Characterization of a multicrystalline silicon ingot using a data science approach

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  • データ科学を用いた結晶シリコンの評価
  • データ カガク オ モチイタ ケッショウ シリコン ノ ヒョウカ

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Abstract

<p>We review our recent attempt to integrate data science with experimental science to establish universal guidelines to improve multicrystalline materials with complicated microstructures. Based on data collection from a large quantity of practical multicrystalline silicon wafers for solar cells and image processing, we succeeded in realizing a three-dimensional (3D) visualization of the microstructures and dislocation clusters in a multicrystalline silicon ingot. This manifested generation, propagation, and annihilation of dislocation clusters in the multicrystalline silicon ingot. The combination of data science and experimental approaches showed that small-angle boundaries are likely to be the source of dislocation clusters in multicrystalline silicon.</p>

Journal

  • Oyo Buturi

    Oyo Buturi 87 (12), 912-916, 2018-12-10

    The Japan Society of Applied Physics

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