Electron Microscope Threshold Calibration for Accurate Electron Counting
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Solution Overview
Problem
Existing electron microscopes face issues with setting appropriate thresholds for binarization, leading to noise being counted as incident electrons or count loss, which affects the detective quantum efficiency and image quality.
Innovation Solution
An electron microscope with a computation unit that repeatedly sets tentative thresholds, acquires frame images following a Poisson process, binarizes and integrates them to obtain an optimal threshold based on normalized constants, ensuring accurate binarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed threshold is used for binarization, then the processing is simple and fast, but noise may be counted as incident electrons or count loss occurs
Solution Approach 1:
The patent applies dynamics by making the threshold adaptive rather than fixed. The computation unit automatically adjusts the threshold based on the statistical properties (mean and standard deviation) of the detected signal, allowing the threshold to dynamically adapt to varying signal conditions while maintaining both processing efficiency and measurement precision
Solution Approach 2:
The patent implements feedback by using the detected signal characteristics to adjust the threshold. The system calculates the mean and standard deviation from the detected events and uses this feedback information to set an optimal threshold that adapts to the actual signal conditions, preventing both noise misclassification and count loss
2Measurement precision
If counting processing is performed on each frame at high frame rate, then linearity is improved, but appropriate threshold setting becomes critical and difficult
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine its own optimal threshold without external calibration or manual intervention. The computation unit autonomously calculates the threshold based on the statistical properties of the detected events, making the system self-calibrating and eliminating the difficulty of threshold setting while maintaining high linearity
Solution Approach 2:
The patent changes the parameter approach by transitioning from fixed threshold values to statistically-derived thresholds. By calculating the threshold based on the mean and standard deviation of the detected signal, the system adapts the threshold parameter to the actual measurement conditions, simultaneously achieving high linearity and simplifying the measurement process
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
The method allows for appropriate threshold setting, improving linearity between pixel values and incident electron counts, reducing noise, and maintaining detective quantum efficiency.
Implementation Method 1
a camera that includes an image sensor and outputs a frame image which is based on a signal obtained by electrons entering each cell of the image sensor
Implementation Method 2
acquiring a plurality of the frame images obtained on a condition that electrons entering the image sensor follow Poisson process
Data Source
AI summary
An electron microscope includes an electronic optical system that irradiates a specimen with an electron beam and forms an image; a camera that includes an image sensor and outputs a frame image; and a computation unit that generates an image based on the frame image. The computation unit sets a threshold; and binarizes the frame image using the threshold, and generates the image based on the binarized frame image. In setting the threshold, the computation unit repeatedly sets a tentative threshold, acquires a plurality of the frame images obtained on a condition that electrons entering the image sensor follow Poisson process, binarizes each of the plurality of acquired frame images using the tentative threshold, generates an integrated image by integrating the plurality of binarized frame images, and obtains a normalized constant based on a mean and variance of pixel values of pixels of the integrated image.