Process Proximity Correction by Neural Networks

Autor:	Ilsin An, Kyoung-Ah Jeon, Ji-Yong Yoo, Hye-Keun Oh, Jun-Taek Park, Hyeong-Soo Kim
Rok vydání:	2003
Předmět:	Physics and Astronomy (miscellaneous) Artificial neural network Computer science Proximity effect (audio) General Engineering Process (computing) General Physics and Astronomy Experimental data Value (computer science) Algorithm Critical dimension
Zdroj:	Japanese Journal of Applied Physics. 42:3908-3912
ISSN:	1347-4065 0021-4922
Popis:	Making an accurate and quick critical dimension (CD) prediction is required for higher integrated device. Because simulation tools are consisted of many process parameters and models, it is hard that process parameters are optimized to match with the CD results for various patterns. This paper presents a method of improving accuracy of predicting CD results by applying the CD difference between simulation and experimental data value to neural network algorithm to reduce the CD difference caused by optical proximity effect.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::504bc420f579fe8cdee8d93b122b6456 https://doi.org/10.1143/jjap.42.3908 Zobrazit plný text záznamu