Modeling and optimization of thermal-flow lithography process using a neural-genetic approach

Li, Te-Sheng; Chen, Szu-Hung

doi:10.1007/s10845-009-0271-0

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…For thermal-flow lithography processes, a hybrid approach, combining the Taguchi method, ANN, and GA, was proposed to solve for the process modeling and parameter optimization (Li and Chen 2011). Furthermore, in (Li and Chen 2011), the ANN is embedded inside the GA to produce the predicted process output.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid evolutionary optimization for nutraceutical manufacturing processes

2015

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In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN's input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Furthermore, in (Li and Chen 2011), the ANN is embedded inside the GA to produce the predicted process output.…”

Section: Related Workmentioning

confidence: 99%

Hybrid evolutionary optimization for nutraceutical manufacturing processes

2015

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“…Machine learning (ML) is one of the most intelligent and cutting-edge modeling methods in artificial intelligence (AI), which studies how to use computers to simulate or implement human learning activities . Recently, researchers have begun to pay attention to an ML method, that is, long short-term memory (LSTM) networks, and use them to build predictive models for fuel cells, financial market, stock market, soft sensor, wind power, photovoltaic power, and solar irradiance. − It is worth noting that many scholars have demonstrated that the LSTM network − with long-term memory function has better prediction ability than multilayer perceptron neural network (MPNN), ,,− which is one of the most commonly used ML modeling methods. Therefore, in this article, we use an LSTM network to establish a lithographic imaging prediction model.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applied to Electron Beam Lithography to Accelerate Process Optimization of a Contact Hole Layer

Zhao,

Wang,

Wei

et al. 2024

ACS Appl. Mater. Interfaces

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Determining the lithographic process conditions with high-resolution patterning plays a crucial role in accelerating chip manufacturing. However, lithography imaging is an extremely complex nonlinear system, and obtaining suitable process conditions requires extensive experimental attempts. This severely creates a bottleneck in optimizing and controlling the lithographic process conditions. Herein, we report a process optimization solution for a contact layer of metal oxide nanoparticle photoresists by combining electron beam lithography (EBL) experiments with machine learning. In this solution, a long short-term memory (LSTM) network and a support vector machine (SVM) model are used to establish the contact hole imaging and process condition classification models, respectively. By combining SVM with the LSTM network, the process conditions that simultaneously satisfy the requirements of the contact hole width and local critical dimension uniformity tolerance can be screened. The verification results demonstrate that the horizontal and vertical contact widths predicted by the LSTM network are highly consistent with the EBL experimental results, and the classification model shows good accuracy, providing a reference for process optimization of a contact layer.

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“…Nowadays, machine learning methods are gradually being applied to the field of lithography, in which the most widely used is the multilayer feed-forward neural network (MFNN). At present, researchers have proposed process optimization approaches 5,21,[29][30][31][32][33] based on MFNN to obtain matching process conditions. Nevertheless, the MFNN model requires a large number of data sets to achieve high-precision predictive performance.…”

Section: Introductionmentioning

confidence: 99%