Semiconductor Measurement Imaging for Orthogonal Critical-Dimension Extraction
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Solution Overview
Problem
Existing semiconductor measurement apparatuses struggle to accurately determine critical dimensions of miniaturized structures due to interference from multiple dimensions, leading to inaccuracies in spectrum distribution and reduced measurement efficiency.
Innovation Solution
A semiconductor measurement apparatus that uses orthogonal decomposition of images to isolate critical dimensions by selecting weights sensitive to the desired dimension, allowing for accurate determination through a single image capture with wide azimuth and incident angle data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to measure critical dimensions of miniaturized structures, then measurement coverage is comprehensive, but measurement accuracy deteriorates due to interference from multiple dimensions
Solution Approach 1:
The patent extracts and isolates the critical dimension information from the complex multi-dimensional measurement data by applying orthogonal decomposition. This separates the desired measurement parameter (critical dimension) from interfering factors (other dimensional variations), thereby improving measurement accuracy while managing data complexity through selective extraction of relevant information.
Solution Approach 2:
The patent transforms the measurement approach by changing parameters through orthogonal decomposition, converting complex multi-dimensional data into weighted one-dimensional data that highlights the critical dimension. This parameter transformation enables accurate measurement by emphasizing the relevant dimension while suppressing interference from other dimensions.
2Productivity
If multiple measurement dimensions are captured simultaneously, then comprehensive data is acquired, but measurement efficiency decreases due to processing complexity
Solution Approach 1:
The patent extracts only the essential critical dimension information from the comprehensive multi-dimensional data through orthogonal decomposition. By taking out only the relevant measurement parameter and its associated weights, the system reduces processing complexity while maintaining measurement comprehensiveness, thereby improving efficiency.
Solution Approach 2:
Instead of processing all multi-dimensional data directly to extract critical dimensions, the patent inverts the approach by first establishing orthogonal basis functions that represent the critical dimension, then projecting the measurement data onto these bases. This inverted approach simplifies processing by working with pre-defined orthogonal bases rather than attempting to separate dimensions from raw data.
3Measurement precision
If orthogonal decomposition is applied to isolate critical dimensions, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial orthogonal decomposition by selecting only the necessary number of orthogonal bases required to accurately represent the critical dimension, rather than performing complete decomposition of all possible dimensions. This partial action maintains measurement accuracy while reducing computational complexity by avoiding unnecessary decomposition steps.
Solution Approach 2:
The patent manages computational complexity through parameter changes by transforming the decomposition results into weighted one-dimensional data that can be processed more efficiently. This parameter transformation converts complex multi-dimensional decomposition results into a simplified format that maintains accuracy while reducing subsequent computational burden.
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
Enables precise measurement of critical dimensions by minimizing interference from other dimensions, improving measurement accuracy and process yield in semiconductor manufacturing.
Implementation Method 1
an illumination unit including a light source
Implementation Method 2
a polarizer disposed on a propagation path of light emitted from the light source
Implementation Method 3
an optical unit configured to incident the light passing through the polarizer onto a sample, and transmit the light, reflected from the sample, to an image sensor
Implementation Method 4
generate an image representing an interference pattern of the light
Data Source
AI summary
A semiconductor measurement apparatus includes an illumination unit including a light source, and a polarizer disposed on a propagation path of light emitted from the light source; an optical unit configured to direct the light passing through the polarizer to be incident onto a sample, and to transmit the light, reflected from the sample, to an image sensor; and a controller configured to process an original image, output by the image sensor, to determine a critical dimension of a structure included in a region of the sample on which the light is incident. The controller acquires a two-dimensional image. The controller orthogonally decomposes the two-dimensional image corresponding to a selected wavelength into a plurality of bases, generates one-dimensional data including a plurality of weights corresponding to the plurality of bases, and uses the one-dimensional data to determine a selected critical dimension among critical dimensions of the structure.