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関連する概念動画

Semiconductors01:22

Semiconductors

1.3K
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...
1.3K
Types of Semiconductors01:20

Types of Semiconductors

1.3K
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...
1.3K
Network Covalent Solids02:18

Network Covalent Solids

15.9K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
15.9K
Energy Bands in Solids01:01

Energy Bands in Solids

1.7K
Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
1.7K
Valence Bond Theory02:42

Valence Bond Theory

10.9K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
10.9K
Lewis Structures of Molecular Compounds and Polyatomic Ions02:54

Lewis Structures of Molecular Compounds and Polyatomic Ions

44.0K
To draw Lewis structures for complicated molecules and molecular ions, it is helpful to follow a step-by-step procedure as outlined:
44.0K

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Updated: Dec 30, 2025

Monolayer Contact Doping of Silicon Surfaces and Nanowires Using Organophosphorus Compounds
09:45

Monolayer Contact Doping of Silicon Surfaces and Nanowires Using Organophosphorus Compounds

Published on: December 2, 2013

7.9K

シリコンにおける無秩序なドーパント原子ネットワークによる分類

Tao Chen1, Jeroen van Gelder1, Bram van de Ven1

  • 1NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain-Inspired Nano Systems, University of Twente, Enschede, The Netherlands.

Nature
|January 17, 2020
PubMed
まとめ
この要約は機械生成です。

研究者らは効率的で並行的な非線形分類のための 新しいシリコンベースのナノマテリアルシステムを開発しました このアプローチはニューラルネットワークに 触発され ナノスケールで複雑な計算を行い エネルギー効率の良い 計算の道を開きます

さらに関連する動画

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

10.1K
Fabrication and Optimization of Type II Silicon Clathrate Films
06:53

Fabrication and Optimization of Type II Silicon Clathrate Films

Published on: October 14, 2025

889

関連する実験動画

Last Updated: Dec 30, 2025

Monolayer Contact Doping of Silicon Surfaces and Nanowires Using Organophosphorus Compounds
09:45

Monolayer Contact Doping of Silicon Surfaces and Nanowires Using Organophosphorus Compounds

Published on: December 2, 2013

7.9K
All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

10.1K
Fabrication and Optimization of Type II Silicon Clathrate Films
06:53

Fabrication and Optimization of Type II Silicon Clathrate Films

Published on: October 14, 2025

889

科学分野:

  • 材料科学
  • 計算神経科学
  • 機械学習

背景:

  • 生物学的および人工的なニューラルネットワークは 分類作業に優れています
  • 機械学習における非線形プロジェクションは分類を改善しますが,計算的には高価です.
  • 物理的な材料は高計算密度,並列性,非線形投影のためのエネルギー効率を提供します.

研究 の 目的:

  • 非線形分類と特徴抽出のためのナノスケール並行アプローチを開発する.
  • 調節可能なシリコンドーパントのネットワークにおけるジャンプ伝導の非線形性を利用する.
  • 小規模でエネルギー効率のよい コンピューティングの新たなパラダイムを 示すためです

主な方法:

  • シリコンのボロンドーパント原子の 電気的に調整可能なネットワークを利用した.
  • 特定の計算機能のためにドーパントネットワークを再構成するために人工進化を使用しました.
  • ブール論理ゲートと手書きの数字分類 (改訂された国家標準技術研究所データベース) でシステムをテストしました.

主要な成果:

  • 室温までのすべてのブール論理ゲートを成功裏に実現し,非線形分類を実証した.
  • 進化したドーパントネットワークは,線形分類器よりも精度が向上した手書きの数字で4入力バイナリ分類を行った.
  • 素材ベースのフィルターは,分類精度の大幅な改善を達成しました.

結論:

  • シリコンベースの電子機器のパラダイムを確立し,小さな足跡とエネルギー効率の良い計算を可能にしました.
  • 複雑な計算作業のためのナノスケール材料システムの可能性を実証した.
  • このアプローチは,非線形分類のための従来の,計算的に高価な方法の有望な代替案を提供します.