The sections in this article are Defect Reduction Cycle in Semiconductor Manufacturing Inspection in the IC Manufacturing Process Life Cycle Optical Imaging Technology Laser‐Scattering Technology Measurement of Optical Scatter from Contaminants on Wafers Automatic Defect Classification Future Challenges Conclusions Acknowledgments

As semiconductor device density and wafer area continue to increase, the volume of in-line and off-line data required to diagnose yield-limiting conditions is growing exponentially. To manage this data in the future, analysis tools will be required that can automatically reduce this data to useful information, e.g., by assisting the engineer in rapid root-cause diagnosis of defect generating mechanisms. In this paper, we describe a technology known as Spatial Signature Analysis (SSA) and its application to both opticallydetected defect data as well as electrical test (e-test) bin data. The results of a validation study are summarized that demonstrate the effectiveness of the SSA approach on optical defect wafermaps through field-testing at three semiconductor manufacturing sites on ASIC, DRAM and SRAM products. This method has been extended to analyze and interpret electrical test data and to provide a pathway for correlation of this data with in-line optical measurements. The image processing-based, fuzzy classifier system used for optical defect SSA has been adopted and applied to e-test binmaps to interpret and rapidly identify characteristic patterns, or "signatures", in the binmap data that are uniquely associated with the manufacturing process. An image of the binmap is created, and features such as mass, simple moments, and invariant moments are extracted and presented to a pair-wise, fuzzy, k-NN classifier. The preliminary performance results show an 84% correct e-test signature classification rate, even under sub-optimal training conditions.

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To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. This includes data collected from in-line and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being used to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer's ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through "automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time." SEMATECH and the Oak Ridge National Laboratory † have been developing new strategies and technologies for providing the yield engineer with higher levels of assisted data reduction for the purpose of automated yield analysis. In this paper, we will discuss the current state of the art and trends in yield management automation.

Image data management in the semiconductor manufacturing environment is becoming more problematic as the size of silicon wafers continues to increase, while the dimension of critical features continues to shrink. Fabricators rely on a growing host of image-generating inspection tools to monitor complex device manufacturing processes. These inspection tools include optical and laser scattering microscopy, confocal microscopy, scanning electron microscopy, and atomic force microscopy. The number of images that are being generated are on the order of 20,000 to 30,000 each week in some fabrication facilities today. Manufacturers currently maintain on the order of 500,000 images in their data management systems for extended periods of time. Gleaning the historical value from these large image repositories for yield improvement is difficult to accomplish using the standard database methods currently associated with these data sets (e.g., performing queries based on time and date, lot numbers, wafer identification numbers, etc.). Researchers at the Oak Ridge National Laboratory have developed and tested a content-based image retrieval technology that is specific to manufacturing environments. In this paper, we describe the feature representation of semiconductor defect images along with methods of indexing and retrieval, and results from initial field-testing in the semiconductor manufacturing environment.