Semiconductor Defect Detection via Spatial Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semiconductor fabrication processes face challenges in accurately detecting defects and edge roughness in ultra-large scale integration devices due to the need for high precision and uniformity, which is not adequately addressed by existing automated examination methods.
Innovation Solution
A system comprising a processor and memory circuitry that obtains segmented and reference images of semiconductor specimens, determines spatial transformations to match structural elements, and identifies defects and edge roughness using data informative of spatial transformations and pixel distances, enabling efficient defect detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated examination methods are used to detect defects in semiconductor specimens, then productivity is improved, but measurement precision deteriorates due to difficulty in accurately detecting defects and edge roughness
Solution Approach 1:
The system performs preliminary spatial transformation on the reference image to align it with the segmented image before comparison. This preliminary alignment action ensures that subsequent defect detection operates on properly registered images, resolving the contradiction by preparing the data structure in advance for accurate automated measurement.
Solution Approach 2:
The patent introduces an intermediary spatial transformation process that acts as a mediator between the reference image and segmented image. This intermediary step computes transformation parameters and applies corrections, enabling the automated system to achieve precision comparable to manual examination while maintaining high productivity.
2Measurement precision
If spatial transformation is applied to match structural elements, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the examination process into distinct modules: image acquisition, spatial transformation computation, transformation application, and defect detection. Each module handles a specific task independently, which reduces overall system complexity while maintaining high measurement precision through coordinated operation of specialized components.
Solution Approach 2:
The system creates a transformed copy of the reference image that aligns with the segmented image. This copying approach allows the original reference data to be preserved while generating a matched version for comparison, reducing complexity by avoiding direct modification of source data and enabling reusable reference libraries.
3Measurement precision
If detailed analysis of pixel distances is performed to detect defects, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies local quality analysis by focusing detailed pixel distance measurements only on regions where spatial transformation indicates potential discrepancies. Rather than uniformly analyzing all pixels, the system concentrates computational resources on local areas requiring verification, thereby maintaining high detection precision while reducing overall examination time.
Solution Approach 2:
The system performs partial action by computing pixel distances selectively based on transformation parameters. Instead of exhaustive comparison of all image regions, the method applies distance analysis only where needed according to the spatial transformation results, achieving sufficient defect detection precision with reduced computational time.
Data Source
AI summary
There are provided systems and methods of obtaining a segmented image of a semiconductor specimen, the image comprising first structural elements, obtaining a reference image of the semiconductor specimen, the reference image being based on design data and comprising second structural elements, determining, for at least one pair of elements including a first structural element and a corresponding second structural element, data Dspat informative of a spatial transformation required in order to match the elements of the pair in accordance with a matching criterion, and determining at least one of data informative of a defect in the first structural element and data informative of edge roughness of the first structural element using at least Dspat.