Hundreds of thousands of high resolution images are generated every week – and in some cases every day. But these images are significantly underutilized. Their rich information content is typically reduced to a single class code number, and roughly 50% of the images are classified as SEM Non-Visual (SNV) and discarded.
When images are reduced to a single class code and half the images are thrown away, there is a huge loss in the potential value of both the images and the imaging tools.
D2DB-PM not only provides a solution to the problem of image underutilization, but it goes much further by taking Line Monitoring to the next level with the integration of advanced image processing and die-to-database technologies that provide new dimensions and new insights into the yield enhancement workflow. It studies every image in great detail – even if it is an SNV image – and then uploads all of the information into a central database where a historical record of that information is built. The historical record opens a wealth of possibilities for new yield enhancement applications.
Pattern Tracking Database
Inline Real-Time Pattern Monitor
Seamless Integration into Production
(Note that good contour extraction is dependent on good source image quality.)
Zero Waste Policy — Maximize ROI of SEM
- The rich content of images is not reduced to single class codes.
- SEM Non-Visual (SNV) images that would otherwise be discarded are fully utilized.
- Return-on-Investment (ROI) is maximized from the Fab’s considerable investment in SEM imaging tools.
- Realize the full potential of high resolution images from SEM tools.
- Don’t throw away SEM Non-Visual (SNV) images!
- Use the rich information content from each and every image to monitor patterning quality and process drift in production fabs.
- Over time, as more and more images are analyzed, a comprehensive database of patterns is built – all based on real silicon instead of simulations.
- Finds all critical and consequential features with each SEM image, measures the printed fidelity of those features (by comparing contour to design), pulls out a larger pattern centered around each of those features, and tracks these patterns in a true relational database.
- Quickly see which patterns are printing well and which patterns are problematic.
- Determine if good patterns are becoming problematic or if problematic patterns are becoming good – and determine whether such change is produced by a mask or process revision, or some other factor.
- Compare the real silicon problematic patterns against the OPC simulation’s predicted weakpoints to determine the effectiveness of the OPC model.
- Generate care areas for inspection or for targeted SEM review based on the real silicon problematic patterns.
- Perform PWQ/FEM analysis by comparing the fidelity of patterns in each modulation to determine which ones are the best modulations.
- Linux 2.6 or later, 64-bit, x86 based processor.
- 16 or more physical cores.
- 128 GB or more physical memory.
- 2 TB or more available hard drive capacity.
Memory and hard drive requirements can vary substantially from customer to customer. Customers who expect to store large quantities of images on the server should allocate appropriate hard drive capacity. Customers who expect to process large numbers of images should allocate additional physical memory. Anchor Semiconductor will help each customer with the appropriate sizing of their hardware.