Compact modeling of negative tone development resist with photo decomposable quencher

Chen, Ao; Koh, Kar Kit; Foong, Yee Mei; Morgenfeld, Bradley; Lee, Sandra; Chen, Xi; Omar, Hesham R.; Mu, Feng; Wang, Chang‐An; Gronlund, Keith; Lang, Jun; Guerrero, James; Zhao, Yingjie

doi:10.1117/12.2514784

Cited by 2 publications

(1 citation statement)

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“…There are several special physical effects in the NTD process, such as photoresist shrinkage [7], developer depletion and diffusion [8], and critical dimension (CD) hopping induced by subresolution assistance feature [9]. NTD resist exhibits different development characteristics with organic solvent compared to aqueous based development of PTD resist [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Cluster Sampling and Scalable Bayesian Optimization with Constraints for Negative Tone Development Resist Model Calibration

Ma,

Hao

et al. 2024

Preprint

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As the semiconductor technology node continues to shrink, achieving smaller critical dimension in lithography becomes increasingly challenging. Negative tone development (NTD) process is widely employed in advanced node due to their large process window. However, the unique characteristics of NTD, such as shrinkage effect, make the NTD resist model calibration more complex. Gradient descent (GD) and heuristic methods have been applied for calibration of NTD resist model. Nevertheless, these methods depend on initial parameter selection and tend to fall into local optima, resulting in poor accuracy of the NTD model and massive computational time. In this paper, we propose cluster sampling and scalable Bayesian optimization (BO) with constraints method for NTD resist model calibration. This approach utilizes cluster sampling strategy to enhance the capability for global initial sampling and employs scalable BO with constraints for global optimization of high-dimensional parameter space. With this approach, the calibration accuracy is significantly enhanced in comparison with results from GD and heuristic methods, and the computational efficiency is substantially improved compared with GD. By gearing up cluster sampling strategy and scalable BO with constraints, this method offers a new and efficient resist model calibration.

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