technology LEDmanufacturing
automatically alert the engineer to process excursions based on signature classification and analysis, even if wafers have a defect count that is within spec. SSA also enables defect signature monitoring by zone, associating spatial signature classes - including ring, radial, scratch, and line - with pre-defined areas of the wafer, such as its edge. By using SSA in combination with SPC and DSA, engineers can utilise quantifiable data to support timely, real-world decision making. For example, it is possible to set up trend charts to monitor the occurrence of specific edge signatures and/or clusters. Feedback from DSA functions can also be used to confirm and/or modify SSA rules and SPC charts.
Figure 6: The best approach to defect analysis begins with the aggregation of defect data in a centralized database
What’s more, SSA signature data can be used to identify and ignore non yield-limiting nuisance defects that do not impact failure rates. For example, post-epi particle defects rarely impact LED yield, but if they occur in large numbers and vary significantly from lot-to-lot they can trigger a false- excursion event. SSA algorithms can be pre-tuned to recognize such conditions and to avoid false alerts.
Engineers have the option to use the SSA Recipe Editor to effectively “train” the SSA node, by customizing rules and incorporating signatures from sample wafer data (see Figure 7). This can lead to refinements of process control methods, if SSA recipes are saved into the universal database and linked to DSA files and SPC charts.
Figure 7: Spatial Signature Analysis (SSA) identifies key process signatures
of defect changes from layer to layer throughout the process with a Defect Transition Table (DDT), an advanced feature of DSA. This allows them to narrow the search for the sources of problems, and it also supports tracing of the transition of defects on a wafer as this moves between steps in the flow. Using DDT in combination with quantitative layer-by-layer ‘adder’ analysis and wafer map galleries allows rapid identification of root causes. By being able to compare data and images from throughout the process, engineers are also better placed to see the ‘big picture’ and determine interrelationships that are not obvious. The result: faster, more accurate DSA for supporting corrective actions.
Another powerful tool is Spatial Signature Analysis (SSA). This enables detection and classification of spatial signatures, such as defect clusters and patterns, which can indicate an out-of-spec process or a process tool problem. Tailored SSA recipes can be set up to automatically identify, analyse and characterize process-induced signatures and defect clusters for specific LED designs. This allows SSA to
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www.compoundsemiconductor.net July 2011
Summing the parts Tying everything together is our Klarity LED comprehensive yield management software system. This leverages proven techniques that produce excellent results in the silicon industry. However, this software has been adapted to address the specific requirements and unique challenges of LED manufacturing. It caters for small die and thousands of devices on a typical LED wafer, enabling far more efficient and effective management of LED fab yields than is possible with manually managed systems. The automated software approach spans the entire production flow from end-to-end; combining yield analysis, excursion responses, front-end to back-end correlations, and corrective actions.
By adopting a holistic approach to yield management, all of the pieces of the puzzle can be brought together, leading to accelerated process development, faster ramp up of production yields, improved quality levels, faster excursion detection, and an overall more cost-effective LED manufacturing process. This will help to drive down the cost of LEDs, and fuel their deployment in emerging markets, such as general illumination.
© 2011 Angel Business Communications. Permission required.
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