Source and mask optimization via diffraction orders
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Solution Overview
Problem
Current illumination optimization and mask optimization techniques in microlithography are not effectively linked, leading to errors and increased computational time due to linearization of mask transmission using approximations, which is inadequate for precise image formation as logic feature sizes decrease.
Innovation Solution
A method that optimizes the illumination source and mask by determining optimal diffraction orders and intensity at fragmentation points, allowing for simultaneous adjustment of illumination source intensity and shape, and mask diffraction orders to maximize the image log slope while maintaining intensity within a predetermined range, thereby reducing computational time and variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If linearization of mask transmission using approximations is used, then computational time is reduced, but manufacturing precision deteriorates due to errors in image formation
Solution Approach 1:
The patent changes the parameter representation from linearized mask transmission to diffraction order parameters (magnitude and phase). This transformation allows the optimization to work directly with the physical parameters that govern image formation, eliminating the need for linearization approximations while maintaining computational efficiency through structured parameter optimization.
Solution Approach 2:
The patent replaces the traditional mechanical/optical optimization approach (directly optimizing mask transmission patterns) with a mathematical substitution approach (optimizing diffraction order parameters). This substitution enables precise control of image formation through analytical optimization of the diffraction spectrum, avoiding the errors introduced by linearization while reducing computational complexity.
2Device complexity
If traditional separate optimization of illumination and mask is used, then device complexity is reduced, but manufacturing precision deteriorates due to lack of coordination between illumination and mask parameters
Solution Approach 1:
The patent merges the separate optimization processes of illumination source and mask into a unified optimization framework. By simultaneously optimizing illumination source parameters (intensity distribution) and mask parameters (diffraction order magnitudes and phases) together, the system achieves coordinated optimization that maximizes image log slope, resolving the trade-off between system complexity 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
This approach enables fast computation of mask parameters, reduces the number of optimization variables, and optimizes mask transmission by non-linear optimization of diffraction orders followed by linear optimization of quantized mask transmissions, improving precision and efficiency in forming desired images.
Implementation Method 1
determining an optimal illumination source as an illumination source which maximizes the image log slope at the selected fragmentation points
Implementation Method 2
providing illumination from an illumination source to a plurality of source points and a predetermined mask pattern; selecting fragmentation points in an image plane of an image formed by the illumination provided to the predetermined mask pattern
Data Source
AI summary
An illumination source is optimized by changing the intensity and shape of the illumination source to form an image in the image plane that maximizes the minimum ILS at user selected fragmentation points while forcing the intensity at the fragmentation points to be within a small intensity range. An optimum mask may be determined by changing the magnitude and phase of the diffraction orders to form an image in the image plane that maximizes the minimum ILS at user selected fragmentation points while forcing the intensity at the fragmentation points to be within a small intensity range. Primitive rectangles having a size set to a minimum feature size of a mask maker are assigned to the located minimum and maximum transmission areas ad centered at a desired location. The edges of the primitive rectangle are varied to match optimal diffraction orders O(m,n). The optimal CPL mask OCPL(x,y) is then formed.