SEM Image Enhancement Using Pulsed-Beam Hybrid Reconstruction
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Solution Overview
Problem
Current charged particle microscopy techniques face challenges in minimizing sample damage while maintaining image quality and signal-to-noise ratio due to the use of reduced beam strength or pulsed beams, which limit the investigation capabilities at high magnification levels.
Innovation Solution
A hybrid machine learning method that involves acquiring multiple images with a pulsed charged particle beam, upsampling each image using non-linear transformations, and combining them to form a noise-reduced image, enhancing signal-to-noise ratio and contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the beam strength of the charged particle beam is decreased to lower sample damage, then sample damage is reduced, but the emissions generated by irradiation are decreased, resulting in reduced detector data and decreased information in sample images
Solution Approach 1:
The patent employs pulsed charged particle beam irradiation instead of continuous beam irradiation. The beam is delivered in periodic pulses with specific duty cycles, allowing the sample to rest between pulses and reducing cumulative damage while still generating sufficient emissions during each pulse for detector data acquisition.
Solution Approach 2:
The patent applies machine learning algorithms to predict and enhance image information before final image formation. The system uses trained models to anticipate missing information from low-strength beam irradiation and reconstructs enhanced images by predicting structures and features that would be present with higher beam strength.
2Object-affected harmful factors
If a pulsed charged particle beam is used to irradiate the sample periodically, then sample damage per pulse is limited, but the emissions induced from the sample are greatly reduced compared to traditional steady beam irradiation, resulting in decreased detector data and poor signal-to-noise ratio
Solution Approach 1:
The patent combines multiple pulsed beam images acquired at different time points and potentially different pulse intensities to create a composite enhanced image. By merging information from multiple pulses and using machine learning reconstruction, the system achieves sufficient signal-to-noise ratio while maintaining the benefits of pulsed irradiation.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the raw detector data from pulsed beam irradiation and the final enhanced image. The trained models act as mediators that can infer and reconstruct image information even when the raw signal from individual pulses is weak or noisy.
3Measurement precision
If multiple images are captured across multiple pulses to compensate for reduction in data, then the ability to investigate samples at high magnification is improved, but the process complexity increases and time is required to combine information from multiple images
Solution Approach 1:
The patent employs machine learning models that are trained on reference data and then applied to enhance experimental images. The system uses feedback from the training phase to optimize the enhancement process, allowing automated processing of multiple pulsed beam images without requiring complex manual intervention for each image combination.
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 improves image quality by increasing the signal-to-noise ratio by a factor of the square root of two, providing clearer and more detailed images of the sample.
Implementation Method 1
a pulsed charge particle source configured to emit a pulsed charged particle beam towards a sample
Implementation Method 2
a detector system configured to generate detector data from emissions from the sample resultant from one or more pulses of the pulsed charged particle beam being incident on the sample
Data Source
AI summary
Methods and systems for performing a hybrid machine learning method for enhancing scanning electron microscopy (SEM) images are disclosed herein. Methods include the steps of acquiring a plurality of images of a region of a sample that were each generated by irradiating the sample with a pulsed charged particle beam, upscaling each of the individual images to generate a plurality of upscaled images of the region of the sample, and combining the plurality of upscaled images to form a noise reduced image of the region of the sample.