February, 2020


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Page 79 Optimizing SMT Production with Artificial Intelligence Continued from previous page


challenging to correlate with the quantitative measurement of 3D objects. Koh Young measures the 3D


information of the components and solder joints. The company’s AOIs offer valuable and reliable measure- ment data, so they become the most reliable “sensor” on the line. The validity of the 3D data


is guaranteed, since the company uses 3D technology for all compo- nent types to extract the exact body dimensions. This is unlike other systems that use “blob detection,” which may be suscep- tible to external factors like board warpage and component proximity. The combination of meas-


urement and process data piles collected from its SPI and AOI systems, as well as from printers and mounters, allows Koh Young to deliver advanced AI features with reliable “big data.” Indeed, the quality of data is more impor- tant than the quantity of data to create effective and reliable solu- tions with high value proposition.


More Accurate Measurements


Koh Young’s use of AI


begins with solving the challenges of SMT assemblies. The solder and components on finished boards have many specular surfaces, which will reflect some light back to the camera, while creating strong inter-reflection with other lighting reflections. Since some of the reflected light


does not reach the camera, it gener- ates false signals that can cause


Inspection machines as “sensors.”


model and AI used for learning abnormal symptoms from the data allows Koh Young to detect and elim- inate abnormal measurements. Through this hybrid approach, the measurement accuracy is increased for many different scenarios. Koh Young is also proactively


applying AI in AOI programming. With the help of deep learning, the assignment of components on a PCB


ume or time-sensitive applications. The company’s prediction engine cat- egorizes components by lead count, type, measurement score, and more. These elements help cleaning, pars- ing, enriching, and shaping the data. Going forward, Koh Young will


apply AI for pad grouping and inspection condition tuning, while incrementally learning new packages at new sites.


measurement value errors. This specular reflection issue is becoming more difficult to deal with, along with increasing board density and decreasing component spacing. Koh Young uses AI to prevent


measurement errors by incorporat- ing learning directly in the inspec- tion system. The fusion of an analyt- ical approach using a mathematical


is becoming autonomous. KAP pro- poses the recommended inspection conditions, based on 3D measured data, which simplifies inspection con- dition programming and makes it faster and more consistent with the best mapping conditions. KAP can reduce job preparation


by up to 70 percent, making it an ideal solution for high-mix, low-vol-


Improving Yield With increasing component


miniaturization, improving inspec- tion quality and increasing through- put, programming is critical. Greater computational power is yielding bet- ter inspection solutions. AI can aid inspection by enabling machines to continuously learn and solve new problems. The main challenge is end-


to-end optimization. Harnessing the power of its AI, Koh Young has developed its KPO solution. KPO is a smart factory system driven by AI to control and opti- mize printing and mounting. KPO relies heavily on accurate 3D measurements and data and error detection from SPI and AOI machines. KPO includes three inter-


linking modules that exercise complex algorithms to develop closed-loop print process recom- mendations. The three modules are Printer Diagnostic Manager (PDM), Printer Advisor Manager (PAM), and Printer Optimizer Manager (POM). The AI engine actively opti-


mizes the printing process by combining real-time printing and SPI measurement data. PAM automatically performs DOEs for


detailed SPI analysis, using diagnos- tic algorithms and noise filtering models. It then recommends the ideal print parameters. PDM uses multiple anomaly


detection algorithms to actively opti- mize the print process and reduce false calls. The third module, POM, uses


Continued on page 81


See at IPC APEX, Booth 1007


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