SEM Pseudo Reference Imaging for Wafer Defect Throughput
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Solution Overview
Problem
Existing sample observation systems for semiconductor wafers face challenges in improving throughput due to the need for acquiring reference images, which can be cumbersome especially when design data is not available.
Innovation Solution
A sample observation system that uses a scanning electron microscope and a calculator to acquire learning defect and reference images, calculate estimation processing parameters, and estimate pseudo reference images based on inspection defect images, thereby omitting the need for acquiring reference images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference images are acquired for each defect image to perform defect detection, then defect detection accuracy is improved, but observation throughput deteriorates due to the time-consuming acquisition process
Solution Approach 1:
The system performs preliminary actions by acquiring multiple reference images in advance from different regions of the wafer and storing them in a buffer memory before defect inspection begins. This allows the defect detection process to use pre-prepared reference images rather than acquiring them in real-time, thereby improving throughput while maintaining detection accuracy.
Solution Approach 2:
The system creates multiple copies of reference images from different wafer regions and stores them in buffer memory. These copied reference images are then used for defect detection without requiring additional real-time acquisition, thus improving observation throughput while maintaining the ability to perform accurate defect detection through comparison.
2Measurement precision
If design data is used to generate reference images, then reference image quality is improved, but system adaptability deteriorates due to confidentiality constraints in mass production lines
Solution Approach 1:
The system performs self-service by automatically acquiring reference images directly from the wafer during the inspection process itself, without requiring external design data. The reference images are obtained by imaging normal (defect-free) regions of the wafer, and the system uses these self-generated references for defect detection, thereby maintaining adaptability in mass production environments where design data is confidential.
Solution Approach 2:
The system introduces an intermediary approach by using actual wafer images from normal regions as intermediate reference data, rather than relying directly on design data. This intermediary reference image serves as a bridge between the physical wafer and the defect detection process, allowing the system to operate adaptively without exposing confidential design information.
3Reliability
If multiple reference images are acquired and stored in buffer memory, then defect detection reliability is improved through multiple comparisons, but device complexity increases
Solution Approach 1:
The buffer memory serves multiple functions: it stores reference images for defect detection, holds inspection images for analysis, and manages the flow of image data between the imaging device and processing unit. This multi-functionality reduces the need for separate dedicated storage components, thereby improving reliability through multiple reference comparisons while limiting the increase in overall device complexity.
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 enables the estimation of reference images and defect portions from defect images without using design data, thereby enhancing the throughput of sample observations.
Implementation Method 1
a scanning electron microscope (SEM) as an imaging device to image the sample
Data Source
AI summary
The invention provides a sample observation system including a scanning electron microscope and a calculator. The calculator: (1) acquires a plurality of images captured by the scanning electron microscope; (2) acquires, from the plurality of images, a learning defect image including a defect portion and a learning reference image not including the defect portion; (3) calculates estimation processing parameters by using the learning defect image and the learning reference image; (4) acquires an inspection defect image including a defect portion; and (5) estimates a pseudo reference image by using the estimation processing parameters and the inspection defect image.