Adaptive 3D Imaging Method for Industrial Metrology
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Solution Overview
Problem
Current 3D imaging methods for industrial metrology face challenges in optimizing the number of projection images needed for accurate imaging, leading to either inaccurate measurements due to insufficient images or excessive data acquisition time, as they fail to account for variations in imaging parameters and sample attributes.
Innovation Solution
A method that acquires a limited initial set of 2D images from differently oriented sample planes, iteratively increasing the number of planes based on measurement parameters and convergence criteria, using machine learning for convergence prediction and adaptive HART projection angles to optimize image quality and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of projection images are acquired to ensure imaging accuracy for all possible sample variations, then measurement precision is improved, but data acquisition time increases excessively
Solution Approach 1:
The system dynamically adjusts the number of projection images acquired based on real-time evaluation of sample attributes (density, geometry, topology, composition) and imaging parameters (beam energy). Instead of using a fixed worst-case projection count, the method adaptively determines the optimal number of projections needed for each specific sample, thereby reducing acquisition time while maintaining required measurement precision.
Solution Approach 2:
The invention changes the parameter of projection image count from a static worst-case value to a dynamic value that varies according to sample-specific attributes and imaging conditions. By evaluating sample characteristics and adjusting the number of projections accordingly, the system optimizes the balance between measurement accuracy and acquisition time for different measurement scenarios.
2Productivity
If a small number of projection images are acquired to reduce data acquisition time, then productivity is improved, but measurement precision deteriorates due to poor signal to noise ratio and large imaging artifacts
Solution Approach 1:
The system incorporates feedback mechanisms that evaluate sample attributes and imaging parameters to determine the optimal number of projections. By using feedback from sample characterization and preliminary imaging data, the system adjusts the projection count to achieve sufficient signal-to-noise ratio and minimize artifacts, thereby maintaining measurement precision while optimizing productivity.
Solution Approach 2:
The method dynamically determines the projection image count based on actual sample characteristics rather than using a conservative fixed value. This dynamic adjustment allows the system to acquire fewer projections for simple samples (improving productivity) while ensuring adequate projections for complex samples (maintaining precision).
3Measurement precision
If a fixed number of projection images is used based on worst-case scenarios, then measurement precision is maintained across all sample types, but device complexity increases to account for all variations
Solution Approach 1:
The invention changes the approach from using a fixed worst-case projection count to dynamically adjusting the projection number based on sample attributes (density, geometry, topology, composition) and imaging parameters (beam energy). This parameter adaptation reduces the need for overly complex acquisition systems designed for all possible worst-case scenarios.
Solution Approach 2:
The system performs preliminary evaluation of sample attributes and imaging parameters before finalizing the acquisition plan. By assessing sample characteristics in advance, the system can determine the appropriate number of projections needed, avoiding the need for complex systems that must handle all possible variations with maximum projections.
4Productivity
If an adaptive method is implemented to optimize projection count based on sample attributes, then productivity is improved by reducing unnecessary acquisitions, but device complexity increases due to additional evaluation and control systems
Solution Approach 1:
The system adapts the projection image count parameter based on sample attributes and imaging conditions, allowing productivity improvement through reduced unnecessary acquisitions. The parameter changes are driven by evaluation of sample characteristics rather than requiring complex hardware modifications.
Solution Approach 2:
The acquisition system uses the sample's own attributes (density, geometry, topology, composition) and imaging parameters to determine the optimal number of projections. The system serves itself by using inherent sample characteristics to guide the acquisition process, reducing the need for external complex control mechanisms.
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 enhances sample throughput by reducing unnecessary data acquisition, improving imaging accuracy, and shortening processing time, while adapting to the complexity of the sample structure and its attributes.
Implementation Method 1
acquire a first raw 2D set of 2D images of a sample structure by recording a limited number of 2D image acquisitions at a respectively assigned limited number of raw sample planes
Data Source
AI summary
A method for acquiring a 3D image of a sample structure includes acquiring a first raw 2D set of 2D images of a sample structure at a limited number of raw sample planes; calculating a 3D image of the sample structure represented by a 3D volumetric image data set; and extracting a measurement parameter from the 3D volumetric image data set. A further number of interleaving 2D image acquisitions are recorded at a further number of interleaved sample planes which do not coincide with previous acquisition sample planes. The steps “calculating,”“extracting” and “assigning” are repeated for the further interleaving 2D set until convergence or a maximum number of 2D image acquisitions is recorded. A projection system used for such method comprises a projection light source, a rotatable sample structure holder and a spatially resolving detector. Such method can also be used to acquire virtual tomographic images of a sample.