Dummy Feature Density Computation for CMP Topography Control
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Solution Overview
Problem
Current methods for computing dummy feature density in Chemical-Mechanical Polishing (CMP) processes are inefficient and often result in suboptimal dummy feature placement, leading to significant post-CMP topography variations that can cause fabrication issues such as defocusing during photolithography and performance variations in semiconductor chips.
Innovation Solution
A system that computes dummy feature density by discretizing a layout into panels, calculating feature and slack densities, and iteratively adjusting these densities using a variance-minimizing heuristic and a 2-D filter function to minimize effective feature density variation, thereby reducing post-CMP topography variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple rule-based techniques are used to add dummy features, then the process is easy to implement, but the dummy features are added at suboptimal locations and more dummy features are added than necessary
Solution Approach 1:
The patent transforms the dummy feature placement problem from a simple rule-based approach to an optimization problem by changing the parameters to be optimized (effective feature density, post-CMP topography variation) and using an objective function to guide the placement of dummy features to optimal locations
Solution Approach 2:
The patent incorporates feedback mechanisms by using an objective function that evaluates the impact of dummy feature placement on post-CMP topography variation, allowing the system to adjust placement strategies based on computed outcomes and iterate toward optimal solutions
2Manufacturing precision
If LP-based smart dummy filling techniques are used, then optimal dummy feature placement is achieved, but the computational cost becomes prohibitively high for large layouts
Solution Approach 1:
The patent divides the large layout into smaller panels or regions, allowing the optimization to be performed locally on each panel rather than globally across the entire layout, significantly reducing computational complexity while maintaining placement accuracy
Solution Approach 2:
The patent changes the optimization approach from exact LP-based methods to approximate optimization techniques that use objective functions and heuristics, trading some computational precision for dramatically reduced computation time that makes the process practical for large layouts
3Manufacturing precision
If more dummy features are added to low density regions, then post-CMP topography variation is reduced, but the feature density non-uniformity may increase and other issues arise
Solution Approach 1:
The patent uses objective functions that explicitly model the relationship between dummy feature density and post-CMP topography variation, allowing the system to find the optimal quantity of dummy features that achieves uniformity without excessive addition
Solution Approach 2:
The patent incorporates feedback through the objective function evaluation that monitors both the topography variation improvement and the dummy feature density increase, allowing the optimization to stop when the marginal benefit diminishes or negative effects begin to appear
Data Source
AI summary
One embodiment of the present invention provides a system that computes dummy feature density for a CMP (Chemical-Mechanical Polishing) process. Note that the dummy feature density is used to add dummy features to a layout to reduce the post-CMP topography variation. During operation, the system discretizes a layout of an integrated circuit into a plurality of panels. Next, the system computes a feature density and a slack density for the plurality of panels. The system then computes a dummy feature density for the plurality of panels by, iteratively, (a) calculating an effective feature density for the plurality of panels using the feature density and a function that models the CMP process, (b) calculating a filling amount for a set of panels in the plurality of panels using a target feature density, the effective feature density, and the slack density, and (c) updating the feature density, the slack density, and the dummy feature density for the set of panels using the filling amount. In one embodiment of the present invention, the iterative process is guided by a variance-minimizing heuristic to efficiently select the set of panels and assign/remove dummy density to the set of panels to decrease the effective feature density variation.