Defect detection algorithm of MEMS acoustic film based on frequency domain transformation

WEI Dong; SANG Mei; YU Minhui; HUANG Yaokang; LIU Hongguang; LIU Tiegen

doi:10.5768/JAO202142.0603005

Journal of Applied Optics > 2021 > 42(6): 1086-1091. > DOI: 10.5768/JAO202142.0603005

WEI Dong, SANG Mei, YU Minhui, HUANG Yaokang, LIU Hongguang, LIU Tiegen. Defect detection algorithm of MEMS acoustic film based on frequency domain transformation[J]. Journal of Applied Optics, 2021, 42(6): 1086-1091. DOI: 10.5768/JAO202142.0603005

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Defect detection algorithm of MEMS acoustic film based on frequency domain transformation

WEI Dong^{1, 2,},
SANG Mei^{1, 2, ,},
YU Minhui^{1, 2},
HUANG Yaokang^{1, 2},
LIU Hongguang³,
LIU Tiegen^{1, 2}

1.
School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China
2.
Key Laboratory of Opto-electronic Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
3.
Tianjin Institute of Metrological Supervision and Testing, Tianjin 300192, China

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Received Date: April 24, 2021
Revised Date: May 08, 2021
Available Online: August 29, 2021

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Abstract

Abstract

The micro-electro-mechanical system (MEMS) acoustic film has extremely high requirements for tape-out, storage and packaging environments, and its surface defects will affect the quality and performance of MEMS devices. The image defects detection is an effective non-contact optical detection means that can effectively improve the yield rate of MEMS production. However, the periodic structure texture of the MEMS devices surface will interfere with defect detection. A acoustic film defect detection algorithm based on frequency domain transformation was proposed. By calculating the gradient distribution of spectrogram and establishing the Boolean mask, the dominant frequency components corresponding to the periodic structure texture were eliminated. The residual spectrograms were subjected to a Fourier inversion to reconstruct the defect images. The reconstructed images were decomposed by single-layer Haar wavelet to obtain the low-frequency sub-band image and the defect information was extracted by simple threshold segmentation. The defect detection effects of different types of MEMS acoustic film were showed. The experimental results show that it is reasonable to set the zoom constant in the range of 0.7~1.0.

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Defect detection algorithm of MEMS acoustic film based on frequency domain transformation