Photolithography Mask Aerial Image Simulation Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for simulating aerial images of photolithography masks are either computationally expensive and inaccurate, particularly when dealing with short wavelengths and mask 3D effects, or they require extensive training data and computation time.
Innovation Solution
A computer-implemented method using a machine learning model that simulates the propagation of electromagnetic waves through a detailed model of a photolithography mask, taking into account its 3D structure and material properties, to generate accurate aerial images with reduced computation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigorous simulation methods (FDTD, RCWA) are used to simulate aerial images, then accuracy is improved, but computation time and computational load increase significantly
Solution Approach 1:
The patent pre-calculates and stores optical properties (transmission coefficients, phase shifts) of mask structures in lookup tables before simulation. During aerial image simulation, these pre-computed values are directly retrieved rather than calculating electromagnetic wave propagation in real-time, significantly reducing computation time while maintaining accuracy
Solution Approach 2:
The patent creates simplified 2D representations (copies) of complex 3D mask structures that capture essential optical characteristics. These simplified models are used for rapid aerial image simulation while preserving the key physical effects, achieving a balance between accuracy and computational efficiency
2Productivity
If the thin element approximation (TEA) is used to reduce computation time, then productivity is improved, but accuracy deteriorates due to inability to account for mask 3D effects
Solution Approach 1:
The patent applies different levels of modeling complexity to different parts of the mask structure. Simple 2D patterns use fast TEA methods, while complex 3D structures (absorber blocks, phase shifters) use pre-computed optical properties that capture 3D effects. This localized approach maintains accuracy where needed while preserving speed elsewhere
Solution Approach 2:
The patent transforms the 3D mask structure description into effective 2D optical parameters (complex transmission coefficients) that encode 3D effects. By changing the representation from geometric 3D models to optical parameter maps, the simulation achieves both speed and accuracy
3Measurement precision
If detailed 3D mask models are used to account for mask 3D effects, then accuracy is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential optical properties (transmission magnitude and phase) from detailed 3D mask models and stores them in compact lookup tables. The complex geometric details are separated from the simulation process, keeping the aerial image calculation simple while preserving accuracy through the extracted optical parameters
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
The method achieves high accuracy in simulating aerial images, effectively addressing mask 3D effects and short wavelength challenges, while significantly reducing computation time and memory requirements.
Implementation Method 1
simulating the propagation of electromagnetic waves through the model of the photolithography mask using a machine learning model, wherein the machine learning model maps the model of the photolithography mask to a representation of an electromagnetic field generated by the incident electromagnetic waves
Implementation Method 2
obtaining the aerial image of the model of the photolithography mask by applying a simulation of an imaging process of a photolithography system or optical metrology system within a projection section to the representation of the electromagnetic field
Data Source
AI summary
The invention relates to a computer implemented method for simulating an aerial image of a model of a photolithography mask illuminated by incident electromagnetic waves, the method comprising: obtaining the model of the photolithography mask, the model describing the photolithography mask at least partially in a dimension orthogonal to the mask carrier plane; simulating the propagation of the incident electromagnetic waves through the model of the photolithography mask using a machine learning model, wherein the machine learning model maps the model of the photolithography mask to a representation of an electromagnetic field generated by the incident electromagnetic waves on the photolithography mask; obtaining the aerial image of the model of the photolithography mask by applying a simulation of an imaging process. The invention also relates to corresponding computer programs, computer-readable media and systems.