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The application of AOI (Automated Optical Inspection) is crucial across various industries such as general manufacturing,semiconductor production,and food processing,where operations are carried out under strict control environments. In recent years, due to the vertical integration of the semiconductor industry and the emphasis on product precision and yield improvement, AOI inspection systems have become a critical concern for this industry. With the widespread adoption of AOI inspection systems, different industries and workplaces have established corresponding standards. While this ensures product quality in manufacturing, it has also increased the cost required for vertical integration. Therefore, this thesis aims to propose a solution to address these challenges. In addition to solving the aforementioned issues, it also aims to achievesystematic monitoring through functionalities like cloud-based logging. The AOI inspection and monitoring system presented in this thesis offer the capability to provide real-time inspection feedback images to users. It integrates with the Edge Impulse model for image learning, allowing users to adapt recognition to different product conditions. Furthermore, by leveraging FIREBASE as an intermediary, the system can transmit recognition data through the Internet of Things (IoT), enabling data centralization for this system. Compared to past monitoring systems, this thesis introduces the functionality of cloud-based logging. Besides recording routine parameter logs for adjustment references, it also facilitates the visualization of data, enabling inspection or discarding of products during specific time periods. This mitigates unnecessary inspection or elimination costs that arise from the inability to grasp comprehensive data in the past
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