Source Mask Optimization for Lithography Defect Reduction
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Solution Overview
Problem
Current lithographic projection apparatuses face challenges in accurately reproducing patterns with features smaller than the classical resolution limit, leading to defects and reduced yield due to the difficulty in optimizing source and mask characteristics effectively.
Innovation Solution
A method is introduced that involves defining a defect ambit around potential defect locations on a substrate, determining cost functions associated with defect metrics, and adjusting source and mask characteristics using guide functions to optimize the patterning process, thereby reducing defects and improving yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional lithographic projection apparatuses are used to reproduce patterns with features smaller than the classical resolution limit, then the manufacturing precision deteriorates due to defects, but the productivity is reduced because effective optimization of source and mask characteristics is difficult
Solution Approach 1:
The patent applies preliminary action by performing source and mask optimization before the actual lithographic patterning process. The method calculates cost functions based on defect metrics and determines guide functions in advance to guide the optimization process, allowing the system to pre-determine optimal source and mask characteristics that will minimize defects in subsequent production runs.
Solution Approach 2:
The patent implements feedback by using defect metrics from actual or simulated lithographic results to calculate cost functions, which then feed back into the optimization process. The guide functions are determined based on this feedback loop, continuously adjusting source and mask characteristics to reduce defects and improve pattern reproduction accuracy.
2Manufacturing precision
If the lithographic process is optimized to reduce defects, then the manufacturing precision improves, but the loss of time increases due to additional optimization steps
Solution Approach 1:
The patent replaces traditional trial-and-error mechanical optimization approaches with computational methods. By using cost function calculations and guide function determination based on defect metrics, the system substitutes iterative physical adjustments with algorithmic optimization, reducing the time required to achieve defect reduction while maintaining manufacturing precision.
3Productivity
If features are made smaller to increase functional elements per device, then the productivity increases, but the manufacturing precision deteriorates due to increased difficulty in pattern reproduction
Solution Approach 1:
The patent applies parameter changes by systematically adjusting source and mask characteristics based on calculated cost functions and guide functions. The optimization process modifies parameters such as source illumination distribution and mask pattern dimensions to maintain manufacturing precision even as feature sizes are reduced to increase the number of functional elements per device.
Data Source
AI summary
Methods for optimizing an aspect of a patterning process based on defects. For example, a method of source and mask optimization of a patterning process includes obtaining a location on a substrate having a threshold probability of having a defect; defining an defect ambit around the location to include a portion of a pattern on the substrate and one or more evaluation points associated with the portion of the pattern; determining a value of a first cost function based on a defect metric associated with the defect; determining a first guide function for the first cost function, wherein the first guide function is associated with a performance metric of the patterning process at the one or more evaluation locations within the defect ambit; and adjusting a source and/or a mask characteristic based on the value of the first cost function, and the first guide function.