Using the RGB data of bright-field optical microscopy to efficiently monitor the surface cleanliness of the active elements of semiconductor wafers in the planar production cycle

Kozyrev, А.А., Gordeeva M.V., Burtsev D.N., Eliseeva Y.A.

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For Russian citation (Opticheskii Zhurnal):

Козырев А.А., Гордеева М.В., Бурцев Д.Н., Елисеева Ю.А. Использование RGB данных оптической светлопольной микроскопии для оперативного контроля чистоты поверхности активных элементов полупроводниковых пластин в производственном планарном цикле // Оптический журнал. 2020. Т. 87. № 6. С. 76–88. http://doi.org/10.17586/1023-5086-2020-87-06-76-88 Kozyrev A.A., Goreeva M.V., Burtsev D.N., Eliseeva Yu.A. Using the RGB data of bright-field optical microscopy to efficiently monitor the surface cleanliness of the active elements of semiconductor wafers in the planar production cycle [in Russian] // Opticheskii Zhurnal. 2020. Т. 87. № 6. С. 76–88. http://doi.org/10.17586/1023-5086-2020-87-06-76-88

For citation (Journal of Optical Technology):

A. A. Kozyrev, M. V. Goreeva, D. N. Burtsev, and Yu. A. Eliseeva, "Using the RGB data of bright-field optical microscopy to efficiently monitor the surface cleanliness of the active elements of semiconductor wafers in the planar production cycle," Journal of Optical Technology. 87(6), 379-388 (2020). https://doi.org/10.1364/JOT.87.000379

Abstract:

It is shown that it is promising to use the RGB data of bright-field optical microscopy to efficiently monitor the surface cleanliness of the active elements of semiconductor wafers in the planar production cycle. It is demonstrated that the film thickness on a substrate surface can be determined from RGB data obtained by means of a photosensor based on a Bayer array with known spectral response. An example shows how to use it on a GaAs semiconductor wafer with metallized active gold elements. Software has been developed for channel-by-channel comparison of a discriminated region of two pictures. An algorithm is presented for obtaining micrographs that can subsequently be analyzed and compared.

Keywords:

RGB, optical microscopy, digital image processing, thin films, thickness measurement

OCIS codes: 100.2000

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