Model-Based Positional Correction for Digital Lithography Stabilization
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Solution Overview
Problem
Existing photolithography systems take a long time to stabilize, leading to uneven patterning due to thermal variations and other effects, affecting total pitch and overlay correction repeatability.
Innovation Solution
A model-based approach is used to collect positional and environmental data during the stabilization period, creating a model to estimate and dynamically correct errors, allowing for precise patterning without hardware modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the photolithography system operates during the stabilization period without corrections, then the system can begin printing immediately, but the patterning becomes uneven due to thermal variations and positional drift
Solution Approach 1:
The system performs preliminary actions during the stabilization period by collecting temperature, pressure, and humidity data, and creating a model of expected positional errors. This preliminary modeling enables subsequent real-time corrections without delaying production
Solution Approach 2:
The system implements feedback by continuously monitoring environmental parameters and comparing actual positional readings against the predicted error model. This feedback loop enables dynamic corrections to be applied during printing, maintaining precision while the system is still stabilizing
2Manufacturing precision
If the system waits for full stabilization before printing, then patterning uniformity improves, but production time increases by 8 hours or longer
Solution Approach 1:
Instead of waiting for full stabilization, the system performs preliminary error characterization during the stabilization period by collecting data and building a predictive model. This allows the system to compensate for drift in real-time rather than waiting for it to stop
Solution Approach 2:
The patent replaces the traditional mechanical/waiting approach to stabilization with a software-based correction system. Rather than waiting for thermal equilibrium, the system uses computational models and real-time calculations to correct positional errors, substituting software intelligence for physical waiting time
3Manufacturing precision
If hardware solutions are implemented to reduce thermal variations, then patterning precision improves, but system cost and complexity increase
Solution Approach 1:
The patent substitutes software-based error correction for hardware-based thermal control. Instead of adding complex thermal management hardware, the system uses computational models to predict and correct positional errors caused by thermal drift, reducing hardware complexity while maintaining precision
Solution Approach 2:
The system changes the approach from controlling physical parameters (temperature, pressure) through hardware to correcting output parameters (positional readings, patterning coordinates) through software. This parameter transformation allows precision improvement without increasing hardware 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
Enhances total pitch and overlay correction repeatability by applying software-based corrections to digital masks, reducing the need for costly hardware solutions and improving stabilization efficiency.
Implementation Method 1
The tool comprises numerous heat sources and components that have different conductivity coefficients and thermal capacitances, each potentially contributing to the variations causing the uneven patterning
Data Source
AI summary
The present disclosure generally relates to photolithography systems, and methods for correcting positional errors in photolithography systems. When a photolithography system is first started, the system enters a stabilization period. During the stabilization period, positional readings and data, such as temperature, pressure, and humidity data, are collected as the system prints or exposes a substrate. A model is created based on the collected data and the positional readings. The model is then used to estimate errors in subsequent stabilization periods, and the estimated errors are dynamically corrected during the subsequent stabilization periods.