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Titolo:
Process control to improve yield in the plasma etching process using an adaptively trained neural network
Autore:
Choi, MK; Kim, HM;
Indirizzi:
Sung Kyun Kwan Univ, Grad Sch Mech Engn, Jangan Ku, Suwon 440746, Kyunggido, South Korea Sung Kyun Kwan Univ Suwon Kyunggido South Korea 440746 ggido, South Korea Sung Kyun Kwan Univ, Sch Mech Engn, Jangan Ku, Suwon 440746, Kyunggido, South Korea Sung Kyun Kwan Univ Suwon Kyunggido South Korea 440746 ggido, South Korea
Titolo Testata:
JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING
fascicolo: 3, volume: 43, anno: 2000,
pagine: 594 - 602
SICI:
1344-7653(200009)43:3<594:PCTIYI>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Keywords:
plasma etching process; adaptively trained neural network; process analysis system; process prediction; process control;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
22
Recensione:
Indirizzi per estratti:
Indirizzo: Choi, MK Sung Kyun Kwan Univ, Grad Sch Mech Engn, Jangan Ku, 300 Chunchun Dong, Suwon 440746, Kyunggido, South Korea Sung Kyun Kwan Univ 300 ChunchunDong Suwon Kyunggido South Korea 440746
Citazione:
M.K. Choi e H.M. Kim, "Process control to improve yield in the plasma etching process using an adaptively trained neural network", JSME C, 43(3), 2000, pp. 594-602

Abstract

In this paper, we present a process analysis system that can analyze causes with expert proficiency for a given result after undergoing various processes. Also, the plasma etching process that affects yield is controlled, using an adaptively trained neural network, to predict an output before the real process. In modeling, a method that utilizes the trend history of inputdata shows considerable advantage in both learning and prediction. The research regards CD (Critical Dimension), which is crucial in high integrated circuits, as the output variable of the model. Based on the model using this method, we propose an algorithm to analyze and control the effect of input variables for predicted defects. Both the weight of input variables and their trend history are considered in for this algorithm.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 06/04/20 alle ore 23:53:17