Localized Substrate Geometry Characterization via Feature-Specific Filters
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Solution Overview
Problem
Current substrate characterization methods fail to accurately quantify higher order shape components and localized features, which are critical for modern integrated circuit fabrication, especially at smaller node sizes, due to the lack of suitable metrics that can impact lithography and other processing steps.
Innovation Solution
An optical substrate measurement system that analyzes surface data using feature-specific filters to produce surface-specific metrics, including range, deviation, and root mean square metrics, allowing for the characterization and quantification of higher order shape components and localized features without referencing the back surface, enabling enhanced substrate classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional substrate characterization methods are used, then global shape metrics (warp and bow) can be obtained, but higher order shape components and localized features cannot be accurately quantified
Solution Approach 1:
The substrate surface is divided into multiple evaluation areas, with each area characterized by its own reference plane and metrics. This segmentation allows localized higher order shape components to be quantified independently without being averaged out by global measurements, resolving the contradiction between measurement precision for local features and system complexity.
Solution Approach 2:
Different metrics are applied to different evaluation areas based on their specific characteristics. Each area has its own reference plane fitted to local data points, enabling tailored characterization of localized features such as notches, laser marks, and epitaxial pins. This local quality approach achieves precise quantification of higher order shape components without requiring a completely new complex system.
2Reliability
If traditional flatness characterization excluding localized regions is used, then global flatness metrics can be obtained, but the impact of localized features on lithography and processing cannot be assessed
Solution Approach 1:
Reference planes are fitted to evaluation areas in advance of the actual characterization process. This preliminary action establishes a local reference framework that accounts for localized geometry features before they are used to assess substrate suitability for lithography, preventing loss of information about these critical features.
Solution Approach 2:
Local reference planes act as intermediaries between the raw surface data and the final assessment of substrate suitability. These intermediaries capture the impact of localized features such as notches and laser marks on lithography and processing, translating complex localized geometry into meaningful metrics without losing critical information.
3Productivity
If substrate areas with uncontrolled geometry features are excluded from characterization, then traditional metrics can be maintained, but substrate utilization for salable integrated circuits is reduced
Solution Approach 1:
The characterization approach changes from excluding areas with uncontrolled geometry features to including them with locally fitted reference planes. This parameter change in the measurement methodology enables these areas to be evaluated and utilized for salable integrated circuits while maintaining the necessary geometry control through localized metric application.
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
This approach enables the effective characterization and sorting of substrates based on higher order shape and localized features, improving substrate suitability for integrated circuit fabrication by providing objective, quantitative metrics that were previously unattainable, thus optimizing substrate utilization and process compatibility.
Implementation Method 1
an optical substrate measurement system
Implementation Method 2
optical substrate measurement system
Data Source
AI summary
A system for evaluating the metrological characteristics of a surface of a substrate, the system including an optical substrate measurement system, a data analyzing system for analyzing data in an evaluation area on the substrate, applying feature-specific filters to characterize the surface of the substrate, and produce surface-specific metrics for characterizing and quantifying a feature of interest, the surface-specific metrics including a range metric for quantifying maximum and minimum deviations in the evaluation area, a deviation metric for quantifying a point deviation having a largest magnitude in a set of point deviations, where the point deviations are an amount of deviation from a reference plane fit to the evaluation area, and a root mean square metric calculated from power spectral density.