In electronics manufacturing, a common measurement technique is Automated Optical Inspection (AOI) 3D AOI, which offers significant improvements over 2D.
3D Automated Optical Inspection (AOI)
With component sizes continuously decreasing and new packages (e.g. BGA, flipchip), the third dimension has become more important and made improved measurement methods necessary. With just a 2D view from the top, one can only see defects such as shifts, rotations, and cracks, but not whether components are flat on the board or the volume of solder paste.
While some 3D measurement systems may use 4-5 images per inspected ROI, more advanced systems use 20 images or even more to increase measurement accuracy and to add color vision.
The migration from 1 image for measurement, to multiple images results in more demands on the camera-based imaging system.
With 3D AOI, the most critical camera parameters are:
High Resolution Combined with High Frame Rate
High-speed 3D-AOI systems need more megapixels to get the 3rd dimension.
Higher resolution cameras allow for a larger area to be inspected at once and provide more data, which can improve accuracy. BUT since many images are required to perform quantitative measurements and the overall system throughput must be maintained, the camera frame rate must also be high. (for example 4 Megapixel at 180 fps or even 25 Megapixel at 32 fps and higher)
QE and Read Noise
Modern systems use advanced lighting techniques and algorithms to perform true 3D measurements with high accuracy. Extensive illumination can get very expensive. A poor match between illuminator wavelength and sensor QE results in poor measurement accuracy due to too much noise in the image. With optimized QE and read noise in the camera, the required illumination can be reduced.
Because these measurements use many consecutive images, image-to-image stability is critical. In-camera image processing and robust design ensure that our cameras produce repeatable images regardless of other system fluctuations, including temperature. The variations in the images must be smaller than the variations you are trying to measure so you are not measuring within the noise of the camera.
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