Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

Hu, Yifan; Wang, Fucheng; Chen, Jingwen; Dhungel, Suresh Kumar; Li, Xinying; Song, Jang-Kun; Kim, Yong-Sang; Pham, Duy Phong; Yi, Junsin

doi:10.3390/electronics12143139

Open AccessArticle

Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

by

Yifan Hu

¹,

Fucheng Wang

¹,

Jingwen Chen

¹,

Suresh Kumar Dhungel

²,

Xinying Li

³,

Jang-Kun Song

¹,

Yong-Sang Kim

¹

,

Duy Phong Pham

^1,*

and

Junsin Yi

^2,*

¹

Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

²

College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

³

Department of Surface Interior Design, Hanyang University, Ansan 15588, Republic of Korea

^*

Authors to whom correspondence should be addressed.

Electronics 2023, 12(14), 3139; https://doi.org/10.3390/electronics12143139

Submission received: 26 June 2023 / Revised: 12 July 2023 / Accepted: 17 July 2023 / Published: 19 July 2023

(This article belongs to the Section Electronic Materials, Devices and Applications)

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Abstract

The production and optimization of HfAlO-based charge trapping memory devices is central to our research. Current optimization methods, based largely on experimental experience, are tedious and time-consuming. We examine various fabrication parameters and use the resulting memory window data to train machine learning algorithms. An optimized Support Vector Regression model, processed using the Swarm algorithm, is applied for data prediction and process optimization. Our model achieves a MSE of 0.47, an R² of 0.98856, and a recognition accuracy of 90.3% under cross-validation. The findings underscore the effectiveness of machine learning algorithms in non-volatile memory fabrication process optimization, enabling efficient parameter selection or outcome prediction.

Keywords: high-k material; support vector regression; swarm intelligence; memory device

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MDPI and ACS Style

Hu, Y.; Wang, F.; Chen, J.; Dhungel, S.K.; Li, X.; Song, J.-K.; Kim, Y.-S.; Pham, D.P.; Yi, J. Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices. Electronics 2023, 12, 3139. https://doi.org/10.3390/electronics12143139

AMA Style

Hu Y, Wang F, Chen J, Dhungel SK, Li X, Song J-K, Kim Y-S, Pham DP, Yi J. Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices. Electronics. 2023; 12(14):3139. https://doi.org/10.3390/electronics12143139

Chicago/Turabian Style

Hu, Yifan, Fucheng Wang, Jingwen Chen, Suresh Kumar Dhungel, Xinying Li, Jang-Kun Song, Yong-Sang Kim, Duy Phong Pham, and Junsin Yi. 2023. "Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices" Electronics 12, no. 14: 3139. https://doi.org/10.3390/electronics12143139

APA Style

Hu, Y., Wang, F., Chen, J., Dhungel, S. K., Li, X., Song, J.-K., Kim, Y.-S., Pham, D. P., & Yi, J. (2023). Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices. Electronics, 12(14), 3139. https://doi.org/10.3390/electronics12143139

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Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

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