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12 SMT007 MAGAZINE I NOVEMBER 2020 Of course, simply collecting reliable and repeatable measurement data is not enough on its own to realize a smart factory. The system must also instantly analyze the data with rel- evant indicators like yield rate, no-good (NG) analysis, parts per million (PPM) analysis, Gage R&R (repeatability and reproducibility), offset analysis, and other key metrics. When combined, these metrics allow manufacturers to compare board performance and identify process deviations from critical process steps like printing with an SPI, placement with a pre-reflow AOI, and reflow with a post-reflow AOI (Figure 1). Measurement-Based Inspection Process optimization is desired by every manufacturer, as well as equipment suppli- ers, including automated inspection providers. However, it has been difficult to realize due to the limitation of two-dimensional (2D) imag- ing, which was the de facto standard for the past 25 years. It is difficult for 2D AOI systems to identify defects on a curved and reflective solder joint, and 2D and 2.5D AOI systems do not generate reliable measurement-based data. Every aspect of the 2D/2.5D inspection process relies on contrast, not quantitative measurement. As such, 2D/2.5D AOI users must either scrap or repair defective boards, which increases costs and eliminates process improvement opportunities. At the same time, the results from these non-measurement-based systems limit the application of AI to improve all aspects of the inspection environment. The introduction of 3D imaging to the inspec- tion market solved many of the problems. By measuring components and solder joints, and then offering critical height information to the icant set of reliable and accurate measurement data. True 3D utilizes all measurement data, but not all 2.5D and 3D systems work this way. The 2.5D and most 3D systems rely on non-measurement-based 2D technology while incorporating a small amount of 3D capabil- ity to simply provide 3D images and—in some cases—basic measured values. The Smart Factory Data—especially from inspection and test systems—is the foundation for Industry 4.0 and smart factories, so advanced systems must evolve from simply judging pass/fail situations into highly intuitive, dynamic decision-making systems. This emphasizes the need for reliable, repeatable, and relatable data. Artificial intelligence (AI) engines can empower systems to help manufacturers ana- lyze and optimize the PCBA process by man- aging process data from connected SPI, pre- reflow AOI, and post-reflow AOI systems. Ideally, the AI system collects all inspection and measurement data from equipment in the line and then delivers information anywhere within the network with an intuitive, web- based user interface. Communications The machine-to-machine (M2M) commu- nication standards—guided in part by Indus- try 4.0—are altering the manufacturing process by improving metrics like first-pass yield and throughput with autonomous process adjust- ments that increase board quality and reduce production costs. As part of this path, certain process control software suites like KSMART can revolutionize data collection and analysis and— more importantly—PCBA process optimization. Figure 1: An example of a line configuration to improve yield with multipoint data correlation.

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