Charged Particle Beam Overlay Shift Defect Inspection
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Solution Overview
Problem
Current methods for inspecting overlay shift defects in semiconductor manufacturing are inefficient and inaccurate, relying on human observation of charged particle microscopic images, leading to poor accuracy and unnecessary waste in the manufacturing process.
Innovation Solution
A method involving charged particle beam inspection systems that form and analyze microscopic images by identifying and averaging inspection patterns to estimate the average width of features, comparing it to a threshold value to determine the presence of overlay shift defects, thereby automating the defect detection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observation and interpretation of charged particle microscopic images is used to detect overlay shift defects, then the inspection process is simple, but the accuracy of defect detection is poor
Solution Approach 1:
The patent replaces the mechanical human observation system with an automated image processing system that uses charged particle beam imaging combined with computational algorithms. The system automatically identifies pattern periods, averages images, estimates widths, and detects defects without human intervention, thereby improving measurement precision while managing system complexity through software automation.
Solution Approach 2:
The patent creates multiple copies of the charged particle microscopic image and processes them through averaging operations. By generating and comparing multiple image copies, the system enhances the reliability of defect detection and improves measurement accuracy through statistical analysis of repeated measurements.
2Measurement precision
If automated image processing with pattern averaging is implemented, then the accuracy of overlay shift defect detection is improved, but the complexity of the inspection system increases
Solution Approach 1:
The patent segments the inspection process into distinct automated stages: image acquisition, pattern period identification, image averaging, width estimation, and defect detection. This segmentation allows each function to be performed by specialized software modules, improving overall system automation while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent automatically adjusts processing parameters such as pattern period identification, averaging factors, and width estimation thresholds based on the specific characteristics of each semiconductor device being inspected. This adaptive parameter adjustment enhances detection accuracy for different device types while the system maintains automated operation without requiring manual recalibration.
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 significantly increases the accuracy of overlay shift defect inspection, reducing unnecessary waste and enabling timely detection during semiconductor manufacturing, thereby improving process control.
Implementation Method 1
A charged particle beam source 210 generates a charged particle beam, and then the charged particle beam is condensed and focused by a condenser lens module 220 and an objective lens module 230, respectively, to form a charged particle beam probe 240
Implementation Method 2
After charged particle beam probe 240 bombards the surface of sample 295, secondary charged particles 260 are induced to emit from the sample surface along with other charged particles of beam probe 240 reflected by sample 295
Data Source
AI summary
A method for inspecting overlay shift defect during semiconductor manufacturing is disclosed herein and includes a step for providing a charged particle microscopic image of a sample, a step for identifying an inspection pattern measure in the charged particle microscopic image, a step for averaging the charged particle microscopic image by using the inspection pattern measure to form an averaged inspection pattern measure, a step for estimating an average width from the averaged inspection pattern measure, and a step for comparing the average width with a predefined threshold value to determine the presence of the overlay shift defect.