Grid-Based Resist Simulation for Lithography Fidelity
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Solution Overview
Problem
Conventional photolithographic processes face challenges in accurately simulating and compensating for distortions in feature patterns on wafers, particularly as feature sizes decrease, due to limitations in resolution enhancement techniques that rely on discrete sampling sites and complex resist modeling.
Innovation Solution
A grid-based resist simulator computes a resist surface function for all locations on a wafer, using aerial image intensity values to determine a threshold value and contour, allowing for more accurate prediction of feature patterns and enabling improved resolution enhancement techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If discrete sampling sites are used for resist simulation, then processing time is reduced, but accuracy of resist behavior representation deteriorates
Solution Approach 1:
The simulation domain is segmented into a grid of discrete points that uniformly cover the entire wafer surface. This segmentation allows the continuous resist behavior to be represented through a structured set of discrete calculations, achieving both computational efficiency and comprehensive coverage without relying on sparsely distributed sampling sites
Solution Approach 2:
The approach transitions from point-based discrete sampling to a two-dimensional grid-based representation. By introducing the grid structure as a new dimensional framework, the method achieves continuous coverage across the wafer surface while maintaining computational tractability through systematic discretization
2Manufacturing precision
If resolution enhancement techniques are applied, then fidelity of feature printing is improved, but complexity of simulation and processing increases
Solution Approach 1:
The resist surface function is calculated in advance for all grid points across the entire wafer before the actual lithography process. This preliminary computation of resist behavior under various mask patterns enables subsequent optimization of resolution enhancement techniques without requiring complex real-time simulations during processing
Solution Approach 2:
The method creates a computational model (resist surface function) that replicates and predicts resist behavior under different exposure conditions. This virtual copy of resist behavior allows for simulation and optimization of resolution enhancement techniques without physically modifying the actual lithography process, reducing overall system complexity
3Measurement precision
If grid-based simulation is performed for all locations, then accuracy of feature pattern prediction is improved, but computational time increases
Solution Approach 1:
The computational domain is divided into a regular grid of points, allowing the complex continuous simulation to be broken down into many simpler discrete calculations. This segmentation enables accurate prediction across all locations while maintaining computational efficiency through structured processing
Solution Approach 2:
The method transforms the resist simulation into a parameter-based calculation where the resist surface function is evaluated at grid points using standardized computational parameters. By changing the approach from physical simulation to parameter-based prediction, the method achieves high accuracy with reduced computational time
Data Source
AI summary
A grid-based resist simulator predicts how a wafer coated with one or more resist layers will develop when exposed with a mask pattern. Image intensity values are calculated at a grid of points on the wafer, and the image intensity points are analyzed with a resist simulator that produces a resist surface function. A threshold contour of the resist surface function defines how the mask pattern will print on a wafer.