概括
这项研究引入了一种新型的卷积回归网络,用于 lithography 的错位感应. 这种新方法通过分析moiré边缘的空间和频率域来实现0.12nm的精度,克服了传统方法的局限性.
科学领域:
- 半导体制造业 半导体制造业
- 计量学 计量学是一门学科.
- 图像处理 图像处理
背景情况:
- 在光刻画中,达到0.2nm以下的精度,对半导体制造至关重要.
- 现有的回归算法由于卷积网络对单域信息处理的局限性而与莫雷边缘分析作斗争.
- 这种特征数据的缺陷阻碍了高精度回归以准确测量错位.
研究的目的:
- 开发一个先进的卷积回归网络,用于精确的光刻错位感应.
- 克服当前回归算法中单域分析的局限性.
- 综合空间和频率域信息,以进行增强的边缘模式分析.
主要方法:
- 设计了一个新的卷积回归网络.
- 来自边缘模式的综合空间和频率域信息.
- 应用网络来分析moiré边缘用于错位检测.
主要成果:
- 在3σ的置信级别下,实现了0.12nm的误调测量精度.
- 证明了与需要0.2nm以下精度的现有方法相比的显著改进.
- 该方法证明了对系统错误和环境噪声的稳定性.
结论:
- 开发的卷积回归网络有效地合成了多域边缘信息,用于高精度 lithography 错位感应.
- 这种方法成功地解决了单域分析的局限性,使得准确度低于0.2nm.
- 该技术的稳定性使其适用于半导体制造中的关键应用.
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