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Updated: Jul 10, 2026

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
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在室温半导体探测器中,在没有先验知识的情况下识别缺陷,使用物理启发的机器学习模型.

Srutarshi Banerjee1, Miesher Rodrigues2, Manuel Ballester1

  • 1Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

灵感来自物理学的机器学习模型现在可以识别室温半导体辐射探测器 (RTSD) 中未知的缺陷. 这些模型在体积上对缺陷进行了表征,改善了计算机断层扫描 (CT) 等应用的材料表征.

关键词:
运输费 运输费 运输费 运输费缺陷 缺陷 缺陷 缺陷 缺陷脱落的 脱落的 脱落机器学习是机器学习.材料表征材料的表征.启发物理的机器学习模型 (PI-ML)室温半导体检测器检测器半导体检测器捕捉陷 捕捉陷是一种捕捉.捕捉中心,捕捉中心.

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