PREDICTION MODEL OF ETCHING BIAS BASED ON ARTIFICIAL NEURAL NETWORK

被引：0
作者：
Hu, Haoru [1 ,2 ]
Dong, Lisong [1 ]
Wei, Yayi [1 ]
Zhang, Yonghua [1 ,2 ]
机构：
[1] Chinese Acad Sci Beijing, Inst Microelect, Integrated Circuit Adv Proc Ctr, Beijing 100029, Peoples R China
[2] Guizhou Univ, Coll Data & Informat Engn, Guiyang 550025, Guizhou, Peoples R China
来源：
2019 CHINA SEMICONDUCTOR TECHNOLOGY INTERNATIONAL CONFERENCE (CSTIC) | 2019年
基金：
中国国家自然科学基金;
关键词：
etch bias; layout features; ANN; aperture effect; micro-loading effect;
D O I：
10.1109/cstic.2019.8755792
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Etching is an important step in the transfer of design layout to wafer results. Its quality directly impacts the performance of chips or integrated circuit. The etch aperture effect and micro-loading effect that affect the etch quality (etch bias) are considerably dependent on layout features such as pattern density, spacing between patterns, etc. Under fixed etching process parameters, a predicative model of etch bias is proposed in this paper. The experimental results show that the absolute error of the predicative value of the model can reach within +/- 2nm, and the relative error can reach less than 10%, respectively, relative to the true etch bias.
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页数：3
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