Stochastic Failure Model for Lithographic Resist Layers
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Solution Overview
Problem
Lithographic processes face challenges in accurately reproducing patterns with features smaller than the classical resolution limit, leading to stochastic variations in resist layers that result in failures such as hole closure, missing holes, and line breaking, which affect the yield and throughput of semiconductor manufacturing.
Innovation Solution
A method is developed to determine a failure model that simulates stochastic behavior in resist layers by fitting measured data to a product of dose and image intensity values, allowing for the prediction of pattern failures and adjustment of patterning process parameters like dose, focus, and optical proximity correction to reduce failure rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lithographic projection apparatus is used to manufacture ICs with features smaller than classical resolution limit, then manufacturing precision is improved, but stochastic variations in resist layer cause pattern failures
Solution Approach 1:
The patent applies preliminary action by performing optical proximity correction (OPC) and adjusting patterning parameters before the actual lithographic exposure. The system calculates corrected patterns and optimizes dose and focus parameters in advance to compensate for anticipated stochastic variations, thereby preventing pattern failures before they occur during manufacturing
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting patterning parameters such as exposure dose, focus position, and numerical aperture based on calculated stochastic variation models. The system modifies these parameters to shift the process window away from critical failure thresholds, thereby maintaining manufacturing precision while reducing pattern failure rates
2Manufacturing precision
If sophisticated fine-tuning steps are applied to overcome resolution limitations, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical fine-tuning procedures with computational methods. Instead of relying on manual optimization and multiple iterative lithographic steps, the system uses automated optical proximity correction algorithms and stochastic variation models to calculate optimal patterning parameters, thereby reducing device complexity while maintaining manufacturing precision
Solution Approach 2:
The patent uses copying by creating virtual models and simulations of the lithographic process to predict and correct pattern outcomes. The system generates corrected pattern data and stochastic variation models that serve as copies of the intended design, allowing optimization to be performed computationally rather than through repeated physical trial-and-error processes
Data Source
AI summary
A method of determining a failure model of a resist process of a patterning process. The method includes obtaining (i) measured data of a pattern failure (e.g., failure rate) related to a feature printed on a substrate based on a range of values of dose, and (ii) image intensity values for the feature via simulating a process model using the range of the dose values; and determining, via fitting the measured data of the pattern failure to a product of the dose values and the image intensity values, a failure model to model a stochastic behavior of spatial fluctuations in the resist and optionally predict failure of the feature (e.g., hole closing).