Micron-level processing technology of microlens array （MLA） photolithography based on convolutional neural network

YAO Yuchao; ZHOU Rui; YAN Xing; WANG Zhenzhong; GAO Na

doi:10.37188/OPE.20243201.0043

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Micron-level processing technology of microlens array （MLA） photolithography based on convolutional neural network

Micro/Nano Technology and Fine Mechanics | 更新时间：2024-01-13

- Micron-level processing technology of microlens array （MLA） photolithography based on convolutional neural network
- Optics and Precision Engineering Vol. 32, Issue 1, Pages: 43-52(2024)
- 作者机构：
  
  1.厦门大学萨本栋微米纳米科学技术研究院，福建厦门 361005
  2.福建省能源材料科学与技术创新实验室（IKKEM），福建厦门 361005
  3.厦门大学航空航天学院，福建厦门 361102
  4.厦门大学物理科学与技术学院，福建厦门 361005
  5.厦门大学九江研究院，江西九江 332000
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20243201.0043
  CLC：
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姚宇超,周锐,严星等.基于卷积神经网络判定方法的激光微透镜阵列微米级加工工艺[J].光学精密工程,2024,32(01):43-52.

YAO Yuchao,ZHOU Rui,YAN Xing,et al.Micron-level processing technology of microlens array （MLA） photolithography based on convolutional neural network[J].Optics and Precision Engineering,2024,32(01):43-52.
姚宇超,周锐,严星等.基于卷积神经网络判定方法的激光微透镜阵列微米级加工工艺[J].光学精密工程,2024,32(01):43-52. DOI： 10.37188/OPE.20243201.0043.

YAO Yuchao,ZHOU Rui,YAN Xing,et al.Micron-level processing technology of microlens array （MLA） photolithography based on convolutional neural network[J].Optics and Precision Engineering,2024,32(01):43-52. DOI： 10.37188/OPE.20243201.0043.

摘要

在MLA曝光工艺中，曝光点的数量庞大，通过高倍率显微镜配合人工目检来判定曝光质量耗时耗力，造成工艺成本偏高。为了解决这个问题，设计了一种便于检测的圆环形图案并引入深度学习中的目标检测Yolov5模型，一定程度上能够取代人工目检，完成对曝光质量的快速判定。基于上述方法，分析了不同光刻胶厚度之下，线能量密度的最优区间与光刻胶的剖面倾角。并在同等线能量密度下通过圆度判定曝光图案失真情况。在本研究的MLA曝光工艺中，选取光刻胶厚度、激光曝光功率以及加工平台移动速度作为自变量，评价曝光合格率、光刻胶剖面倾角以及曝光圆度等加工质量参数具有重要的工程意义。

Abstract

During microlens array（MLA） photolithography exposure process， the number of photolithography points is considerably large， thus， judgement of the photolithography quality by human eyes with a high-magnification microscope is time-consuming and labor-intensive， resulting in high process cost. To solve this problem， an easily detected circular pattern was designed and a Yolov5 model for target detection in deep learning was introduced， which can replace manual eye inspection to a certain extent and complete the rapid judgement of photolithography quality. Based on the proposed method， the optimal interval of the level of energy density during laser scanning and the profile dip angle of the photoresist were analyzed under different photoresist thicknesses. At the same level of energy density during laser scanning， the distortion of photolithography pattern was judged considering circularity. Further， the photoresist thickness， laser power， and processing platform moving speed were selected as independent variables in the MLA photolithography process to evaluate processing quality parameters processing quality parameters， such as photolithography qualification rate， photoresist profile inclination angle， and photolithography circularity， is of great significance for engineering.

关键词

无掩膜光刻微透镜阵列曝光合格率目标检测

Keywords

maskless lithogrophymicrolens arrayqualification rateobject detection

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