Void Defect Sizing via Reflected Darkfield Microscopy
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Solution Overview
Problem
Current methods for inspecting semiconductor-on-insulator (SOI) structures fail to accurately detect and classify void-type defects smaller than 500 microns in size, which are crucial for ensuring high product quality in the microelectronics industry, as they do not effectively differentiate the signature of void-type defects from particulates and yield imprecise classification results.
Innovation Solution
A method utilizing reflected darkfield microscopy that generates a roughness-related signal from scattered light, captures intensity data with multiple pixels, compares pixel intensities to identify abnormal zones, extracts standard deviation to determine defect size, and uses a correlation curve to measure void-type defects, allowing precise classification of defects between 5 and 500 microns in size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If reflected darkfield microscopy is used to detect defects on substrate surfaces, then defect visibility and contrast are improved, but measurement precision for void-type defects smaller than 500 microns deteriorates due to low light intensity collected and resolution limits
Solution Approach 1:
The patent segments the collected light signal into two distinct components: a first signal related to defects and a second signal related to roughness. This segmentation allows separate analysis of each signal type, enabling precise measurement of void-type defects by focusing on the defect-related signal while using the roughness signal for normalization and comparison, thereby overcoming the resolution limits of conventional darkfield microscopy
Solution Approach 2:
The patent changes the parameter of light signal analysis by introducing multi-pixel intensity capture and comparing intensity variations across pixels. By analyzing the standard deviation of intensity values across multiple pixels and correlating it with defect size, the system achieves precise defect classification without being limited by the traditional resolution constraints of darkfield microscopy
2Productivity
If conventional defect inspection methods are used, then detection speed is maintained, but measurement precision for void-type defects deteriorates due to inability to differentiate void signatures from particulates
Solution Approach 1:
The patent segments the inspection process into distinct signal analysis streams: one for defect detection and another for roughness characterization. By separating these signals and analyzing them independently, the system can rapidly classify different defect types (voids versus particulates) based on their unique optical signatures, maintaining high inspection speed while achieving accurate defect type differentiation
Solution Approach 2:
The patent implements a feedback mechanism by comparing the measured standard deviation of intensity values against a correlation curve that relates standard deviation to defect size. This feedback loop enables real-time, automated defect classification with high precision, allowing the system to maintain fast inspection speeds while accurately distinguishing between different defect types and sizes
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 detection and classification of void-type defects with a precision of about ±15% for defects larger than 5 microns, improving the quality assessment of SOI structures by accurately determining defect sizes and enhancing quality control in the microelectronics industry.
Implementation Method 1
some of the incident light ray 1 illuminating the surface of the substrate 4 is scattered by the defects in the direction of the collecting channel 5
Implementation Method 2
the incident light ray 1 would be entirely reflected by the surface of the substrate 4 at the same angle β (called the "β" reflected ray and referenced by the reference 1′ in FIG. 1)
Data Source
AI summary
A method for determining the size of a void-type defect in a top side of a structure comprising a top layer placed on a substrate, the defect being located in the top layer, includes introducing the structure into a reflected darkfield microscopy device in order to generate, from a light ray scattered by the top side, a defect-related first signal and a roughness-related second signal. The intensity of the roughness-related second signal is captured with a plurality of pixels. The intensity captured by each pixel is compared with the intensities captured by neighboring pixels. It is defined whether or not the pixel is contained in an abnormal zone. The standard deviation of the intensity values captured by the pixels of the abnormal zone is extracted, and the size of the void-type defect associated with the abnormal zone is determined from the extracted standard deviation. A new device may be used for carrying out such a method.