Scatterometry Library Construction via Priority-Based Profile Fitting
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Solution Overview
Problem
The existing scatterometry techniques for measuring complex micropatterns in semiconductor manufacturing require significant cost and expertise to construct accurate libraries, making them inefficient and labor-intensive for precise sectional shape evaluation.
Innovation Solution
A measurement apparatus comprising a library creation unit, spectral profile acquiring unit, and measurement unit that creates a layer stack model by calculating feature values from reflected light intensity, determining a priority order of analysis, and fitting theoretical profiles to measured profiles, allowing for accurate sectional shape measurement without requiring skilled labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scatterometry is used to measure complex micropatterns, then measurement precision is improved, but device complexity and cost increase due to the need for skilled labor and time-consuming library construction
Solution Approach 1:
The system performs self-service by automatically constructing the measurement library using the measurement apparatus itself. The apparatus measures reference patterns, extracts spectral profiles, and generates the library without requiring external skilled labor or manual intervention, thereby reducing complexity while maintaining precision
Solution Approach 2:
The system creates a digital copy of the measurement library that can be stored and reused. By copying the spectral profile data from reference patterns into a reusable library database, the system eliminates the need to reconstruct the library for each measurement, reducing both complexity and time requirements
2Measurement precision
If scatterometry library construction is performed manually by skilled engineers, then measurement precision is improved, but productivity decreases due to time consumption and labor costs
Solution Approach 1:
The measurement apparatus performs library construction autonomously by automatically measuring reference patterns, extracting spectral characteristics, and generating the library database. This self-service capability eliminates manual labor while maintaining measurement precision, significantly improving productivity
Solution Approach 2:
The system performs preliminary library construction using reference patterns before actual measurements. By pre-measuring and pre-processing reference data to create the library, the system prepares all necessary measurement parameters in advance, enabling rapid and accurate measurement of subsequent samples without repeating the library construction process
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
Enables accurate and efficient measurement of complex micropatterns by constructing an optimum library, reducing the need for skilled engineers and minimizing costs, while improving measurement precision and speed.
Implementation Method 1
light is applied to a measurement target pattern, and a pattern sectional shape corresponding to the spectral profile of the reflected light is estimated
Data Source
AI summary
In accordance with an embodiment, a measurement apparatus includes a library creation unit, a spectral profile acquiring unit, and a measurement unit. The library creation unit creates a library in which a layer stack model is matched to a theoretical profile regarding a pattern of stacked layers. The spectral profile acquiring unit acquires an actual measured profile by applying light to a measurement target pattern obtained when the pattern is actually created. The measurement unit measures the sectional shape of the measurement target pattern by performing fitting of the theoretical profile to the actual measured profile. The layer stack model is created by calculating a feature value that reflects the intensity of reflected light from an interface for each of the layers, determining a priority order of analysis from the feature value, and sequentially performing fitting of the theoretical profile to the measured profile in the determined priority order.