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Related Concept Videos

Carrier Transport01:21

Carrier Transport

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The generation of electrical current in semiconductors is fundamentally driven by two mechanisms: drift and diffusion. These processes are essential for the functionality and performance of semiconductor-based devices.
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A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
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Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
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Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
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Machine learning based charge mobility prediction for organic semiconductors.

Tianhao Tan1, Dong Wang1

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Machine learning models predict charge mobility in organic semiconductors accurately and efficiently. This accelerates the calculation of transfer integrals, crucial for understanding charge transport in materials like pentacene and rubrene.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Organic Electronics

Background:

  • Transfer integrals dictate charge mobility in organic semiconductors, but their quantum chemical calculation is computationally expensive.
  • Molecular packing significantly influences transfer integrals and charge transport properties.

Purpose of the Study:

  • To develop accurate and efficient machine learning models for predicting transfer integrals in organic semiconductors.
  • To accelerate the study of charge transport in organic materials, including those with dynamic disorders.

Main Methods:

  • Artificial neural networks were employed to build predictive models for transfer integrals.
  • A data augmentation scheme was implemented to enhance model accuracy.
  • Models were validated against quantum chemical calculations for molecules like quadruple thiophene (QT), pentacene, rubrene, and DNTT.

Main Results:

  • Machine learning models achieved high accuracy, with a determination coefficient of 0.97 and a mean absolute error of 4.5 meV for QT.
  • Accurate predictions of charge mobility and anisotropy were obtained for organic crystals with dynamic disorders at 300 K.
  • The models demonstrated excellent agreement with rigorous quantum chemical calculations.

Conclusions:

  • Machine learning offers an efficient alternative to traditional quantum chemical calculations for transfer integrals in organic semiconductors.
  • The developed models can be refined to study charge transport in amorphous organic solids, thin films, and materials with static disorders.