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Epitaxial ferroelectric YMnO3 (YMO) thin films were fabricated on (0001) GaN substrates by pulsed laser deposition followed by rapid thermal annealing. The temperature and field dependence of the leakage current of YMO/GaN interface was studied in a temperature range from 150 to 300 K and for an applied voltage up to 10 V. In a low temperature region from 180 to 220 K, the YMO/GaN interface acted as a Schottky barrier with a height of 0.27 eV for a field below 1.4 MV/cm, while the leakage mechanism was governed by the Fowler–Nordheim tunneling for a field above 1.4 MV/cm. Moreover, a space-charge-limited-current behavior was observed in a high field for a temperature above 270 K, while an Ohmic behavior was observed in a low field. In comparison, the dominant leakage mechanism of In/YMO interface was an Ohmic behavior in the whole measured voltage and temperature ranges.

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There are kinds of defects that may appear in the process of Liquid Crystal Display (LCD) manufacturing, which cannot be effectively detected, owing to the uneven illumination, low contrast, and miscellaneous patterns of defects. To improve the efficiency of defect detection and ensure the quality of LCD, three visual real‐time detection methods are adopted for detecting six different defects in multiple backgrounds, where image preprocessing methods are used to highlight the defects and facilitate the segmentation and detection. Specifically, the interclass variance (OTSU) method is used to segment and mark Liquid Crystal Display (LCD) Mura and scratch defects in six kinds of solid color backgrounds; the method and the connectivity‐4 judgment criteria are adopted to label edge defects in grid display background; the gray mean and standard deviation of the segmented subregions are calculated to recognize the color gradation defect in the 32‐level gradation display background. Experimental results show that LCD Mura defects and scratches can be segmented more completely by the proposed method compared with the benchmark methods, and the edge defects can be identified accurately by the OTSU‐based method and particle‐based morphological processing with grids as the detection background, and the color gradation can also be recognized with the 32‐level gray gradation as the background.