Automatic detection of photoresist residual layer in lithography using a neural classification approach

Abstract : Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method.
Type de document :
Article dans une revue
Microelectronic Engineering, Elsevier, 2012, 97, pp.29-32. 〈10.1016/j.mee.2012.02.032〉
Liste complète des métadonnées

https://hal-ujm.archives-ouvertes.fr/ujm-01390616
Contributeur : Stéphane Robert <>
Soumis le : mercredi 2 novembre 2016 - 11:26:59
Dernière modification le : samedi 18 novembre 2017 - 18:16:02

Identifiants

Collections

Citation

Issam Gereige, Stéphane Robert. Automatic detection of photoresist residual layer in lithography using a neural classification approach. Microelectronic Engineering, Elsevier, 2012, 97, pp.29-32. 〈10.1016/j.mee.2012.02.032〉. 〈ujm-01390616〉

Partager

Métriques

Consultations de la notice

26