Lithographic Inspection via Diffraction Pupil Image Classification
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Solution Overview
Problem
Current lithographic inspection methods require prior knowledge of structure information and optical properties, and are computationally intensive, making them inefficient for quickly determining process parameters like focus and dose during the lithographic process.
Innovation Solution
Acquiring diffraction pupil images of structures formed on a substrate with varying process variables and determining a discriminant function to classify these images based on the process variables, allowing for classification without prior knowledge or intensive reconstruction steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scatterometry methods are used to measure substrate properties, then measurement capability is achieved, but computational intensity and setup time increase significantly
Solution Approach 1:
The patent pre-calculates and stores diffraction pupil images for various known substrate properties in a library before actual measurement. This preliminary action eliminates the need for computationally intensive real-time reconstruction during measurement, significantly reducing setup and computational time while maintaining measurement precision.
Solution Approach 2:
The patent creates a library of pre-computed diffraction pupil images that serve as templates or copies of expected measurement results. Instead of performing complex reconstructions on actual measurements, the system compares measured images against these pre-existing copies to rapidly determine substrate properties.
2Loss of information
If traditional scatterometry with library usage is employed, then parameter estimation is possible, but prior knowledge of structure information and optical properties is required
Solution Approach 1:
The patent varies process parameters (such as focus and dose) during structure formation to create structures with different properties. By measuring diffraction pupil images from these varied structures and comparing them against a library generated from the same parameter variations, the system can estimate parameters without requiring prior knowledge of the actual structure or optical properties.
Solution Approach 2:
The patent pre-generates a comprehensive library of diffraction pupil images covering a range of process parameter variations. This preliminary library creation includes all possible structure variations that may be encountered, eliminating the need for prior knowledge during actual measurement as the correct match will always exist in the pre-prepared library.
3Measurement precision
If computationally intensive reconstruction steps are performed, then accurate parameter estimation is achieved, but productivity decreases
Solution Approach 1:
The patent replaces computationally intensive reconstruction algorithms with a simple image matching process against pre-computed library copies. This substitution maintains measurement precision by ensuring accurate parameter estimation through library matching, while dramatically improving productivity by eliminating real-time computational reconstruction.
Solution Approach 2:
The patent performs all computationally intensive work in advance by pre-calculating the entire library of diffraction pupil images. This shifts the computational burden from the measurement phase to the preparation phase, allowing rapid inspections to be performed by simply comparing measured images against the pre-existing library without requiring real-time computation.
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 direct observation of focus disturbances and other parameter variations without requiring previous knowledge of product stack information or optical properties, reducing computational intensity and setup time, and improving yield monitoring.
Implementation Method 1
a beam of radiation is directed onto a target on the surface of the substrate and properties of the scattered or reflected beam are measured
Implementation Method 2
properties of the scattered or reflected beam are measured
Implementation Method 3
acquiring diffraction pupil images of a plurality of structures formed on a substrate during a lithographic process
Data Source
AI summary
An inspection method, and corresponding apparatus, enables classification of pupil images according to a process variable. The method comprises acquiring diffraction pupil images of a plurality of structures formed on a substrate during a lithographic process. A process variable of the lithographic process varies between formation of the structures, the variation of the process variable resulting in a variation in the diffraction pupil images. The method further comprises determining at least one discriminant function for the diffraction pupil images, the discriminant function being able to classify the pupil images in terms of the process variable.