Image Hashing for Wafer Pattern Synonym Grouping
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Solution Overview
Problem
Current defect detection methods for semiconductor wafers, such as pattern synonym detection, are tedious, manual, time-consuming, and impractical, especially as dimensions shrink, leading to inefficient production schedules and unmanageable groupings that hinder root cause analysis.
Innovation Solution
An image hashing-based unsupervised pattern synonym detection system that groups similar patterns together using fixed length hash strings, reducing the number of design bins and enabling quick, unsupervised pattern synonym detection across wafers, layers, and devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pattern synonym detection methods are used, then detection accuracy can be achieved, but the process is tedious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system that captures wafer images and uses image processing algorithms to automatically detect pattern synonyms. This substitution eliminates the time-consuming manual process while maintaining detection accuracy through systematic image analysis.
Solution Approach 2:
The patent creates hash representations (copies) of wafer images that capture essential pattern features. By working with these compressed hash copies instead of full-resolution images, the system enables rapid comparison and detection of pattern synonyms without analyzing complete image data, significantly reducing processing time.
2Reliability
If OPC rule-based search is used to detect pattern synonyms, then detection capability is improved, but the process becomes slow and cannot be practically implemented for all patterns
Solution Approach 1:
The patent transforms wafer images into fixed-length hash representations, fundamentally changing the data parameter from high-dimensional image data to compact numerical vectors. This parameter transformation enables rapid computation and comparison, allowing the system to process all patterns at high speed while maintaining reliable detection capability through the preserved essential features in the hash representation.
3Productivity
If design-based grouping algorithm is used, then processing speed is improved, but it divides the dataset into an unmanageable number of groups
Solution Approach 1:
The patent performs preliminary clustering by grouping wafer images based on their hash representations before conducting detailed pattern synonym detection. This preliminary action organizes the dataset into manageable clusters that share similar characteristics, reducing the overall complexity and making subsequent analysis more tractable while maintaining processing speed.
4Loss of time
If exact search algorithm is used, then processing time is reduced, but designs with similar root cause are split into multiple groups
Solution Approach 1:
The patent applies local quality by focusing detection on specific critical regions and features within wafer images rather than treating all areas uniformly. The hash representation captures essential local features that are most relevant to pattern synonym detection, enabling fast processing while preserving the ability to identify designs with similar root causes through targeted feature analysis.
Data Source
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AI summary
Images of semiconductor wafers can be hashed to determine a fixed length hash string for each of the images. Pattern synonyms can be determined from the hash strings. The pattern synonyms can be grouped. A degree of similarity between images in the groups is adjustable via a hamming distance. This can be used for various applications, including determination of latent defects.