Binary Parsing for Multi-Wafer Metrology Data Analysis
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Solution Overview
Problem
State-of-the-art semiconductor manufacturing systems face inefficiencies in analyzing continuous data from multiple wafers to identify common problem areas, as standard deviation analysis generates analog, continuous data that is difficult to quantify and represent efficiently for multi-wafer data analysis.
Innovation Solution
A method is developed to generate binary mappings of wafer regions by acquiring measured values, defining common regions, and assigning binary values based on cumulative results above or below predetermined thresholds, enabling efficient pattern recognition and process adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard deviation analysis is used to analyze continuous metrology data from multiple wafers, then comprehensive data analysis is achieved, but the data becomes difficult to quantify and represent efficiently
Solution Approach 1:
The patent transforms continuous metrology data into binary data by changing the parameter representation from analog values to discrete binary states (0 or 1). This is achieved by comparing continuous measurements against threshold values, converting them into binary indicators that represent whether a measurement exceeds a specified limit. This parameter transformation simplifies data representation while preserving the essential information needed for multi-wafer analysis.
Solution Approach 2:
The patent extracts the critical information from continuous data by taking out only the essential characteristic - whether a measurement exceeds a threshold - and discarding the continuous variation. This extraction process converts complex continuous data into simplified binary form, making it easier to analyze across multiple wafers while retaining the key quality indicators.
2Loss of information
If continuous data from multiple wafers is analyzed using standard methods, then complete measurement information is retained, but identification of common problem areas becomes inefficient
Solution Approach 1:
By transforming continuous measurement data into binary form through threshold comparison, the patent enables more efficient computational processing. The binary representation allows for faster aggregation and analysis across multiple wafers, significantly improving productivity in identifying common problem areas while maintaining the essential measurement information through the binary encoding of exceedance conditions.
Solution Approach 2:
The patent segments the continuous data range into distinct binary categories (below threshold = 0, above threshold = 1), creating discrete segments that are easier to process and analyze. This segmentation approach divides the complex continuous data space into manageable binary units that can be efficiently aggregated and analyzed across multiple wafers to identify patterns and common problems.
3Loss of information
If analog-style continuous data is used for multi-wafer analysis, then detailed measurement characteristics are preserved, but quantification and common pattern identification become difficult
Solution Approach 1:
The patent applies parameter transformation by converting continuous measurement characteristics into binary indicators through threshold-based classification. This change preserves the essential measurement information in terms of whether specifications are met or exceeded, while making the data much easier to quantify and analyze for common patterns across multiple wafers. The binary representation enables straightforward statistical analysis and pattern recognition.
Solution Approach 2:
The patent introduces binary data as an intermediary representation between the original continuous measurements and the final analysis results. This intermediary binary form serves as a bridge that maintains the essential measurement information while enabling efficient computation and pattern identification. The binary data acts as a mediator that translates complex continuous variations into actionable quality indicators.
Data Source
AI summary
A method, apparatus, and a system for generating a binary mapping of wafer regions using measured value. A first measured value relating to processing a first workpiece is acquired. A second measured value relating to a second workpiece is acquired. At least a first region common to the first and second workpieces is defined. A determination is made as to whether the results associated with the first or second measured value is above a predetermined threshold. A first binary value is assigned to the first region based upon a determination that the results associated the first or second measured value data is above the threshold.