Optical Critical Dimension Models for Faster Semiconductor Metrology
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Solution Overview
Problem
Creating a three-dimensional model for optical critical dimension (OCD) metrology in semiconductor devices is time-consuming and resource-intensive.
Innovation Solution
Utilizing machine learning models to establish and update optical transformation models based on measured spectra and physical parameters of semiconductor structures, allowing for more precise determination of structure parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimension model is created based on references and spectra to determine dimensions of semiconductor devices, then measurement precision is improved, but productivity deteriorates due to time-consuming and resource-intensive model creation
Solution Approach 1:
The patent pre-calculates and stores optical transformation models for various semiconductor structures before actual measurement. When measuring a device, the system selects from pre-existing models rather than creating a new three-dimension model from scratch, significantly reducing measurement time while maintaining precision.
Solution Approach 2:
The patent transforms the complex three-dimension model creation process into a parameter-based optical transformation model selection process. By changing from geometric model creation to spectral parameter matching, the system achieves fast and accurate dimension determination without extensive computational modeling.
2Measurement precision
If traditional optical transformation models are used, then device complexity is reduced, but measurement precision deteriorates due to inability to capture physical parameter variations
Solution Approach 1:
The patent introduces physical parameters (refractive index, extinction coefficient) as intermediaries between the optical measurement and structural dimension determination. These parameters act as a bridge that captures material properties and their variations, enabling more accurate dimension measurement without directly complexifying the geometric model.
Solution Approach 2:
The patent incorporates dynamic physical parameter variations into the optical transformation models. By allowing refractive index and extinction coefficient to vary with wavelength and structure dimensions, the models achieve higher measurement precision while managing complexity through parameterized representations rather than full three-dimension geometric modeling.
Data Source
AI summary
The present disclosure provides a method and a system for obtaining OCD. The system inputs a plurality of first spectra associated with a first semiconductor structure into the first optical transformation model to output a plurality of first structure parameters; update the first structure parameters based on at least one physical parameter associated with the first semiconductor structure; establish a second optical transformation model according to the updated first structure parameters and the corresponding first spectra; and store the second optical transformation model for later OCD generation.