xingming Gao · Journal of Computational Mathematics (2024) | TGRS Research Map | TGRS