Dynamic Imaging Sharpness via Object Density Mapping
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Solution Overview
Problem
Imaging modalities, such as SEM, face challenges in achieving high-resolution images efficiently due to equal focus on all objects regardless of size and shape, leading to prolonged imaging times, especially in applications like reverse engineering of ICs where all structures need precise extraction without prior knowledge.
Innovation Solution
The method involves generating a frequency map representing object density in various regions of a sample, allowing for dynamic adjustment of magnification settings based on object density, with higher magnification applied only to dense regions, reducing overall imaging time and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If equal focus is given to all objects in the image, then all regions are imaged with the same sharpness, but imaging time increases significantly
Solution Approach 1:
The patent applies local quality by differentiating imaging parameters based on spatial location and object density. Regions with high object density receive higher magnification and sharper focus, while low-density regions use lower magnification. This is achieved through computing object density maps and using them to dynamically adjust imaging parameters, thereby resolving the contradiction between uniform sharpness and imaging time.
Solution Approach 2:
The patent implements dynamics by making imaging parameters adaptive rather than fixed. The system dynamically adjusts magnification and focus settings based on real-time analysis of object density in different regions. This dynamic adaptation allows the system to optimize between sharpness and imaging time for each region according to its specific characteristics.
2Measurement precision
If higher magnification is applied to all regions, then image detail is improved, but imaging time increases
Solution Approach 1:
The patent applies local quality by differentiating imaging parameters based on spatial location and object density. Regions with high object density receive higher magnification and sharper focus, while low-density regions use lower magnification. This is achieved through computing object density maps and using them to dynamically adjust imaging parameters, thereby resolving the contradiction between uniform sharpness and imaging time.
Solution Approach 2:
The patent applies partial action by applying high magnification only to the extent necessary for regions with high object density, rather than uniformly across the entire image. This selective application of high magnification to only those regions where it is needed maintains image detail where critical while reducing overall imaging time.
3Productivity
If compressed sensing algorithms are used, then imaging speed is improved, but memory requirements and processing complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple regions based on object density and processing each region with appropriate imaging parameters. This segmentation approach reduces the overall processing complexity compared to applying complex compressed sensing algorithms to the entire image, while still achieving improved imaging speed through targeted high-resolution capture only where necessary.
Data Source
AI summary
Systems and methods are configured to generate a frequency map representing a density of objects found in regions of a sample that may be used in setting parameters for imaging the regions. Various embodiments involve binarizing the pixels for a raw image of the sample to transform the image into binary data. Run-length encoded components are identified from the data for dimensions of the raw image. Each component is a length of a sequence of adjacent pixels found in a dimension with the same value in the binary data. A projection of the image is then generated from projection values for the dimensions. Each projection value provides a measure of the density of objects present in a dimension with respect to the components identified for the dimension. This projection is used to identify a level of density for each region of the sample from which the frequency map is generated.