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28 March 2017 Precise design-based defect characterization and root cause analysis
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As semiconductor manufacturing continues its march towards more advanced technology nodes, it becomes increasingly important to identify and characterize design weak points, which is typically done using a combination of inline inspection data and the physical layout (or design). However, the employed methodologies have been somewhat imprecise, relying greatly on statistical techniques to signal excursions. For example, defect location error that is inherent to inspection tools prevents them from reporting the true locations of defects. Therefore, common operations such as background-based binning that are designed to identify frequently failing patterns cannot reliably identify specific weak patterns. They can only identify an approximate set of possible weak patterns, but within these sets there are many perfectly good patterns. Additionally, characterizing the failure rate of a known weak pattern based on inline inspection data also has a lot of fuzziness due to coordinate uncertainty. SEM (Scanning Electron Microscope) Review attempts to come to the rescue by capturing high resolution images of the regions surrounding the reported defect locations, but SEM images are reviewed by human operators and the weak patterns revealed in those images must be manually identified and classified. Compounding the problem is the fact that a single Review SEM image may contain multiple defective patterns and several of those patterns might not appear defective to the human eye.

In this paper we describe a significantly improved methodology that brings advanced computer image processing and design-overlay techniques to better address the challenges posed by today’s leading technology nodes. Specifically, new software techniques allow the computer to analyze Review SEM images in detail, to overlay those images with reference design to detect every defect that might be present in all regions of interest within the overlaid reference design (including several classes of defects that human operators will typically miss), to obtain the exact defect location on design, to compare all defective patterns thus detected against a library of known patterns, and to classify all defective patterns as either new or known. By applying the computer to these tasks, we automate the entire process from defective pattern identification to pattern classification with high precision, and we perform this operation en masse during R & D, ramp, and volume production.

By adopting the methodology, whenever a specific weak pattern is identified, we are able to run a series of characterization operations to ultimately arrive at the root cause. These characterization operations can include (a) searching all pre-existing Review SEM images for the presence of the specific weak pattern to determine whether there is any spatial (within die or within wafer) or temporal (within any particular date range, before or after a mask revision, etc.) correlation and (b) understanding the failure rate of the specific weak pattern to prioritize the urgency of the problem, (c) comparing the weak pattern against an OPC (Optical Procimity Correction) Verification report or a PWQ (Process Window Qualification)/FEM (Focus Exposure Matrix) result to assess the likelihood of it being a litho-sensitive pattern, etc. After resolving the specific weak pattern, we will categorize it as known pattern, and the engineer will move forward with discovering new weak patterns.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qian Xie, Panneerselvam Venkatachalam, Julie Lee, Zhijin Chen, and Khurram Zafar "Precise design-based defect characterization and root cause analysis", Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 101452W (28 March 2017);

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