Automated measurement method based on deep learning for cross-sectional SEM images of semiconductor devices

Autor:	Yutaka Okuyama, Takeshi Ohmori
Rok vydání:	2022
Předmět:	Physics and Astronomy (miscellaneous) General Engineering General Physics and Astronomy
Zdroj:	Japanese Journal of Applied Physics. 62:SA1016
ISSN:	1347-4065 0021-4922
DOI:	10.35848/1347-4065/ac923d
Popis:	Feature length measurement in cross-sectional scanning electron microscopy images of modern semiconductor devices is time-consuming and laborious. We propose an automated measurement method based on deep learning technology and applied it to trench pattern images. The method combines two image-recognition tasks: (1) object detection for determining the coordinates of each unit of a pattern and (2) semantic segmentation for obtaining the boundaries of each area (mask, substrate, and background). By combining the results of these two tasks, typical feature lengths, such as width and depth, are precisely and immediately measured. The extraction speed of the proposed method is 240 times faster than manual measurement and provides measurement results independent of the engineer’s skills.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1f901c5acc8663ce07e2641d4c31ba99 https://doi.org/10.35848/1347-4065/ac923d Zobrazit plný text záznamu