Smart Defect Screening for IC Layout Pattern Groups
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Solution Overview
Problem
The shrinking feature size in integrated circuit fabrication leads to increased sensitivity requirements for defect detection, resulting in higher non-killer defect rates and limited capacity for SEM review, making it difficult to identify systematic defects promptly, which can cause yield loss and prolong volume production cycles.
Innovation Solution
A method for smart defect screening and sampling that preprocesses design layouts into pattern groups, extracts defect data, constructs composite pattern groups, applies defect sample selection rules, and generates a defect pattern library for efficient systematic defect capture and review, allowing for earlier identification of potential systematic defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensitivity of scan and inspection tool is increased to capture all killer defects, then defect detection capability is improved, but number of detected defects increases and percentage of non-killer defects becomes higher
Solution Approach 1:
The patent segments the large set of detected defects into meaningful groups based on layout pattern similarity. By dividing defects into pattern groups (PGs) and defect composite pattern groups (DCPGs), the system organizes the overwhelming number of defects into manageable categories that can be efficiently reviewed, resolving the contradiction between detecting all defects and managing the volume of detected defects.
Solution Approach 2:
The patent merges multiple defects that share similar layout pattern characteristics into single defect composite pattern groups. By combining defects with identical or similar layout patterns into representative DCPGs, the system reduces the total number of individual defects that need review while preserving all critical information, thus resolving the contradiction between comprehensive detection and manageable review volume.
2Measurement precision
If SEM review tool is used to observe and classify all killer defects, then defect classification accuracy is improved, but review time and Review SEM capacity are restricted
Solution Approach 1:
The patent performs preliminary grouping and organization of defects into layout pattern groups and defect composite pattern groups before the actual SEM review process. By pre-organizing defects into representative DCPGs with selected representative defects, the system reduces the review workload while maintaining classification accuracy, thus resolving the contradiction between thorough classification and time consumption.
Solution Approach 2:
The patent creates representative copies (DCPGs) that capture the essential characteristics of multiple similar defects. Instead of reviewing every single defect individually, the system reviews representative defects from each DCPG, which serve as copies that embody the common features of the group, thereby reducing review time while maintaining classification accuracy.
3Productivity
If review sampling count per wafer is limited by time and Review SEM capacity, then review efficiency is maintained, but ability to identify defect of interest type is reduced
Solution Approach 1:
The patent applies local quality by making different layout pattern groups have different review priorities and sampling rates. High-priority DCPGs that may contain systematic defects receive higher review sampling rates, while low-priority groups receive lower rates. This differentiated approach maintains overall review efficiency while ensuring adequate identification of critical systematic defects.
Solution Approach 2:
The patent dynamically adjusts review sampling parameters based on defect characteristics, layout pattern complexity, and priority levels. By changing the sampling rate parameter adaptively for different DCPGs rather than applying a uniform sampling rate, the system optimizes both review efficiency and the ability to identify systematic defects across different defect types.
Data Source
AI summary
A method for smart defect review is disclosed. The method includes pre-processing a design layout into a plurality of layout based pattern groups, dividing the design layout into a plurality of cells, overlapping the cells belong to the same layout based pattern groups, extracting a plurality of defect data of all defects on a wafer, constructing a plurality of layout based defect composite pattern groups, executing layout pattern match to obtain each individual layout based defect composite pattern group, performing some defect sample selection rules to each layout based defect composite pattern group, sorting the layout based defect composite pattern groups into different defect types, obtaining a defect image file by reviewing different sample number of defect image from each layout based defect composite pattern group, and generating a defect pattern library or a defect yield prediction by performing a defect yield diagnosis to the defect image file.