Sheet resistance prediction of laser induced graphitic carbon with transformer encoder-enabled contrastive learning

Yupeng Wei (), Gerd Grau () and Dazhong Wu ()
Additional contact information
Yupeng Wei: San Jose State University
Gerd Grau: York University
Dazhong Wu: University of Central Florida

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 24, 1983-1997

Abstract: Abstract Accurately predicting the sheet resistance of laser-induced graphitic carbon (LIGC) is crucial for optimizing process conditions and designing high-performance LIGC-based devices. However, identifying the most significant process parameter for predicting the sheet resistance of LIGC is challenging. In addition, training an accurate model with a small dataset remains a challenge. To address the first issue, a novel transformer encoder with a self-attention mechanism is introduced to determine the most and least significant process parameters affecting the sheet resistance of LIGC. To address the second issue, a contrastive learning method is developed to augment a small training dataset. Unlike conventional deep learning approaches that establish a direct relationship between process parameters and sheet resistance, the proposed method can learn the relationship between the difference in features extracted from process parameter settings and the difference in the corresponding sheet resistances. Experimental results have demonstrated that the proposed transformer encoder-enabled contrastive learning method accurately predicted the sheet resistance of LIGC and outperformed other machine learning methods.

Keywords: Laser induced graphitic carbon; Sheet resistance; Contrastive learning; Transformer; Deep learning (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02333-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02333-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02333-2

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().