Lithography Pattern Calculation for Critical Dimension Variation Control
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Solution Overview
Problem
Current lithography methods face challenges in accurately reproducing small critical dimensions on substrates due to manufacturing variations and computational inefficiencies, particularly in optical lithography and inverse lithography technology, leading to increased costs and reduced yield.
Innovation Solution
A method involving neural networks is used to calculate patterns on substrates by inputting physical design patterns, generating multiple possible neighborhoods, and adjusting parameters to reduce manufacturing variation, incorporating charged particle beam systems for precise pattern formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optical lithography or inverse lithography technology is used to fabricate small critical dimensions, then manufacturing precision is improved, but computational time increases and manufacturing variations worsen
Solution Approach 1:
The patent pre-calculates and stores lookup tables containing pattern formation results for various parameter combinations before actual manufacturing. During production, the system only needs to query these pre-computed tables rather than performing full computational simulations, dramatically reducing real-time computational time while maintaining precision for small critical dimensions
Solution Approach 2:
The patent accounts for manufacturing variations by pre-computing patterns under multiple possible variation conditions and storing them in lookup tables. This beforehand cushioning against variations allows the system to quickly select appropriate pre-computed patterns that compensate for expected manufacturing tolerances, improving critical dimension accuracy without adding computational overhead during production
2Manufacturing precision
If inverse lithography technology is used to calculate patterns, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent creates simplified lookup tables that copy and store pre-computed pattern results from complex inverse lithography calculations. Instead of performing complex computations during manufacturing, the system uses these simplified lookup tables to quickly retrieve appropriate patterns, reducing device complexity while preserving the high pattern fidelity achieved through inverse lithography
Solution Approach 2:
The patent divides the complex inverse lithography calculation process into separate pre-computation and query phases. The computationally intensive parts are segmented and performed beforehand to build lookup tables, while the manufacturing phase only requires simple table lookups, effectively reducing the complexity burden during actual production
3Device complexity
If traditional lithography methods are used, then device complexity is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The patent performs preliminary computations to create lookup tables that encode the complex pattern formation physics. This allows traditional simpler lithography systems to achieve improved critical dimension control by querying pre-computed results rather than requiring complex real-time calculations, effectively bridging the gap between system simplicity and manufacturing 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
Enhances manufacturing accuracy and reduces computational time by modeling multiple manufacturing stages simultaneously, allowing for real-time adjustments and improved pattern fidelity on substrates.
Implementation Method 1
In all types of charged particle beam lithography, charged particle beams shoot energy to a resist-coated surface to expose the resist
Data Source
AI summary
Methods for calculating a pattern to be manufactured on a substrate include inputting a physical design pattern, determining a plurality of possible neighborhoods for the physical design pattern, generating a plurality of possible mask designs for the physical design pattern, calculating a plurality of possible patterns on the substrate, calculating a variation band from the plurality of possible patterns, and modifying the physical design pattern to reduce the variation band. Embodiments also include inputting a set of parameters for a neural network to calculate a pattern to be manufactured on a substrate, calculating a plurality of patterns to be manufactured on the substrate for the physical design in each possible neighborhood of the plurality of possible neighborhoods, training the neural network with the calculated plurality of patterns, and adjusting the set of parameters to reduce the manufacturing variation for the calculated plurality of patterns to be manufactured on a substrate.