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A neural network approach based on interference pattern analysis: Application to an autoalignment method for the focusing unit of NFR system

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Názov: A neural network approach based on interference pattern analysis: Application to an autoalignment method for the focusing unit of NFR system
Autori: Yoon, HK, Gweon, Dae-Gab, Lee, JH, Jeong, J, Oh, HR
Prispievatelia: Gweon, Dae-Gab, Yoon, HK, Lee, JH, Jeong, J, Oh, HR
Informácie o vydavateľovi: Japan Soc Applied Physics
Rok vydania: 2004
Zbierka: Korea Advanced Institute of Science and Technology: KOASAS - KAIST Open Access Self-Archiving System
Predmety: DATA-STORAGE, LENS, solid immersion lens, near-field recording, interference pattern analysis, neural network, pattern recognition, autoalignment method
Popis: From the viewpoint of assembly, evaluation results and an autoalignment method for the focusing unit (FU) of a near-field recording (NFR) system are proposed. Generally, the size of the focusing unit composed of the objective lens and the solid immersion lens is smaller than that of the conventional focusing unit. Hence there are difficulties in the precise assembly of the small focusing unit. We developed an evaluation system with an interferometer and evaluated some focusing unit samples, then a tolerance analysis of the assembly error between the SIL and the objective lens and an interference pattern analysis of the assembly error were carried out. A pattern recognition method using a neural network is presented with features, which were extracted from interference patterns due to errors in the FU.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: http://hdl.handle.net/10203/84895; 596; 67073; 000223477600075
Dostupnosť: http://hdl.handle.net/10203/84895
Prístupové číslo: edsbas.56FF23BF
Databáza: BASE
Popis
Abstrakt:From the viewpoint of assembly, evaluation results and an autoalignment method for the focusing unit (FU) of a near-field recording (NFR) system are proposed. Generally, the size of the focusing unit composed of the objective lens and the solid immersion lens is smaller than that of the conventional focusing unit. Hence there are difficulties in the precise assembly of the small focusing unit. We developed an evaluation system with an interferometer and evaluated some focusing unit samples, then a tolerance analysis of the assembly error between the SIL and the objective lens and an interference pattern analysis of the assembly error were carried out. A pattern recognition method using a neural network is presented with features, which were extracted from interference patterns due to errors in the FU.