participants
Computational Intelligence Technology Center (CITC) is the first big data R&D center to bridge the big data technology across industries in Taiwan. CITC aims to develop intelligent analytics technologies to boost Artificial Intelligence and big data core capabilities for local IT software companies, also to apply intelligent analytics to help the related industries to enhance their productivity and create new business opportunities. The cross-disciplinary approach enables CITC’s core technology capabilities to provide intelligent analytics and machine learning algorithms that industries need. CITC develops an open Artificial Intelligence and big data platform to create solutions that facilitate the innovative application for service design and business model for industries.
Automated optical inspection (AOI) [1] is an automated visual inspection of printed circuit board (PCB) (or LCD,transistor) manufacture where a camera autonomously scans the device under test for both catastrophic failure (e.g. missing component) and quality defects (e.g. fillet size or shape or component skew). It is commonly used in the manufacturing process because it is a non-contact test method. It is implemented at many stages through the manufacturing process including bare board inspection, solder paste inspection (SPI), pre-reflow and post-reflow as well as other stages. The Institute of Electronics and Optoelectronics in Industrial Technology Research Institute(ITRI) has spent years on developing flexible displays, hoping to elevate the production quality with AOI technology during the pilot run. This time we have invited experts from different fields to join us, and focus on identifying defect classifications of AOI image data that are offered so as to elevate the identification efficiency of AOI through statistical science.
Reference
[1] https://en.wikipedia.org/wiki/Automated_optical_inspection
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After classification models of defect prediction are offered by researchers participating in the issue, the back-end of the system would process them in bathces regularly to calculate the score. Evaluations are conducted by calculating the corresponding accuracy rate of the actual value.
The following is the formula:
$$Accuracy = {\text{Number of correct predictions} \over \text{Number of total predictions}}$$
