Scatterometry Waveform Library Automation via Process Parameter Simulation
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Solution Overview
Problem
Current pattern measurement techniques, such as scatterometry, require significant effort and resources to create waveform libraries for nondestructive measurement of complex micropatterns, and are not robust enough to handle unexpected pattern variations, leading to inaccurate measurements.
Innovation Solution
A pattern measurement apparatus and method that creates a waveform library by acquiring and simulating sectional shapes corresponding to various process parameters, calculating predicted spectral waveforms, and matching these with actual spectral waveforms to determine optimum process parameters for accurate pattern sectional shape measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If scatterometry is used for nondestructive pattern measurement, then measurement destructiveness is reduced, but waveform library creation burden increases
Solution Approach 1:
The patent changes the basis of waveform library creation from shape parameters (requiring manual modeling and expert knowledge) to process parameters (automatically obtainable from manufacturing data). This parameter transformation automates library creation and reduces the burden while maintaining measurement accuracy.
Solution Approach 2:
The patent creates waveform libraries by simulating and copying the spectral waveforms that would result from various process parameters, rather than requiring manual construction of shape models. This copying approach through simulation significantly reduces the complexity of library creation.
2Measurement precision
If comprehensive waveform library is created to improve measurement accuracy, then measurement precision improves, but creation cost increases
Solution Approach 1:
By switching from shape-based to process-based parameterization, the patent enables automated generation of comprehensive waveform libraries covering process variations without requiring expert manual intervention for each case, thereby reducing creation costs while maintaining precision.
Solution Approach 2:
The patent performs preliminary simulation of spectral waveforms for various process parameters to pre-construct a comprehensive waveform library that covers expected process variations. This preliminary action ensures measurement precision is maintained while costs are controlled through automation.
3Reliability
If waveform library covers all possible pattern shapes, then measurement robustness improves, but library creation complexity increases
Solution Approach 1:
The patent transforms the library creation approach from manually modeling various shape scenarios to automatically simulating waveforms based on process parameters. This enables comprehensive coverage of process variations for improved robustness without proportionally increasing creation complexity.
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 reduces the burden of creating waveform libraries by focusing on process parameters rather than shape parameters, enabling more efficient and accurate measurements of complex patterns with improved robustness and reduced costs.
Implementation Method 1
scatterometry using light has been recently becoming more popular. The technique of the scatterometry is as follows: Light is applied to a measurement target pattern, and the spectral waveform of the reflected light is used to estimate a corresponding pattern sectional shape and an index of refraction
Data Source
AI summary
A pattern measurement method includes: acquiring sectional shapes of a first pattern corresponding to process parameters, respectively; using the acquired sectional shapes to calculate predicted spectral waveforms which would be obtained when light is applied to the first pattern, and adding information on the corresponding process parameters to the calculated predicted spectral waveforms, respectively, to form a waveform library; setting a process parameter to obtain a desired shape, and acquiring an actual spectral waveform of a second pattern actually created from the first pattern using the set process parameter; performing waveform matching between the actual spectral waveform and the predicted spectral waveforms to acquire matching scores for respective waveform matching, and calculating an optimum process parameter providing the maximum matching score; generating an optimum pattern sectional shape corresponding to the optimum process parameter to measure the optimum pattern sectional shape.