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Semiconductors01:22

Semiconductors

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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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Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System
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人与机器的合作,以改善半导体工艺的发展

Keren J Kanarik1, Wojciech T Osowiecki1, Yu Joe Lu1

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人工智能和人类工程师可以共同开发更实惠的半导体芯片工艺. 结合人类的专业知识和人工智能算法,

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科学领域:

  • 材料科学
  • 计算机科学
  • 工程

背景情况:

  • 半导体芯片的开发依赖于昂贵的化学等离子体工艺.
  • 工程师手工开发需要大量的时间和成本.
  • 有限的实验数据阻碍了精确的原子尺度预测建模.

研究的目的:

  • 研究人工智能 (AI) 降低半导体工艺开发成本.
  • 在虚拟过程设计游戏中对比人工智能算法.
  • 探索混合的人工智能策略,以节省成本.

主要方法:

  • 使用贝叶斯优化算法.
  • 创建了一个可控的虚拟过程游戏,用于系统的基准测试.
  • 人类设计师和人工智能算法的性能指标比较.

主要成果:

  • 人类工程师在最初的发展阶段是有效的.
  • 人工智能算法在目标宽容度附近显示出更高的成本效益.
  • 混合人类-人工智能策略将目标成本降低了50%,

结论:

  • 人工智能可在半导体工艺开发中显著降低成本.
  • 人与人工智能的混合协作是高效芯片制造的一个有前途的策略.
  • 为了在工业中成功实施人工智能,必须应对文化融合的挑战.