LEDmanufacturing technology
analysis, which shows that a one-sigma variation is more damaging than a three-sigma or ten-sigma- variation. Automated in-line inspection can capture these small deviations by narrowing the distribution and enabling much cleaner SPC charting (see Figure 4).
One of the major downsides of manual inspection is that it is inherently dependent on a variety of uncontrolled variables, including the level of operator training, time of day, attentiveness to task, inspection speed and the amount of product that can be inspected. The upshot of all these uncontrolled variables is excessive “noise” in the statistical distribution of inspection results, which makes it far harder to uncover subtle process excursions (see Figure 4). In contrast, automated in-line inspection systems eliminate subjectivity and variability in the inspection process. Engineers can then direct all their focus at identifying variability in the production process, which speeds the identification of minor excursions that have the potential to make a major impact. Automated in-line inspection stations can be deployed at key points throughout the fab, tailored to the complexity of the particular LED fab processes (see Figure 5). If customers choose to employ our tools, they will be equipping their chipmaking facilities with inspection technologies and platforms that have already undergone extensive evolution and refinement in fabs making silicon ICs.
Our automated tools employ advanced optics that features scan and detection algorithms for enabling high-throughput, high-sensitivity inspection. It is easy to configure them for both smaller die sizes that are difficult to handle with manual methods, and larger die sizes that require rapid yield improvement. At the final output step, these automated inspectors also combine go/no-go assessment and accurate multi- bin defect classification with rule-based binning algorithms.
Exposing defects Ideally, process engineers in LED fabs will quickly spot excursions at key production steps. This will empower them to determine root causes and take corrective action before the wave of cumulative errors propagates throughout the rest of the production line. The good news is that this is relatively easy to do when automated in-line inspection is combined with Defect Source Analysis (DSA), which can relate the various sources of defects to impacts later in the production sequence.
Defect information, such as from the Klarity LED product, from key points throughout the production flow is communicated to the centralised database as industry-standard KLARF data (KLA Report File). This data can include images, sort/bin data, and other fab inputs that enable seamless correlation of
July 2011
www.compoundsemiconductor.net 35
Figure 4: Manual inspection introduces a substantial amount of noise into any measurement. The upshot is that deviations can go unnoticed when manual inspection is used, but are easy to spot when automated inspection is employed
information from throughout the fab (see Figure 6). Each KLARF contains detailed information, including the ID, location and size of the defect, as well as other information from the inspection tools. Engineers can access the defect data in the centralized database from PC-based clients through the system, enabling them to quickly perform a range of analysis functions, such as
creating/updating SPC control charts, or generating wafer maps, Pareto charts or image galleries to support DSA activities.
Thanks to automated event-based triggers, engineers – who can access a full range of inspection data and perform extensive analysis functions directly from their desks or workstations – can quickly determine root causes of defects without having to make multiple trips into the cleanroom or assemble and tabulate defect data by hand. It is also possible for engineers to track the morphology
Figure 5: Key LED
process points for deploying automated in- line inspection based on third- generation tools
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