Substrate Inspection Recipe Selection for Defect Detection
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Solution Overview
Problem
The accuracy of macro defect inspection in photolithography processes varies due to differences in the surface state of semiconductor wafers, as different films are formed on the wafer surface, affecting reflectance and other surface properties.
Innovation Solution
A substrate inspection method that images the substrate before treatment, extracts feature amounts, selects an appropriate inspection recipe from a storage unit, and uses this recipe to determine defects in the treated substrate, ensuring consistent inspection accuracy regardless of surface state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed inspection recipe is used for all substrates, then the inspection process is simple and fast, but the inspection accuracy varies due to different surface states of substrates
Solution Approach 1:
The system performs preliminary imaging of the substrate surface before the actual defect inspection to extract feature amounts (such as reflectance characteristics). Based on these extracted features, the system pre-selects the appropriate inspection recipe from multiple available recipes. This preliminary characterization of the substrate surface state enables subsequent high-accuracy inspection by matching the inspection parameters to the specific substrate conditions, thereby resolving the contradiction between maintaining simple processes and achieving consistent accuracy across varied substrate surfaces.
Solution Approach 2:
The system stores multiple inspection recipes with different parameters (illumination conditions, imaging parameters, evaluation criteria) in a storage unit. Each recipe is optimized for specific substrate surface states. By changing the inspection parameters dynamically based on the extracted feature amounts from preliminary imaging, the system adapts to different substrate conditions (such as different film types, thicknesses, or surface treatments), ensuring consistent inspection accuracy without requiring a complex manual adjustment process for each substrate type.
2Reliability
If multiple inspection recipes are stored and selected based on feature amounts, then inspection accuracy is maintained across different surface states, but the system complexity increases
Solution Approach 1:
The system performs self-characterization by automatically imaging the substrate surface and extracting feature amounts (such as reflectance values) without requiring external input or manual measurement. Based on this self-acquired information, the system autonomously selects the appropriate inspection recipe from its stored library. This self-service capability ensures consistent and reliable inspection results across different substrate types while avoiding the complexity of manual recipe selection or external measurement equipment, as the system uses its own imaging resources to gather necessary characterization data.
3Productivity
If inspection is performed without considering substrate surface state, then the process is fast and simple, but the defect detection accuracy varies significantly
Solution Approach 1:
The system performs a quick preliminary imaging step to extract key feature amounts (such as overall reflectance characteristics) from the substrate surface. This preliminary action provides sufficient information to select the appropriate inspection recipe without requiring time-consuming manual analysis. The automated extraction and matching process maintains high inspection throughput while ensuring that the subsequent defect detection uses optimized parameters tailored to the specific substrate surface state, thereby preserving both productivity and measurement precision.
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 allows for optimal defect inspection by selecting the correct inspection recipe based on the substrate's feature amounts, thereby minimizing variation in inspection accuracy and ensuring consistent results across different surface states.
Implementation Method 1
the surface state such as reflectance and so on of the wafer surface is different in each process
Data Source
AI summary
A substrate inspection method in a substrate treatment system including a plurality of treatment apparatuses each performing a predetermined treatment on a substrate, includes: imaging a surface of a substrate before being treated in the treatment apparatuses to acquire a first substrate image; extracting a predetermined feature amount from the first substrate image; selecting an inspection recipe corresponding to the feature amount extracted from the first substrate image, from a storage unit in which a plurality of inspection recipes each set corresponding to the feature amount in a different range are stored; imaging the surface of the substrate after being treated in the treatment apparatuses to acquire a second substrate image; and determining presence or absence of a defect of the substrate, based on the selected inspection recipe and the second substrate image.