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Updated: Jul 27, 2026

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一个光学线性分辨器过器的设计:优化以提高过器传导率和分辨准确度

Jun-Ichiro Sugisaka, Shingo Shimada, Koichi Hirayama

Journal of the Optical Society of America. A, Optics, image science, and vision

|January 4, 2024

概括

一个光学机器学习系统使用光学线性歧视波器 (OLDF) 来检测缺陷. 一个新的算法提高了过器的性能,以改善缺陷歧视和传导率.

科学领域:

光学是什么?光学是什么?光学是什么?
机器学习机器学习
材料科学材料科学材料科学

背景情况:

检测介电基板上的细表面缺陷对于质量控制至关重要.
现有的光学方法在弱信号检测和低传输率方面扎.

研究的目的:

提出一种光学机器学习系统,用于区分形和凸形缺陷.
开发一种算法来提高光学线性差异波器 (OLDF) 的性能.

主要方法:

使用光学线性分辨器过器 (OLDF) 进行散射波的线性分辨分析 (LDA).
开发和实施一种算法,以提高区分精度和过器传输率.
使用基于矢量衍射理论的严格光学模拟器验证设计过器.

主要成果:

拟议的系统表明存在缺陷歧视的可能性.
该算法显著提高了分辨准确性和波器传导率.
分析证实了实现高传输率和高分辨准确性的机制.

结论:

开发的光学机器学习系统有效地区分了介电基板上的微小缺陷.
拟议的算法提供了一个可行的解决方案,以克服弱波器输出的局限性.
这种方法为先进的光学检查技术铺平了道路.

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