Application of fuzzy neural network to optimizing UV-LIGA process

ZHENG Xiao－hu; ZHU Di

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Application of fuzzy neural network to optimizing UV-LIGA process

更新时间：2020-08-12

- Application of fuzzy neural network to optimizing UV-LIGA process
- Optics and Precision Engineering Vol. 14, Issue 1, Pages: 139-144(2006)
- 作者机构：
  
  1. 南京航空航天大学机电工程学院,江苏南京,210016
  2. 淮阴工学院机械系,江苏淮安,223001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN305.7
- Received：10 August 2005，
  
  Revised：14 October 2005，
  
  Published Online：20 February 2006，
  
  Published：20 February 2006
- 稿件说明：
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ZHENG Xiao－hu, ZHU Di. Application of fuzzy neural network to optimizing UV-LIGA process[J]. Optics and precision engineering, 2006, 14(1): 139-144.
DOI：

ZHENG Xiao－hu, ZHU Di. Application of fuzzy neural network to optimizing UV-LIGA process[J]. Optics and precision engineering, 2006, 14(1): 139-144. DOI：

摘要

将模糊神经网络理论和算法应用于负性光刻胶(SU-8)加工高分辨率和高深宽比微结构的工艺研究

在正交试验的基础上对网络进行训练

建立了光刻图形质量与前烘时间、前烘温度、曝光量、后烘时间之间的预测模型。该模型采用五层前向模糊神经网络

学习算法为梯度下降法。进行了实验

实验结果表明

前烘温度与前烘时间对光刻质量影响最大。对120~340 μm厚的光刻胶

前烘温度取95℃

前烘时间100 min时

图形的相对线宽差最小;超声搅拌能缩短显影时间

显著改善图形质量

试验结果与计算结果十分吻合。将模糊神经网络应用于UV－LIGA工艺中

能实现光刻加工微结构的工艺参数优化。

Abstract

The theory and the algorithm of the fuzzy neural network were applied in the research of the fabrication of high resolute microstructure using negative (SU-8)

the artificial neural network were trained based on orthogonality experiment

and the prediction model was built between the fabrication structure quality and the main technology parameters. The model was a Feedforward Fuzzy neural network possessing five layers

and Gradient Descent was adopted as learning algorithm. The experiment results show that the temperature and the soft bake time is the most important factor of the structure quality. when thickness of the photoresist is ranged from 120~340 μm

the temperature and time of the soft bake is 95℃ and 100 min

respectivly

then the optimal microstructure with the least relative line width discrepancy can be obtained. In addition

the developing time is reduced and the image quality is greatly improved through ultrasonic agitation. The predict results are in good agreement with the experimental results

and the lithographic process could be optimized with fuzzy neural network.

关键词

Keywords

references

. KOO H,LIU K,TSENG F.Mechanical strength and interfacial failure analysis of cantilevered SU-8 microposts [J]. Micromech. Microeng.,2003,13:822-831.

. ZHANG J,TAN L K,GONG Q H. Characterization of the polymerization of SU-8 photoresist and its application in micro-electro-mechanical systems [J].Polymer Testing,2001,20:693-701.

. HONG S J,CHOI S K,CHOL Y S, et al. Characterization of low-temperature SU-8 photoresist processing for MEMS applications .2004 IEEE/SEMI Advanced Semiconductor Manufacturing Conference. Hawaii.,2004:404-408.

. 杨承,黄成军,刘峰,等. 毛细管电泳芯片制备工艺研究[J]. 华中科技大学学报 ,2004,32(7):92-94. YANG CH,HUANG CH J,LIU F, et al. Study of microfabrication of capillary electrophoresis chip[J].J.Huazhong Univ. of Sci.& Tech.(Nature Science Edition) ,2004,32(7):92-94.(in Chinese)

. 王士同.神经模糊系统及其应用 [M]. 北京;北京航空航天大学出版社,1998. WANG SH T. Fuzzy neural system and its application [M].Beijing:Beijing University of Aeronautics and Astronautics Press.1998.

. 张立国,陈迪,杨帆,等. SU-8胶光刻工艺研究[J]. 光学精密工程 ,2002,10(3):266-269. ZHANG L G,CHEN D,YANG F,et al.Research on SU-8 resist photolithography process[J]. Optics and Precision Engineering,2002,10(3):266-269.(in Chinese)

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