Fred Lakhani

Inspection in Semiconductor Manufacturing

The sections in this article are 1 Defect Reduction Cycle in Semiconductor Manufacturing 2 Inspection in the IC Manufacturing Process Life Cycle 3 Optical Imaging Technology 4 Laser-Scattering Technology 5 Measurement of Optical Scatter from Contaminants on Wafers 6 Automatic Defect Classification 7 Future Challenges 8 Conclusions 9 Acknowledgments

Automatic classification of spatial signatures on semiconductor wafermaps

This paper describes spatial signature analysis (SSA), a cooperative research project between SEMATECH and Oak Ridge National Laboratory for automatically analyzing and reducing semiconductor wafermap defect data to useful information. Trends towards larger wafer formats and smaller critical dimensions have caused an exponential increase in the volume of visual and parametric defect data which must be analyzed and stored, therefore necessitating the development of automated tools for wafer defect analysis. Contamination particles that did not create problems with 1 micron design rules can now be categorized as killer defects. SSA is an automated wafermap analysis procedure which performs a sophisticated defect clustering and signature classification of electronic wafermaps. This procedure has been realized in a software system that contains a signature classifier that is user-trainable. Known examples of historically problematic process signatures are added to a training database for the classifier. Once a suitable training set has been established, the software can automatically segment and classify multiple signatures from a standard electronic wafermap file into user-defined categories. It is anticipated that successful integration of this technology with other wafer monitoring strategies will result in reduced time-to-discovery and ultimately improved product yield.

Integrated applications of inspection data in the semiconductor manufacturing environment

As integrated circuit fabrication processes continue to increase in complexity, it has been determined that data collection, retention, and retrieval rates will continue to increase at an alarming rate. At future technology nodes, the time required to source manufacturing problems must at least remain constant to maintain anticipated productivity as suggested in the International Technology Roadmap for Semiconductors. Strategies and software methods for integrated yield management have been identified as critical for maintaining this productivity. Integrated yield management must use circuit design, visible defect, parametric, and functional test data to recognize process trends and excursions so that yield-detracting mechanisms can be rapidly identified and corrected. This will require the intelligent merging of the various data sources that are collected and maintained throughout the fabrication environment.

Using historical wafermap data for automated yield analysis

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- 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 o...

Content-based image retrieval for semiconductor process characterization

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.

Content-based image retrieval for semiconductor manufacturing

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 content-based 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.

The use of historical defect imagery for yield learning

The rapid identification of yield detracting mechanisms through integrated yield management is the primary goal of defect sourcing and yield learning. At future technology nodes, yield learning must proceed at an accelerated rate to maintain current defect sourcing cycle times despite the growth in circuit complexity and the amount of data acquired on a given wafer lot. As integrated circuit fabrication processes increase in complexity, it has been determined that data collection, retention, and retrieval rates will continue to increase at an alarming rate. Oak Ridge National Laboratory (ORNL) has been working with International SEMATECH to develop methods for managing the large volumes of image data that are being generated to monitor the status of the manufacturing process. This data contains an historical record that can be used to assist the yield engineer in the rapid resolution of manufacturing problems. To date there are no efficient methods of sorting and analyzing the vast repositories of imagery collected by off-line review tools for failure analysis, particle monitoring, line width control and overlay metrology. In this paper we will describe a new method for organizing, searching, and retrieving imagery using a query image to extract images from a large image database based on visual similarity.

Field test results of an automated image retrieval system

The rapid identification of yield detracting mechanisms through integrated yield management is the primary goal of defect sourcing and yield learning. At future technology nodes, yield learning must proceed at an accelerated rate to maintain current defect sourcing cycle times despite the growth in circuit complexity and the amount of data acquired on a given wafer lot. As integrated circuit fabrication processes increase in complexity, it has been determined that data collection, retention, and retrieval rates will continue to increase at an alarming rate. Oak Ridge National Laboratory (ORNL) has been working with International SEMATECH (ISMT) to develop methods for managing the large volumes of image data that are being generated to monitor the status of the manufacturing process (Tobin et al, 1999 and 2000). This data contains an historical record that can be used to assist the yield engineer in the rapid resolution of manufacturing problems. To date, there are no efficient methods of sorting and analyzing the vast repositories of imagery collected by off-line review tools for failure analysis, particle monitoring, line width control, and overlay metrology. In this paper, we describe a new method for organizing, searching, and retrieving defect imagery based on visual similarity. The results of an industry field test of the ORNL image management system at two independent manufacturing sites are also described.

Field-test results of an image retrieval system for semiconductor yield learning

Images of semiconductor defects are maintained in semiconductor yield management systems to diagnose problems that arise during the manufacturing process. A semiconductor-specific content-based image retrieval system was developed by Oak Ridge National Laboratory under the auspices of International SEMATECH (ISMT) during 1998 - 1999. The system uses commercial databases to store image information and uses a customized indexing technology to rapidly retrieve similar images. Additional defect information (position, wafer ID, lot, etc) has now been incorporated into the system through the use of additional database tables. During Fall 2000, the system was deployed in two ISMT member company fabs to demonstrate the utility of this approach in managing large databases of images and to show causal relationships between image appearance and wafer information such as processing layer, wafer lot, analysis dates, etc. This paper summarizes the results of these field tests and shows the utility of this approach through data analysis conducted on approximately one month of historical defect data.

Industry survey of automatic defect classification technologies, methods, and performance

To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. 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. Towards this end, a significant level of investment has been made over the past decade to bring to maturity viable technologies for Automatic Defect Classification (ADC) as a means of automating the recognition and analysis of defect imagery captured during in-line inspection and off-line review. ADC has been developed to provide automation of the tedious manual inspection processes associated with defect review. Although significant advances have been achieved in the capabilities of ADC systems today, concerns continue to persist regarding effective integration, maintenance, and usability of commercial ADC technologies. During the summer of 2001, the Oak Ridge National Laboratory and International SEMATECH performed an industry survey of eight major semiconductor device manufacturers to address the issues of ADC integration, usability, and maintenance for the various in-line inspection and review applications available today. The purpose of the survey was to determine and prioritize those issues that inhibit the effective adoption, integration, and application of ADC technology in todays fabrication environment. In this paper, we will review the various ADC technologies available to the semiconductor industry today and discus the result of the survey.