Flash-Based Anti-Aliasing Lookup Tables for Lithography Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semiconductor manufacturing processes face challenges in converting non-bandlimited pattern layouts into band-limited images for optical lithography simulations, as existing methods like DFT-based techniques require costly and computationally intensive sub-pixel sampling to suppress alias-noise effectively.
Innovation Solution
A system that constructs anti-aliasing (AAF) lookup tables by convolving basis functions with an AAF kernel, allowing for efficient conversion of non-bandlimited patterns into band-limited images using precomputed convolution values, and applies these to simulate optical lithography processes with improved computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DFT-based techniques are used to convert non-bandlimited patterns into band-limited images, then alias-noise suppression is achieved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent precomputes convolution values for basis functions and stores them in lookup tables before actual pattern conversion. This preliminary action allows the system to retrieve precomputed values during runtime instead of performing computationally intensive DFT-based convolutions, thereby maintaining alias-noise suppression accuracy while dramatically reducing processing time
Solution Approach 2:
The patent creates simplified representations (copies) of the convolution operations by precomputing and storing results in lookup tables. These copied values approximate the full DFT-based convolution results but can be retrieved and applied much more efficiently, trading minimal computational accuracy for significant speed improvements
2Manufacturing precision
If sub-pixel sampling is applied to suppress alias-noise in DFT-based techniques, then conversion accuracy improves, but computational complexity and cost increase
Solution Approach 1:
The patent segments the pattern conversion process into distinct stages: precomputation of basis function convolutions, storage in lookup tables, and runtime retrieval. This segmentation allows sub-pixel sampling to be applied only during the precomputation phase rather than continuously during processing, reducing overall computational complexity while maintaining precision
Solution Approach 2:
Sub-pixel sampling operations are performed in advance during the precomputation phase to generate accurate lookup table values. By performing this complex operation preliminarily, the system achieves high conversion accuracy without repeating the computationally intensive sub-pixel sampling during actual pattern conversion
3Measurement precision
If high-density sampling grids are used in simulations, then simulation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent creates a compact copy of the convolution operation results in lookup tables that can be quickly retrieved during simulation. This copied representation allows the system to use high-density sampling grids for accurate simulation without repeating the full convolution calculations at each sampling point, thereby maintaining simulation accuracy while improving computational efficiency
Data Source
AI summary
One embodiment of the present invention provides a system that converts a non-bandlimited pattern layout into a band-limited pattern image to facilitate simulating an optical lithography process. During operation, the system receives the non-bandlimited pattern layout which comprises one or more polygons. The system further receives an anti-aliasing filter (AAF) kernel, wherein the AAF kernel is configured to convert a non-bandlimited pattern into a band-limited pattern. The system then constructs an AAF lookup table for the AAF kernel, wherein the AAF lookup table contains precomputed values for a set of convolution functions which are obtained by convolving a set of basis functions with the AAF kernel. Next, the system creates a sampled pattern layout by applying a grid map over the pattern layout. The system then obtains the band-limited pattern image by using the AAF lookup table to convolve the AAF kernel with each grid location in the sampled pattern layout.