Defect Location Binning in Charged-Particle Inspection Systems

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing charged particle beam inspection systems face challenges in accurately identifying and locating defects in samples due to misalignment of inspection images with template images, especially when reference images include defects, leading to noisy data and failure in detecting real defects.

Innovation Solution

The implementation of a method and system for image analysis that involves obtaining an image of a sample, identifying features, generating a template image from a design layout, comparing the sample image with the template image, and processing the image based on the comparison, using a machine learning model to generate distortion-free and defect-free template images.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If reference images including defects are used for template matching, then the inspection system can operate with existing data, but the alignment accuracy deteriorates due to noisy data from defects

Engineering Contradiction:

Improveinspection throughputVSAvoiddefect location accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary defect removal and image restoration before template matching. By preprocessing the reference images to eliminate defects and restore original patterns, the system prepares clean templates in advance that enable accurate alignment without the noise interference from defects in the original reference images.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary restoration process that mediates between the defective reference images and the template matching operation. This restoration step acts as a bridge, converting defective reference images into clean templates that can be used for accurate defect detection without directly using the noisy original data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If traditional image comparison methods are used, then the system structure remains simple, but the defect detection accuracy deteriorates due to misalignment with defective reference images

Engineering Contradiction:

Improvesystem structureVSAvoiddefect detection accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent introduces a restoration module as an intermediary between the reference image and the comparison process. This module restores defective reference images to their original state before template matching, improving alignment accuracy without significantly complicating the overall system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary restoration of reference images before the actual defect detection process. By pre-processing the reference images to remove defects and restore original patterns, the system ensures accurate template matching without adding complexity to the core comparison algorithm.

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If manual defect location identification is used, then the system requires less complex processing, but the productivity deteriorates due to time-consuming analysis

Engineering Contradiction:

Improveprocessing complexityVSAvoidinspection speed

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent replaces manual defect location identification with an automated template matching system. By using algorithmic comparison between restored reference images and inspection images, the system automatically identifies defect locations without requiring manual analysis, thereby significantly improving inspection speed while maintaining manageable processing complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

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 the accuracy and throughput of defect detection by ensuring accurate alignment and identification of defects, reducing the impact of noisy data and improving the overall yield in semiconductor manufacturing processes.

Implementation Method 1

The primary electrons interact with the wafer and may be backscattered or may cause the wafer to emit secondary electrons

Methodology Applied

Scientific EffectBackscattering:

Implementation Method 2

The primary electrons interact with the wafer and may be backscattered or may cause the wafer to emit secondary electrons

Methodology Applied

Scientific EffectSecondary electron emission:

Data Source

PatentUS20250166166A1Systems and methods for defect location binning in charged-particle systems

Publication Date: 2025.05.22 ASML NETHERLANDS BV

AI summary

Apparatuses, systems, and methods for providing beams for defect detection and defect location binning associated with a sample of charged particle beam systems. A method of image analysis may include obtaining an image of a sample, identifying a feature captured in the image of the sample, generating a template image from a design layout of the identified feature, comparing 5 the image of the sample with the template image, and processing the image based on the comparison. In some embodiments, a method of image analysis may include obtaining an image of a sample, identifying a feature captured in the obtained image of the sample, mapping the obtained image to a template image generated from a design layout of the identified feature, and analyzing the image based on the mapping.