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Application of artificial neural networks (ANN) and response surface model (RSM) in optical microlithographic process modeling

机译：人工神经网络（ANN）和响应面模型（RSM）在光学微光刻工艺建模中的应用

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Abstract: Optical microlithography represents one of the mostsophisticated processes in the manufacturing ofmicroelectronics devices. Accurate process models arehighly desirable for process control, processoptimization, yield improvement, and cost reduction.Design of experiments (DOE) and response surface model(RSM) are traditional tools for empirical modeling.This paper presents an alternative by using artificialneural networks (ANNs) to model the intricaterelationship between the critical dimension (CD) andthree key lithographic process variables, soft baketime, exposure stage speed, and develop time. A set ofdata obtained from a designed experiment is used totrain a three-layer neural network. A comparison of theANN model with the RSM model shows that ANN modelprovides higher accuracy and greater capability ofgeneralization. !21

机译：摘要：光学微光刻技术是制造微电子设备中最复杂的工艺之一。精确的过程模型对于过程控制，过程优化，产量提高和成本降低是非常需要的。实验设计（DOE）和响应面模型（RSM）是用于经验建模的传统工具。本文提出了使用人工神经网络（ANN）的替代方法以模拟关键尺寸（CD）与三个关键光刻工艺变量，软烘烤时间，曝光阶段速度和显影时间之间的复杂关系。从设计的实验中获得的一组数据用于训练三层神经网络。 ANN模型与RSM模型的比较表明，ANN模型提供了更高的准确性和更强的泛化能力。！21

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《Optical Microlithography XI》|1998年|p.820-830|共11页
会议地点 Santa Clara CA(US)
作者
Author(s): Bo Zhou Storage Technology Corp. Louisville CO USA; Barry A. McPherron Motorola Louisville CO USA.;
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正文语种 eng
中图分类轻工业、手工业;
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Application of artificial neural networks (ANN) and response surface model (RSM) in optical microlithographic process modeling

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