Photolithography Process Control Using Precomputed Diffraction Signals
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Solution Overview
Problem
Conventional methods for quality assurance in semiconductor manufacturing using periodic gratings are time-consuming and costly due to the need for complex calculations in generating simulated diffraction signals for comparing with measured signals, which limits efficient process control in photolithography clusters.
Innovation Solution
A machine learning system is trained to generate simulated diffraction signals using photoresist parameters, allowing for the determination of matching parameters and adjustment of process settings in photolithography clusters based on correlations between photoresist and profile parameters, reducing the reliance on rigorous coupled wave analysis and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigorous coupled wave analysis (RCWA) is used to generate simulated diffraction signals, then measurement precision is improved, but productivity deteriorates due to time-consuming complex calculations
Solution Approach 1:
The patent pre-calculates and stores diffraction signals for various grating profiles in a lookup table before actual measurement. When a measured diffraction signal is obtained, the system quickly searches the pre-computed table for matching signals rather than performing real-time RCWA calculations, thus achieving both high precision and fast processing speed
Solution Approach 2:
The patent creates simplified copies of the complex RCWA calculation results by storing diffraction signals for standard profiles in a database. Instead of replicating the entire calculation process during measurement, the system uses pre-computed signal copies that can be rapidly compared against measured data, maintaining accuracy while dramatically reducing computation time
2Manufacturing precision
If rigorous coupled wave analysis (RCWA) is used to generate simulated diffraction signals, then manufacturing precision is improved, but loss of time increases due to complex calculations
Solution Approach 1:
The system performs diffraction signal calculations in advance for various grating profiles and stores them in a lookup table. During actual manufacturing quality control, the measured signal is compared against these pre-computed references, eliminating the need for time-consuming real-time RCWA calculations while maintaining manufacturing precision
Solution Approach 2:
The patent uses simplified profile representations from the lookup table instead of performing full RCWA calculations each time. These pre-computed signal profiles act as disposable references that can be quickly compared and discarded after use, rather than regenerating them through expensive computational processes
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 enables faster and more cost-effective process control in photolithography clusters by using machine learning to match measured diffraction signals with simulated ones, allowing for real-time adjustments of process parameters and improving the accuracy of semiconductor structure fabrication.
Implementation Method 1
The periodic grating is then illuminated with an electromagnetic radiation. The electromagnetic radiation that deflects off of the periodic grating are collected as a diffraction signal.
Data Source
AI summary
To control a photolithography cluster using optical metrology, a structure is fabricated on a wafer using the photolithography cluster. A measured diffraction signal off the structure is obtained. The measured diffraction signal is compared to a simulated diffraction signal. The simulated diffraction signal is associated with one or more values of one or more photoresist parameters. The one or more photoresist parameters characterize behavior of photoresist when the photoresist undergoes processing steps in the photolithography cluster. The simulated diffraction signal was generated using one or more values of one or more profile parameters. The one or more values of the one or more profile parameters used to generate the simulated diffraction signal were derived from the one or more values of the one or more photoresist parameters associated with the simulated diffraction signal. If the measured diffraction signal and the simulated diffraction signal match, then one or more values of one or more photoresist parameters used in the photolithography cluster are determined to be the one or more values of the one or more photoresist parameters associated with the matching simulated diffraction signal. One or more process parameters or equipment settings of the photolithography cluster are adjusted based on the one or more values of the one or more photoresist parameters.