SEM Image Calibration for Post-Maintenance Measurement Accuracy
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Solution Overview
Problem
Existing scanning electron microscope (SEM) image measurement methods struggle with increased measurement errors due to changes in electron irradiation and reflected electron amounts, particularly after preventive maintenance, which are not adequately corrected.
Innovation Solution
A method involving acquiring multiple SEM images at different time points, including before and after maintenance, to estimate a calibration factor using a training model, and correcting subsequent images with this factor to maintain consistent image characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SEM images are acquired at different time points to enable correction, then measurement precision is improved, but device complexity increases due to requiring multiple images and calibration processes
Solution Approach 1:
The patent applies preliminary action by acquiring a first image before preventive maintenance and a second image after maintenance to establish a calibration factor in advance. This calibration factor is then used to correct subsequent images, preventing measurement errors before they occur in normal operation. The training model is also trained in advance using these images to learn the relationship between pre- and post-maintenance image characteristics.
Solution Approach 2:
The patent introduces a calibration factor as an intermediary element that mediates between images taken at different time points (before and after maintenance). This calibration factor serves as a reference that allows comparison and correction of images acquired at different times, enabling consistent depth measurements across maintenance events without requiring complex real-time adjustments.
2Reliability
If multiple images are acquired for calibration, then reliability of measurements is improved, but loss of time increases due to additional image acquisition steps
Solution Approach 1:
The patent performs the time-consuming image acquisition and calibration factor establishment in advance, during scheduled maintenance periods. Once the calibration factor is established and the training model is trained, normal measurements can proceed quickly by simply applying the pre-computed calibration factor to new images, minimizing time loss in routine operations.
Solution Approach 2:
The training model automatically learns the calibration relationship from the acquired images and performs corrections autonomously. The system self-calibrates by comparing pre- and post-maintenance images and automatically applies the learned calibration to subsequent measurements, reducing manual intervention time and enabling the system to serve itself in the calibration process.
3Manufacturing precision
If calibration factor estimation is performed using training models, then manufacturing precision is improved, but device complexity increases due to neural network implementation
Solution Approach 1:
The training model acts as an intermediary that automatically computes the calibration factor by learning from pre- and post-maintenance images. Instead of requiring complex manual calibration procedures or sophisticated hardware adjustments, the neural network model serves as a software-based mediator that translates image differences into correction factors, simplifying the overall system while maintaining high precision.
Solution Approach 2:
The patent replaces potential mechanical or manual calibration adjustment mechanisms with a computational approach using neural networks. The training model processes images and generates calibration factors through software-based image analysis and machine learning, substituting physical adjustment mechanisms with intelligent algorithms that automatically adapt to maintenance-induced changes.
Data Source
AI summary
A processor-implemented method including acquiring a first image a first wafer at a first time point, acquiring a second image of the first wafer at a second time point after a predetermined amount of time from the first time point, estimating a calibration factor by using the first image and the second image, and correcting a third image of a second wafer by using the calibration factor.