Semiconductor Hot Spot Detection Using Dynamic Threshold Mapping
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Solution Overview
Problem
Existing optical inspection tools for semiconductor manufacturing face challenges in detecting smaller defects due to sensitivity limitations from wafer noise, leading to gaps in defect detection as defect sizes decrease.
Innovation Solution
A method and system for detecting hot spot defects in semiconductor products by extracting and grouping local patterns into hot spot groups, using a dynamic mapping mechanism to align defect images with a hot spot map, and applying automatic thresholding and machine learning techniques to enhance sensitivity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing optical inspection tools are used to detect defects, then the inspection process can be performed, but the sensitivity is limited by wafer noise making it impossible to detect smaller defects
Solution Approach 1:
The patent segments the wafer surface into multiple regions of interest (ROIs) based on design data, allowing focused inspection on specific areas where defects are most likely to occur. This segmentation enables the system to concentrate detection resources on critical areas rather than uniformly inspecting the entire wafer, thereby improving sensitivity for small defects while managing noise interference.
Solution Approach 2:
The patent performs preliminary actions by pre-processing defect images through alignment with design data, extraction of ROIs, and generation of reference images before actual defect detection. This preliminary processing establishes a baseline and reduces noise interference in advance, enabling more sensitive detection of small defects during the actual inspection process.
2Reliability
If the inspection covers the entire wafer surface uniformly, then comprehensive coverage is achieved, but the detection sensitivity for small defects is reduced due to noise from non-critical areas
Solution Approach 1:
The patent applies local quality by assigning different inspection priorities and parameters to different regions of the wafer based on their criticality. Critical areas receive enhanced inspection with higher sensitivity settings and more processing steps, while non-critical areas receive standard or reduced inspection. This localized approach improves overall detection reliability for small defects in critical regions without unnecessarily increasing complexity across the entire wafer.
Solution Approach 2:
The patent introduces dynamics by adaptively adjusting inspection parameters such as ROI selection, reference image generation, and detection thresholds based on the specific characteristics of each inspection target and defect type. This dynamic adjustment allows the system to optimize detection reliability for small defects in different regions while managing complexity through automated parameter selection rather than fixed uniform inspection.
3Manufacturing precision
If traditional optical inspection methods are used, then the process is simple, but the ability to detect smallest defects of interest is insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer between the optical inspection tool and the defect detection algorithm. This intermediary layer includes image alignment with design data, extraction of regions of interest, generation of reference images, and noise filtering. These intermediary steps enhance manufacturing precision for detecting small defects by preparing and conditioning the images before analysis, while the automated nature of these intermediaries manages the complexity burden.
Solution Approach 2:
The patent replaces traditional mechanical/optical inspection approaches with a more sophisticated system that integrates design data processing, image alignment algorithms, and automated ROI extraction. This substitution enables higher manufacturing precision for small defect detection by using computational methods rather than purely optical/mechanical means, while the automation of these processes helps manage the increased complexity.
Data Source
AI summary
A hot spot defect detecting method and a hot spot defect detecting system are provided. In the method, hot spots are extracted from a design of a semiconductor product to define a hot spot map comprising hot spot groups, wherein local patterns in a same context of the design yielding a same image content are defined as a same hot spot group. During runtime, defect images obtained by an inspection tool performing hot scans on a wafer manufactured with the design are acquired and the hot spot map is aligned to each defect image to locate the hot spot groups. The hot spot defects in each defect image are detected by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.