The Analysis of Microlithography Image Simulation and Sub Resolution Assist Feature Combining with HSMO
Date Issued
2011
Date
2011
Author(s)
Lou, Yi-Chuan
Abstract
Optical micro-lithography technology simulation is a critical step in semiconductor manufacturing. As the VLSI manufacture technology develops, the feature size of micro-electronic devices shrinks smaller than the wavelength of exposure light source in modern microlithography. Consequently, the image quality and resolution on the wafer are getting worse owing to diffraction effect. Therefore, lots of resolution enhancement technologies (RETs) with remarkable skills and algorithms are so far widely proposed to minimize the difference between design pattern and image result. Conventional RETs such as off-axis illumination (OAI), phase shift mask (PSM), and optical proximity correction (OPC) are in favor of improving the printing quality.
In this thesis, we consider a method which combines sub resolution assist features (SRAF) and hierarchical source mask optimization (HSMO). Firstly, we add sub resolution assist features for improving depth of focus, and then using the hierarchical source mask optimization for finer image quality.
However, we must ensure the correctness of our vector resist image simulation before using resolution enhancement technology. Thus, we’ll completely introduce the optical lithography image system, and compare the result of our simulation to the result of Sentaurus Lithography from Synopsys© for accuracy.
Besides, there are millions of devices having to deal with in the nowadays state-of-art, it will take lots of time and space to get the final image result. In this work, we utilize the principal component analysis on Abbe’s image formulation (Abbe-PCA) for high speed kernel compaction on the convolution image kernel, and then the convolution lookup table is used in order to accelerate object cost function evaluation, which usually takes a valuable time consuming to get the full image simulation.
In this thesis, we use image intensity error as our cost function. The cost function value of optimized simulation result is 67.46% better than original one, and the optimized result is not only more similar to the original design pattern but also providing finer tolerance of defocus.
Subjects
Resist image simulation
Abbe-PCA (principal component analysis)
RET (resolution enhancement technology)
OPC (optical proximity correction)
HSMO (hierarchical source mask optimization)
SRAF (sub-resolution assist feature)
Type
thesis
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