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

Authors:
Issam Gereige;StéPhane Robert;Jessica Eid
Affiliations:
Solar and Alternative Energy Engineering Center, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;Université de Lyon, F-42023 Saint-Etienne DIOM EA 3523, Télécom Saint-Etienne, Université de Saint-Etienne Jean Monnet, F-42023, France;Solar and Alternative Energy Engineering Center, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Venue:
Microelectronic Engineering
Year:
2012

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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 400nm period grating manufactured with nanoimprint lithography is analyzed with our method.