With the development of the liquid crystal display (LCD) module industry, LCD modules become more and more precise with larger sizes, which demands harsh imaging requirements for automated optical inspection (AOI). Here, we report a high-resolution and clearly focused imaging optomechatronics for precise LCD module bonding AOI inspection. It first presents and achieves high-resolution imaging for LCD module bonding AOI inspection using a line scan camera (LSC) triggered by a linear optical encoder, self-adaptive focusing for the whole large imaging region using LSC, and a laser displacement sensor, which reduces the requirements of machining, assembly, and motion control of AOI devices. Results show that this system can directly achieve clearly focused imaging for AOI inspection of large LCD module bonding with 0.8 μm image resolution, 2.65-mm scan imaging width, and no limited imaging width theoretically. All of these are significant for AOI inspection in the LCD module industry and other fields that require imaging large regions with high resolution.
Anisotropic conductive film (ACF) bonding is widely used in the liquid crystal display (LCD) industry. It implements circuit connection between screens and flexible printed circuits or integrated circuits. Conductive microspheres in ACF are key factors that influence LCD quality, because the conductive microspheres’ quantity and shape deformation rate affect the interconnection resistance. Although this issue has been studied extensively by prior work, quick and accurate methods to inspect the quality of ACF bonding are still missing in the actual production process. We propose a method to inspect ACF bonding effectively by using automated optical inspection. The method has three steps. The first step is that it acquires images of the detection zones using a differential interference contrast (DIC) imaging system. The second step is that it identifies the conductive microspheres and their shape deformation rate using quantitative analysis of the characteristics of the DIC images. The final step is that it inspects ACF bonding using a back propagation trained neural network. The result shows that the miss rate is lower than 0.1%, and the false inspection rate is lower than 0.05%.
Automatic identification of fungi in microscopic fecal images provides important information for evaluating digestive diseases. To date, disease diagnosis is primarily performed by manual techniques. However, the accuracy of this approach depends on the operator’s expertise and subjective factors. The proposed system automatically identifies fungi in microscopic fecal images that contain other cells and impurities under complex environments. We segment images twice to obtain the correct area of interest, and select ten features, including the circle number, concavity point, and other basic features, to filter fungi. An artificial neural network (ANN) system is used to identify the fungi. The first stage (ANN-1) processes features from five images in differing focal lengths; the second stage (ANN-2) identifies the fungi using the ANN-1 output values. Images in differing focal lengths can be used to improve the identification result. The system output accurately detects the image, whether or not it has fungi. If the image does have fungi, the system output counts the number of different fungi types.