Layout-Guided VC Defect Localization in RSEM Images
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Solution Overview
Problem
Existing semiconductor defect analysis methods struggle to accurately identify the root cause of voltage contrast defects in integrated circuits, often misidentifying or failing to detect them due to limitations in resolution and noise interference, which can lead to yield reduction.
Innovation Solution
A combined system of voltage contrast electron beam inspection (VC-EBI) and review scanning electron microscope (RSEM) imaging, utilizing a layout file and machine learning algorithms, to precisely map and analyze defects, enabling automated identification of root causes by aligning spatial coordinates and analyzing regions of interest in high-resolution RSEM images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If voltage contrast electron beam inspection is used to detect defects, then defect detection capability is improved, but measurement precision deteriorates due to noise interference and inability to accurately identify root causes
Solution Approach 1:
The patent combines voltage contrast electron beam inspection with review scanning electron microscope imaging and machine learning algorithms to create an integrated defect analysis system. This merging allows the system to leverage the fast defect detection of VC-EBI while using RSEM high-resolution imaging and ML to accurately identify root causes, thereby resolving the contradiction between detection capability and measurement precision.
Solution Approach 2:
The patent introduces a machine learning algorithm as an intermediary between the VC-EBI defect detection and the final root cause identification. The ML model processes the defect information from VC-EBI and cross-references it with RSEM images to accurately determine root causes, mediating between the conflicting requirements of fast detection and precise measurement.
2Measurement precision
If comprehensive defect analysis is performed to identify root causes, then measurement precision is improved, but productivity deteriorates due to time-consuming analysis processes
Solution Approach 1:
The patent performs preliminary defect detection using voltage contrast electron beam inspection before conducting comprehensive root cause analysis with review scanning electron microscope imaging. This preliminary action filters out obvious defects and prioritizes areas requiring detailed analysis, enabling the system to maintain high measurement precision while improving productivity by avoiding unnecessary comprehensive analysis of all defects.
Solution Approach 2:
The patent applies local quality analysis by focusing review scanning electron microscope imaging and machine learning analysis only on specific regions of interest where voltage contrast defects were detected. Rather than performing comprehensive analysis uniformly across the entire IC, the system concentrates resources on localized defect areas, thereby improving measurement precision for root cause identification while maintaining high productivity.
3Measurement precision
If high-resolution imaging is used to analyze defects, then measurement precision is improved, but device complexity increases due to multiple imaging systems and coordinate transformation requirements
Solution Approach 1:
The patent creates a multi-functional integrated system where the review scanning electron microscope serves both as a high-resolution imaging device for root cause analysis and as a reference for coordinate transformation. The machine learning algorithm serves multiple functions including defect classification, root cause identification, and validation. This multi-functionality reduces the need for separate dedicated systems, thereby improving measurement precision while managing device complexity.
Solution Approach 2:
The patent uses a layout file as a digital copy or representation of the IC structure to facilitate coordinate transformation between different imaging systems. Instead of requiring direct physical alignment and complex real-time transformation hardware, the system uses the layout file as an intermediary digital model that simplifies the coordination between VC-EBI and RSEM imaging systems, reducing device complexity while maintaining high measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances defect analysis efficiency by accurately localizing root causes of voltage contrast defects, reducing misidentification and improving yield by focusing inspections on specific areas, thereby guiding process improvements.
Implementation Method 1
a voltage contrast electron beam image of the IC is acquired
Implementation Method 2
a review scanning electron microscope image of the IC is acquired
Data Source
AI summary
Analysis of an integrated circuit (IC) includes acquiring a review scanning electron microscope (RSEM) image of the IC, and acquiring a voltage contrast electron beam image of the IC. A layout image is rendered from a layout file descriptive of a layout of the IC. A transform between spatial coordinates of the RSEM image of the IC and spatial coordinates of the voltage contrast electron beam image of the IC is determined using the layout image. A voltage contrast (VC) defect or other VC targeted pattern is identified in the voltage contrast electron beam image of the IC, and at least one region of interest (ROI) is located in the RSEM image of the IC associated with the VC defect using the spatial transform. The at least one ROI in the RSEM image of the IC is analyzed to produce information for the VC defect or other VC targeted pattern.