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24 SMT007 MAGAZINE I JUNE 2019 and optimization? Simply stated, the systems must satisfy the three "Rs" for measurement data: reliable, repeatable, and relatable. When Johnson asked Tomor which equip- ment they were using to automate inspection, her answer was straightforward: "Koh Young." Reliable Koh Young's implementation of full 3D cov- erage monitors performance to detect common defects, such as missing or wrong components, and accurately identifies other issues, such as coplanarity and lead bridging. By measuring components and solder joints, and then offer- ing critical height information to the inspec- tion algorithms, contract manufacturers can use reliable measurements to identify errors during the production. For example, after Sun- tronic adopted Koh Young 3D inspection solu- tions, their yields increased into the high 90% range, which has helped to transform their operations. But what's next? How can they continue to improve? Tomor further commented, "We also have a post-reflow AOI after the oven tells us if any- thing shifted. It uses true 3D technology, so it can measure if the part is skewed, missing, shifted, tombstoned, etc. Between the Koh Young SPI and AOI solutions, we increased our yield." When asked what else helps with streamlining the assembly process, Tomor sim- ply stated, "New equipment helps a lot." That new equipment solution, though, pays off by prioritizing data over raw throughput. Global competition means that manufacturers place challenging demands on process solu- tions. Manufacturers want to monitor and adapt the process to achieve zero defects by accessing all of the data anytime, anywhere. Moreover, manufacturers want process opti- mization. 3D inspection solutions have been instrumental in providing better data in the form of body and lead tip measurement, allow- ing the new equipment to quantify shape, coplanarity, solder amount, etc. Koh Young 3D inspection solutions, for exam- ple, measure the component and solder joint per the IPC-610 standard, generating a signif- icant set of reliable measurement data. This data is the foundation for Industry 4.0. Con- sequently, advanced inspection systems must evolve beyond simply judging "pass/fail" into functioning as highly intuitive, dynamic deci- sion-making systems, which emphasizes the need for reliable data. Of course, maintaining quality, repeatable measurement data is not enough to realize a smart factory. The system must also instantly analyze the data with relevant indicators, including yield rate, NG (no good) analysis, PPM analysis, gage R&R, offset analysis, and more metrics that allow manufacturers to com- pare board performance and identify process deviations. Artificial intelligence (AI) engines and machine learning can empower systems to help customers analyze and optimize the pro- duction process by managing the data from connected SPI and AOI systems.

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