Charged Particle Beam Nanometric Structure Evaluation
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Solution Overview
Problem
Current methods for evaluating nanometric structures within deep and narrow recesses face challenges in achieving high-resolution measurements due to limitations in image resolution and attribute analysis.
Innovation Solution
The use of charged particle beam systems, specifically scanning electron microscopes, to acquire high-resolution backscattered electron images, combined with neural networks to reduce attribute information and generate guidelines for evaluating feature heights and structure depths, enabling precise measurements of nanometric scale objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging methods are used to evaluate nanometric structures, then the evaluation process is simpler, but the measurement precision is insufficient for deep and narrow recesses
Solution Approach 1:
The evaluation process is segmented into multiple stages: image acquisition at high resolution, attribute determination, attribute reduction using neural networks, and guideline generation. This segmentation allows achieving high measurement precision through specialized techniques at each stage while managing overall system complexity through modular processing.
Solution Approach 2:
A neural network is introduced as an intermediary component that processes the relationship between image attributes and reference data. The neural network learns optimal attribute combinations and reduces dimensionality, enabling high-precision measurements without requiring direct complex analysis of all image features.
2Measurement precision
If high-resolution imaging is used to capture nanometric structures, then measurement precision improves, but the amount of attribute information increases making analysis more complex
Solution Approach 1:
The neural network extracts and retains only the most relevant attributes from the high-resolution image data that are actually needed for evaluating feature heights and structure depths. This extraction process maintains measurement precision by preserving critical information while discarding redundant data.
Solution Approach 2:
The system transforms the large set of image attributes into a reduced set of meaningful parameters through neural network processing. This parameter transformation maintains the essential information needed for precise measurement while reducing the overall data dimensionality and complexity.
3Measurement precision
If multiple attributes are analyzed from high-resolution images, then measurement accuracy improves, but the evaluation time increases
Solution Approach 1:
The neural network is trained in advance on reference data to learn the optimal mapping between image attributes and structural measurements. This preliminary training allows the system to quickly evaluate new images by applying the pre-learned relationships, reducing evaluation time while maintaining high measurement accuracy.
Solution Approach 2:
The system uses reference data to train the neural network, creating a feedback loop where the network learns from known accurate measurements. This feedback mechanism enables the system to optimize attribute selection and weighting, achieving high accuracy measurements more efficiently by leveraging learned patterns from training data.
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 allows for accurate evaluation of nanometric structures with resolutions at least one hundred times finer than reference data, effectively addressing the limitations of existing technologies by providing detailed attribute analysis and improved measurement precision.
Implementation Method 1
Acquiring the image may comprise collecting backscattered electrons by a backscattered electron detector that has a radial angular coverage range of tens of degrees
Implementation Method 2
Acquiring the image may also comprise rejecting secondary electrons by an energy filter that precedes the backscattered electron detector
Data Source
AI summary
A method for evaluating an object, the method may include acquiring, by a charged particle beam system, an image of an area of a reference object, wherein the area includes multiple instances of a structure of interest, and the structure of interest is of a nanometric scale; determining multiple types of attributes from the image; reducing a number of the attributes to provide reduced attribute information; generating guidelines, based on the reduced attribute information and on reference data, for evaluating the reduced attribute information; and evaluating an actual object by implementing the guidelines.