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Band Theory02:35

Band Theory

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When two or more atoms come together to form a molecule, their atomic orbitals combine and molecular orbitals of distinct energies result. In a solid, there are a large number of atoms, and therefore a large number of atomic orbitals that may be combined into molecular orbitals. These groups of molecular orbitals are so closely placed together to form continuous regions of energies, known as the bands.
The energy difference between these bands is known as the band gap.
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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
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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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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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Fermi Level Dynamics01:12

Fermi Level Dynamics

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The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
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Bonding in Metals02:32

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Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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CHIPS-TB: Evaluating Tight-Binding Models for Metals, Semiconductors, and Insulators.

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A new computational framework, CHIPS-TB, evaluates tight-binding models for semiconductor materials. It benchmarks various parametrizations against DFT and experimental data, improving material property prediction for nanoscale semiconductor design.

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Area of Science:

  • Computational materials science
  • Semiconductor physics
  • Electronic structure theory

Background:

  • Nanoscale semiconductor technology demands efficient material property prediction.
  • Tight-binding (TB) methods offer a computationally tractable alternative to DFT for large systems.
  • Existing TB models face challenges in transferability and standardized benchmarking.

Purpose of the Study:

  • Introduce CHIPS-TB, a framework for evaluating and comparing TB parametrizations.
  • Assess TB model performance for semiconductor-relevant materials.
  • Provide standardized benchmarks for TB methods.

Main Methods:

  • Developed the CHIPS-TB computational framework.
  • Assessed multiple TB parametrizations (DFTB-based MatSci, PBC, PTBP, SlaKoNet, TB3PY).
  • Compared TB results against DFT (OptB88vdW, TBmBJ-DFT) and experimental data from JARVIS-DFT database for over 50 materials.

Main Results:

  • Evaluated the performance of various TB parametrizations.
  • Identified strengths and weaknesses of different models for predicting electronic bandgaps, band structures, and bulk modulus.
  • Established a benchmark dataset for TB model comparison.

Conclusions:

  • CHIPS-TB provides a standardized approach for TB model evaluation in semiconductor design.
  • The framework facilitates the selection of appropriate TB parametrizations for specific material properties.
  • Publicly available code and benchmarks will advance TB method development and application.