Intelligent CAA Failure Pre-diagnosis for IC Layout
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Solution Overview
Problem
Current methods fail to accurately and rapidly predict systematic and random defects in the manufacturing process of integrated circuits, leading to reduced yield and increased learning time for mass production, as they struggle to identify killer defects amidst minor defects detected by sensitive scanning devices within limited manufacturing time.
Innovation Solution
An intelligent Critical Area Analysis (CAA) failure pre-diagnosis system and method that defines layout regions, obtains defect information, analyzes defect patterns for killer defect indices, and judges failure risk levels, enabling timely identification of high, medium, low, or no failure risk areas before manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensitive scanning and detecting devices are employed to capture all killer defects on the wafer, then the number of defects detected increases, but the percentage of minor defects increases while the total number of defects increases, making it harder to identify potential defects within limited manufacturing time
Solution Approach 1:
The system performs preliminary critical area analysis on the design layout before manufacturing to identify regions with high defect susceptibility. By pre-calculating killer defect patterns and their potential impact areas, the system prepares a priority map that guides subsequent defect inspection, allowing manufacturers to focus limited time on the most critical regions rather than examining the entire wafer uniformly.
Solution Approach 2:
The system applies different inspection strategies to different regions of the wafer based on their critical area characteristics. Layout regions with high killer defect indices receive intensified inspection resources and attention, while low-risk regions receive reduced inspection. This localized quality approach optimizes the allocation of manufacturing time according to actual defect risk distribution.
2Measurement precision
If the number of defects detected increases to capture all killer defects, then defect detection coverage improves, but the ability to identify important and killer defects among minor defects deteriorates
Solution Approach 1:
The system extracts and isolates killer defect patterns from the overall defect dataset by comparing detected defects against pre-calculated critical area profiles. By separating defects that match killer patterns from minor defects, the system presents only the most critical defects to manufacturers, preventing information overload and ensuring that important defects are not lost among numerous minor defects.
Solution Approach 2:
The critical area analysis system acts as an intermediary between defect detection and defect evaluation. It processes the raw defect data through a filtering mechanism that uses pre-computed killer defect indices to rank and prioritize defects, transforming the undifferentiated defect list into a prioritized sequence that highlights killer defects first.
3Measurement precision
If manual inspection and categorization of detected defects is performed by scanning electron microscope, then defect classification accuracy improves, but the manufacturing time increases and delivery time requirements cannot be met
Solution Approach 1:
The system replaces manual mechanical inspection by scanning electron microscope with an automated computational analysis system. By using computer-based critical area analysis algorithms to process defect images and patterns, the system achieves high-speed automated classification that maintains accuracy while dramatically increasing throughput and reducing manufacturing time.
Solution Approach 2:
The system creates a virtual model of the design layout with embedded critical area information that serves as a reference for automated defect evaluation. Instead of manually comparing each defect against design specifications, the system uses pre-computed critical area maps as a digital template to automatically classify defects, enabling high-speed processing without sacrificing classification accuracy.
4Manufacturing precision
If design layout characteristic size becomes smaller to improve manufacturing precision, then manufacturing precision improves, but the susceptibility to defects increases and yield decreases
Solution Approach 1:
The system performs preliminary critical area analysis during the design phase to identify layout configurations that are particularly susceptible to defects at small feature sizes. By detecting and flagging high-risk design patterns before manufacturing, the system enables designers to modify layouts to improve robustness, thereby maintaining manufacturing precision while protecting against yield loss from defect susceptibility.
Solution Approach 2:
The system analyzes how different layout parameters (spacing, density, pattern complexity) affect critical area and defect susceptibility. By providing feedback on optimal parameter ranges that balance manufacturing precision with defect resistance, the system guides design adjustments that maintain small feature sizes for precision while optimizing other parameters to reduce overall defect impact and protect yield.
Data Source
AI summary
The instant disclosure provides an intelligent CAA (Critical Area Analysis) failure pre-diagnosis system and method for a design layout. The intelligent CAA failure pre-diagnosis method includes the steps of obtaining a design layout of an object and defining at least one layout region having a layout pattern thereon, obtaining a plurality of defects, comparing the defects one-by-one to a predetermined portion of the layout pattern in the order of their sizes, and calculating a CAA failure risk level of the layout region according to the comparison result.