Abstract
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.
Highlights
The ability to manage large image databases has been a topic of growing research
In this paper we have described a novel content-based image retrieval and management system that has been designed for manufacturing environments
Current image retrieval systems for semiconductor manufacturing depend on additional alphanumeric data to perform retrieval functions, which produces an inherent limitation to the process of locating historic imagery that may have been caused by a similar manufacturing process
Summary
The ability to manage large image databases has been a topic of growing research. Imagery is being generated and maintained for a large variety of applications including remote sensing, architectural and engineering design, geographic information systems, and weather forecasting. Digital imagery for failure analysis is generated between process steps from optical microscopy and laser scattering systems and from optical confocal microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and focused ion beam (FIB) imaging modalities This data is maintained in a data management system (DMS) and used by fabrication engineers to diagnose and isolate manufacturing problems. Current abilities to query the fabrication process are based primarily on product ID, lot number, wafer ID, time/date, process layer, engineer classification, or automatic defect classification (ADC) [19], and so forth This approach can be useful, it limits the user’s ability to quickly locate historical examples of visually similar imagery, especially for data that was placed in the database over one or two weeks prior.
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