Content-based image retrieval for semiconductor manufacturing

Thomas P. Karnowski, Kenneth W. Tobin, Jr., Regina K. Ferrell, Fred Lakhani, Kenneth W. Tobin, Jr.
2000 Machine Vision Applications in Industrial Inspection VIII  
In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train automatic defect classification systems, and examine historical data for trends. Image management in semiconductor yield management systems is a growing cause of concern since many facilities collect 3000 to 5000 images each month, with future estimates of 12,000 to 20,000. Engineers at Oak Ridge National Laboratory (ORNL) have developed a semiconductor-specific
more » ... sed image retrieval architecture, also known as Automated Image Retrieval (AIR). We review the AIR system approach including the application environment as well as details on image interpretation for content-based image retrieval. We discuss the software architecture that has been designed for flexibility and applicability to a variety of implementation schemes in the fabrication environment. We next describe details of the system implementation including image processing and preparation, database indexing, and image retrieval. The image processing and preparation discussion includes a description of an image processing algorithm which enables a more accurate description of the semiconductor substrate (non-defect area). We also describe the features used that identify the key areas of the defect imagery. The feature indexing mechanisms are described next, including their implementation in a commercial database. Next, the retrieval process is described, including query image processing. Feedback mechanisms, which direct the retrieval mechanism to favor specified retrieval results, are also discussed. Finally, experimental results are shown with a database of over 10,000 images obtained from various semiconductor manufacturing facilities. These results include subjective measures of system performance and timing details for our implementation.
doi:10.1117/12.380070 fatcat:ugyo47gjxjah5jnfuomqs6yrg4