Process Window Qualification Defect Binning
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Solution Overview
Problem
Current process window qualification (PWQ) techniques in semiconductor manufacturing are limited in directly assessing the impact of process variables like etch, deposition, and thermal processing, and rely on qualitative expert assessments, which can lead to inefficient defect sampling and missed critical defects.
Innovation Solution
A method and system that sort defects into categories based on process conditions and design similarity using a processor, selecting defects with the highest attribute values for further review, employing shape-based grouping and bin merge algorithms to prioritize critical defects for scanning electron microscope inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional PWQ inspection techniques are used, then wafer inspection can be performed, but defect identification efficiency is low and critical defects are not effectively assessed
Solution Approach 1:
The inspection wafer is divided into multiple die regions, each with different focus and/or exposure modulations. Defects are grouped into bins based on their associated design patterns and process conditions. This segmentation allows systematic evaluation of defect criticality across different process parameters while maintaining inspection efficiency.
Solution Approach 2:
Different regions of the inspection wafer are assigned different quality assessment criteria based on their specific process conditions (focus/exposure modulations). Each bin of defects is evaluated according to the local process conditions it represents, enabling precise assessment of defect criticality for each specific lithography parameter combination.
2Measurement precision
If all defects are inspected using SEM, then measurement precision is improved, but inspection time and resource consumption increase significantly
Solution Approach 1:
The method extracts and separates defects into different bins based on their association with specific design patterns and process conditions. This extraction allows prioritization of defect review based on criticality, enabling selective SEM inspection of only the most critical defects while maintaining overall measurement precision.
Solution Approach 2:
Defects are pre-grouped and categorized into bins based on design patterns and process conditions before SEM inspection. This preliminary classification action enables efficient triage and prioritization, allowing the inspection process to focus resources on the most critical defects first.
3Measurement precision
If design attributes are considered in defect evaluation, then defect criticality assessment is improved, but process complexity increases
Solution Approach 1:
The binning algorithm serves multiple functions simultaneously: it groups defects by design pattern, categorizes them by process conditions (focus/exposure modulations), and prioritizes them by criticality. This multi-functionality reduces overall process complexity while maintaining high measurement precision through comprehensive defect evaluation.
Data Source
AI summary
Techniques are provided that can select defects based on criticality of design pattern as well as defect attributes for process window qualification (PWQ). Defects are sorted into categories based on process conditions and similarity of design. Shape based grouping can be performed on the random defects. Highest design based grouping scores can be assigned to the bins, which are then sorted. Particular defects can be selected from the bins. These defects may be reviewed.