Resist Model Calibration for SEM Artifact Correction
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Solution Overview
Problem
Current resist models fail to accurately predict resist contours, especially for focus-dependent features, due to metrology device-induced artifacts, leading to increased modeling errors as feature sizes shrink.
Innovation Solution
A method and system that calibrate resist models by generating a modeled resist contour based on a simulated aerial image, predicting a metrology contour from actual SEM measurements, and adjusting parameters to correct for SEM-induced artifacts, thereby improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional resist models are used to predict resist contours, then the modeling process is simple, but the prediction accuracy deteriorates due to SEM artifacts
Solution Approach 1:
The patent introduces an intermediary correction process that mediates between the simple conventional resist model and the actual measured data. A correction model is developed that accounts for SEM-specific artifacts and measurement conditions, acting as a bridge that transforms the simple model's output into accurate predictions by compensating for systematic errors introduced by the measurement device
Solution Approach 2:
The patent implements a feedback mechanism where measured resist contour data from SEM is compared with model predictions, and the difference (error) is used to iteratively refine and update the model parameters. This closed-loop feedback process continuously improves prediction accuracy by learning from measurement discrepancies while maintaining the underlying simple model structure
2Productivity
If feature sizes are reduced to increase device density, then productivity increases, but measurement precision deteriorates due to increased SEM artifact influence
Solution Approach 1:
The patent changes the parameters of the resist model to account for scale effects and SEM artifact characteristics that become more pronounced at smaller feature sizes. By adjusting model parameters based on feature size-dependent correction factors, the system maintains accurate predictions even as dimensions shrink and SEM artifacts become relatively more significant
Data Source
AI summary
A method for calibrating a resist model. The method includes: generating a modeled resist contour of a resist structure based on a simulated aerial image of the resist structure and parameters of the resist model, and predicting a metrology contour of the resist structure from the modeled resist contour based on information of an actual resist structure obtained by a metrology device. The method includes adjusting one or more of the parameters of the resist model based on a comparison of the predicted metrology contour and an actual metrology contour of the actual resist structure obtained by the metrology device.