Lithography Process Modeling for Faster Parameter Optimization
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Solution Overview
Problem
Current methods for optimizing device manufacturing processes, such as lithography, involve numerous trial exposures and measurements due to the complexity of characterizing variable parameters, leading to inefficiencies and a need for improved methods to control and analyze these processes effectively.
Innovation Solution
A method involving the creation of a multi-dimensional probability function to represent the expected distribution of process parameters and a performance function to map these parameters to a performance probability function, allowing for optimized process settings and reduced measurements, is employed. This method includes exposing, developing, and transferring radiation-sensitive layers on substrates using determined nominal settings and using measured characteristics to adjust subsequent process steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional trial exposures and measurements are used to optimize process parameters, then manufacturing precision can be improved, but the time and complexity of the optimization process increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-characterizing the relationships between process parameters and performance metrics through simulations and experiments before actual manufacturing. A performance function is established in advance that can predict outcomes without requiring extensive trial exposures during production optimization.
Solution Approach 2:
The patent uses simulations and models as copies of the actual manufacturing process to predict performance outcomes. Instead of repeatedly performing physical trial exposures, virtual models replicate the process behavior to guide parameter optimization, reducing the need for physical iterations.
2Measurement precision
If comprehensive characterization of all variable parameters is performed, then process control accuracy is improved, but the number of required measurements and simulations increases dramatically
Solution Approach 1:
The patent extracts and focuses on the most critical process parameters that have the greatest impact on performance, rather than attempting to measure and characterize all possible parameters. The performance function identifies key input parameters that drive output variations, allowing selective measurement of only those critical factors.
Solution Approach 2:
The patent transforms the complex multi-parameter optimization problem into a simplified form by changing the representation from individual parameter measurements to a performance function that directly relates key parameters to outcomes. This parameter transformation reduces the dimensionality of the measurement problem.
3Reliability
If process parameters are optimized for maximum yield, then manufacturing quality improves, but throughput rate may be reduced
Solution Approach 1:
The patent implements dynamic optimization where the performance function can be evaluated rapidly for different parameter sets, allowing the system to adaptively find optimal settings that balance yield and throughput. The pre-established performance function enables quick what-if analysis without extensive re-measurement.
Solution Approach 2:
The patent establishes a feedback mechanism where the performance function continuously evaluates process parameters against target outcomes. This feedback loop allows real-time adjustments to maintain optimal yield while identifying opportunities to accelerate throughput by adjusting parameters within acceptable performance margins.
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 reduces the time and effort required for optimizing lithographic processes, improves yield, and allows for more accurate control of device manufacturing by determining optimal process settings with fewer measurements, thereby enhancing the precision and efficiency of device production.
Implementation Method 1
exposing a radiation-sensitive layer on a substrate to form a latent image therein
Data Source
AI summary
A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.