OPC modeling by genetic algorithm

Huang, Wen‐Shih; Lai, Ching-Ming; Luo, Boren; Tsai, Cheng‐Kun; Tsay, Cherng-Shyan; Lai, Chien-Wen; Kuo, C. C.; Liu, R. G.; Lin, Hon-Jarn; Lin, B. N.

doi:10.1117/12.602096

Cited by 9 publications

(4 citation statements)

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“…Currently, traditional optimization methods, such as gradient descent (GD) [19][20] and heuristic methods [21][22][23][24], are employed in resist model calibration. The former relies on the gradient information of the loss function to update the parameters, which is widely used due to its stable convergence and clear underlying principles.…”

Section: Introductionmentioning

confidence: 99%

“…However, the calculation of gradient is usually fairly computation consuming and in most of the cases we do not care much about the gradient convergence pathway. The latter treats the optimization problem as a black box and finds a suboptimal solution by employing certain search mechanisms, such as genetic algorithm (GA) [21][22] and particle swarm optimization (PSO) [23][24]. This approach offers the advantages of not requiring a priori knowledge or calculation of gradient.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…To improve the consistency of the pattern on the wafer, it is necessary to reduce the influence of the optical proximity effect on the transferred pattern on the wafer through optical proximity correction (OPC). 1 The current mainstream model-based OPC in the industry involves OPC modeling using parameters determined by optical model parameters and resist model parameters. 2 In this process, electronic design automation (EDA) providers in the market generally optimize the parameters of the optical model and the parameters of the photoresist model separately to construct the OPC model.…”

Section: Introductionmentioning

confidence: 99%

Co-optimization of optical and resist models in the OPC modeling process using in-house genetic algorithm

Pan¹,

Wang²,

Chen³

et al. 2023

DTCO and Computational Patterning II

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Traditional modeling of computational lithography starts first by determining the functional relationship between the change in focus and the aerial image (AI) location of the optical model by setting constraints and then calibrating the resist model separately. In this process, built-in genetic algorithm (GA) tools usually participate in the parameter optimization process of only one model at a time. Additionally, GA tools are vulnerable to becoming trapped in a locally optimal solution. The practice of optimizing the optical and resist models separately may potentially miss better solutions. We propose a method to co-optimize the two models simultaneously. This is done by finding the Pareto optimal frontier of potentially better solution candidates that balance these two models. To avoid the local optimal solution trap, a method is proposed to increase the search range when the algorithm is confined. In the selecting and scoring models process, we quantify metrics that are typically made empirically by engineers to achieve higher levels of automation.

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“…The diffusion length of the resist in the resist model is calibrated with a cross-section SEM. The DAIM model is calibrated with the genetic algorithm[11].…”

mentioning

confidence: 99%

Joint calibration of 3D resist image and CDSEM

Chou

Tang

et al. 2013

SPIE Proceedings

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Traditionally, an optical proximity correction model is to evaluate the resist image at a specific depth within the photoresist and then extract the resist contours from the image. Calibration is generally implemented by comparing resist contours with the critical dimensions (CD). The wafer CD is usually collected by a scanning electron microscope (SEM), which evaluates the CD based on some criterion that is a function of gray level, differential signal, threshold or other parameters set by the SEM. However, the criterion does not reveal which depth the CD is obtained at. This depth inconsistency between modeling and SEM makes the model calibration difficult for low k 1 images.In this paper, the vertical resist profile is obtained by modifying the model from planar (2D) to quasi-3D approach and comparing the CD from this new model with SEM CD. For this quasi-3D model, the photoresist diffusion along the depth of the resist is considered and the 3D photoresist contours are evaluated. The performance of this new model is studied and is better than the 2D model.

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OPC modeling by genetic algorithm

Cited by 9 publications

References 0 publications

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

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

Co-optimization of optical and resist models in the OPC modeling process using in-house genetic algorithm

Joint calibration of 3D resist image and CDSEM

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