Gate-All-Around Transistor Defect Classification Via Multi-Energy SEM
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Solution Overview
Problem
Existing inspection tools struggle to distinguish between top defects and buried defects in gate-all-around (GAA) transistors, leading to inefficiencies in identifying yield-loss causing defects and reliability concerns, as they lack sufficient resolution and precision to classify these defects accurately.
Innovation Solution
A method combining optical inspection with high-resolution scanning electron microscopy (SEM) using multiple landing energies to generate images, analyzing contrast-to-noise ratios (CNR) at different depths, and applying threshold comparisons to classify defects as top, buried, or completely missing epitaxial fill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing inspection tools are used to detect defects in GAA transistors, then the inspection process is simple and fast, but the tools cannot distinguish between top defects and buried defects, leading to insufficient measurement precision
Solution Approach 1:
The inspection process is segmented into multiple stages: initial optical inspection for rapid defect location, followed by selective SEM imaging at different landing energies for specific defect depth classification. This segmentation allows the system to maintain simplicity for routine inspection while providing high precision when needed.
Solution Approach 2:
The patent introduces an intermediary classification system that uses contrast-to-noise ratio (CNR) analysis as a mediator between the inspection tool and defect identification. The CNR metrics serve as intermediate parameters that enable defect depth classification without requiring direct complex imaging of all defects.
2Measurement precision
If high-resolution SEM with multiple landing energies is used to classify defects, then defect classification precision is improved, but inspection time and process complexity increase
Solution Approach 1:
Optical inspection is performed as a preliminary action to identify and locate potential defects before applying the more time-consuming SEM analysis. This preliminary screening ensures that high-resolution SEM imaging is only applied to regions where defects are suspected, significantly reducing total inspection time.
Solution Approach 2:
The patent applies partial action by using multiple landing energies selectively rather than continuously. Specifically, it uses a first landing energy (e.g., 5 keV) for top defect detection and a second landing energy (e.g., 20 keV) for buried defect detection, applying each only where needed based on optical inspection results and CNR analysis.
3Reliability
If multiple landing energies are used in SEM imaging, then defect detection capability at different depths is improved, but device complexity and data processing requirements increase
Solution Approach 1:
Different landing energies are applied locally to different defect depths: a first landing energy (5 keV) is used for detecting top defects near the surface, while a second landing energy (20 keV) is used for detecting buried defects deeper in the structure. This local quality approach ensures optimal detection reliability for each defect type without unnecessarily complicating the overall process.
Solution Approach 2:
The system incorporates feedback through CNR analysis, where the contrast-to-noise ratios obtained from SEM imaging at different landing energies are fed back into the classification algorithm. This feedback mechanism automatically determines whether top defects, buried defects, or both are present based on the CNR patterns, reducing manual interpretation complexity.
4Productivity
If optical inspection alone is used, then the inspection process is fast and simple, but the precision to distinguish between top and buried defects is insufficient
Solution Approach 1:
The inspection workflow is segmented into two distinct phases: a fast optical inspection phase for high-throughput defect location, and a precision SEM analysis phase for accurate defect classification. This segmentation maintains high productivity for the majority of inspections while providing high precision for critical defect identification.
Solution Approach 2:
The patent merges two different inspection technologies (optical inspection and SEM imaging) into a unified multi-stage process. The optical inspection provides rapid overview and defect location, while SEM provides detailed depth classification, combining the advantages of both methods to achieve both high productivity and high 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
Enables immediate detection and classification of defects during semiconductor fabrication, reducing yield loss and improving product reliability by distinguishing between yield-killing top defects and tolerable buried defects, thus enhancing the efficiency of the manufacturing process.
Implementation Method 1
a first image at a first landing energy that is tuned to detect top defects and a second image at a second landing energy that is tuned to detect buried defects
Data Source
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AI summary
Systems, apparatus, articles of manufacture, and methods to detect defects in gate-all-around transistor architectures are disclosed. An apparatus includes interface circuitry; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to: determine a first contrast to noise ratio (CNR) in a first image of a location on a semiconductor wafer; determine a second CNR in a second image of the location on the semiconductor wafer; and determine whether the location includes a buried defect based on the first CNR and the second CNR.