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Development of large array auto write-scan photoresist fabrication and inspection system.

机译:大型阵列自动写扫描光刻胶制作及检测系统的开发。

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摘要

Current metrology methods involve technicians viewing through a microscope, increasing the time, cost, and error rate in inspection. Developing an automated inspection system eliminates these difficulties. Shown in this work is a laser scanning microscope (LSM) design for an opto-electronic detection system (OEDS), based upon the concept that intensity differences related to pattern defects can be obtained from reflections off fused silica samples coated with photoresist (PR) or Aluminum. Development of this system for data collection and processing is discussed. Results show that 2.1 &mgr;m resolution of these defects is obtainable. Preliminary results for larger-array patterns through stitching processes are also shown. The second part of this work uses the concept of phase contrast edge detection. Looking at non-metallized patterns, one can use the property that phase changes induced by a refractive-index sensitive material can be seen with a multi-cell array, rendering the image visible by comparing the respective phases. A variety of defects and samples are shown. Extrapolating results to larger arrays is also discussed. Latent imaging, or imaging without development, is also evaluated. Future work in the areas of system commercialization, sample storage, and other mass-printing techniques are discussed.
机译:当前的计量方法涉及技术人员通过显微镜观察,从而增加了检查的时间,成本和错误率。开发自动检查系统可以消除这些困难。这项工作中展示的是一种用于光电检测系统(OEDS)的激光扫描显微镜(LSM)设计,其原理是可以从涂覆有光阻剂(PR)的熔融二氧化硅样品的反射中获得与图案缺陷相关的强度差异。或铝。讨论了用于数据收集和处理的系统的开发。结果表明,这些缺陷的分辨率为2.1μm。还显示了通过缝合工艺获得的更大阵列图案的初步结果。这项工作的第二部分使用相位对比边缘检测的概念。观察非金属化图案,可以利用一种特性,即可以通过多单元阵列看到由折射率敏感材料引起的相变,从而通过比较各个相使图像可见。显示了各种缺陷和样品。还讨论了将结果外推到更大的数组。还评估了潜像成像或没有显影的成像。讨论了系统商业化,样品存储和其他批量打印技术领域的未来工作。

著录项

  • 作者

    Sierchio, Justin M.;

  • 作者单位

    The University of Arizona.;

  • 授予单位 The University of Arizona.;
  • 学科 Physics Optics.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 162 p.
  • 总页数 162
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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