Role of Reinforcement Learning in Improvement of Semiconductor Doping

Year : 2025 | Volume : 12 | Issue : 02 | Page : 23-34
    By

    Subha Sri Telkar,

  • Manas Kumar Yogi,

  1. Research Scholar, Electronics and Communication Engineering Department, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Computer Science and Engineering Department, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_195225’);});Edit Abstract & Keyword

The semiconductor industry faces increasing challenges in achieving optimal doping profiles as device dimensions shrink and performance requirements intensify. Traditional doping optimization methods, while effective, often struggle with the complex, multi-dimensional parameter spaces characteristic of modern semiconductor manufacturing. This study explores the transformative role of reinforcement learning (RL) in improving semiconductor doping processes, examining how RL algorithms can autonomously optimize doping parameters to enhance device performance, reduce manufacturing costs, and improve yield rates. Through systematic analysis of current applications, methodologies, and emerging trends, this research demonstrates that reinforcement learning offers significant advantages in addressing the intricate challenges of semiconductor doping optimization, promising to revolutionize manufacturing processes in the era of advanced node technologies.

Keywords: Reinforcement learning, dopant, optical, circuits, electronics

[This article belongs to Journal of Semiconductor Devices and Circuits ]

How to cite this article:
Subha Sri Telkar, Manas Kumar Yogi. Role of Reinforcement Learning in Improvement of Semiconductor Doping. Journal of Semiconductor Devices and Circuits. 2025; 12(02):23-34.
How to cite this URL:
Subha Sri Telkar, Manas Kumar Yogi. Role of Reinforcement Learning in Improvement of Semiconductor Doping. Journal of Semiconductor Devices and Circuits. 2025; 12(02):23-34. Available from: https://journals.stmjournals.com/josdc/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_195225’);});Edit

References

  1. Adams J, et al. Safe reinforcement learning for critical manufacturing processes. J Manuf Syst. 2023; 71: 145–62.
  2. Anderson R, et al. Multi-objective optimization in semiconductor processing using advanced machine learning. IEEE Trans Semicond Manuf. 2023; 36(3): 287–301.
  3. Bennett S, Morgan P. Skills requirements for AI implementation in manufacturing: A semiconductor industry perspective. Manuf Eng. 2023; 165(4): 78–85.
  4.  Brown K, Davis L. Reinforcement learning applications in industrial automation: Current status and future prospects. Ind AI Rev. 2024; 12(2): 234–51.
  5.  Campbell M, Harris D. Explainable AI for manufacturing process control: Challenges and solutions. AI Manuf. 2024; 8(1): 45–62.
  6.  Chen W, et al. Deep reinforcement learning for ion implantation optimization in advanced technology nodes. Nat Electron. 2024; 7(4): 312–25.
  7.  Clark P, et al. Federated learning approaches for collaborative manufacturing optimization. IEEE Trans Ind Inform. 2024; 20(6): 4567–80.
  8.  Davis A, Wilson R. Transfer learning for manufacturing process optimization: Methods and applications. J Intell. Manuf. 2024; 35(8): 2145–62.
  9.  Dixon T, Scott M. Privacy-preserving machine learning in semiconductor manufacturing. IEEE Secur Priv. 2024; 22(3): 67–75.
  10.  Evans L, et al. Integration challenges in smart manufacturing systems: A case study approach. Manuf Syst Eng. 2024; 18(2): 112–28.
  11. Foster G, Lewis H. Economic analysis of AI adoption in semiconductor manufacturing. Semicond Econ. 2024; 29(1): 23–37.
  12.  Garcia M, Thompson J. Comparative performance analysis of ML optimization methods in semiconductor processing. Adv Manuf Technol. 2023; 127(5): 1823–41.
  13.  Henderson K, et al. Performance metrics for AI-driven manufacturing systems: A comprehensive framework. Int J Prod Res. 2024; 62(12): 4234–51.
  14. Johnson B, Lee C. Real-time process control using reinforcement learning: Implementation and results. Control Eng Pract. 2023; 138: 105–18.
  15.  Kumar A, et al. Advanced process parameter optimization in semiconductor manufacturing using deep reinforcement learning. J Manuf Sci Eng. 2024; 146(7): 071005-1–071005-12.
  16.  Liu X, Zhang Y. Markov decision processes for semiconductor manufacturing optimization. Oper Res. 2023; 71(4): 1456–73.
  17.  Martinez C, Singh R. Multi-objective reinforcement learning for manufacturing applications. Artif Intell Eng. 2023; 45: 289–305.
  18. Pietquin O, Geist M, Chandramohan S, Frezza-Buet H. Sample-efficient batch reinforcement learning for dialogue management optimization. ACM Transactions on Speech and Language Processing (TSLP). 2011 Jun 6; 7(3): 1–21.
Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 30/05/2025
Accepted 31/05/2025
Published 26/06/2025
Publication Time 27 Days
129

[first_name] [last_name]

My IP

PlumX Metrics