Review Article
Open Access
Electroluminescence Imaging–Driven Software Systems for Solar Cell Defect Detection
1
Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
2
Kakatiya University, Hanamkonda, Telangana, India
3
Department of Computer and Information Technology Services Administration and Management, Hellenic American University, Athens 10680, Greece
* Corresponding Author:
Rehana Ghulam,
[email protected]
Volume 2, Issue 2
2026
Received: 07 January 2026
Accepted: 12 March 2026
Published: 22 April 2026
Article Information
Abstract
Electroluminescence imaging is widely used for detecting defects in solar cells. It reveals electrically active damage that remains invisible under conventional optical inspection. Most existing studies apply machine learning models to classify electroluminescence images and report performance mainly through accuracy scores. Inspection is often treated as an isolated prediction task, while physical defect mechanisms, sensing variability, representation bias, decision risk, and deployment constraints receive limited attention. As a result, strong benchmark results may not translate into reliable inspection outcomes in manufacturing environments. This paper presents a conceptual, non-systematic framework that reframes electroluminescence inspection as a layered system rather than an isolated prediction task. The analysis synthesizes existing research through this structured lens to identify recurring blind spots and system level misalignments. The objective is not to conduct a systematic review or empirical benchmarking study, but to provide a conceptual foundation for positioning automated inspection within a broader physical and operational context.
Graphical Abstract
Keywords
electroluminescence imaging
solar cell inspection
defect semantics
automated inspection systems
representation learning
decision-making under uncertainty
photovoltaic quality assurance
system-level analysis
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Ghulam, R., Ramay, M. F. A., Mukhtar, A., Kainat, Farid, S., Fatima, N., & Syed, K. (2026). Electroluminescence Imaging–Driven Software Systems for Solar Cell Defect Detection. ICCK Journal of Software Engineering, 2(2), 85–101. https://doi.org/10.62762/JSE.2026.195385
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TY - JOUR AU - Ghulam, Rehana AU - Ramay, Muhammad Fahad Amin AU - Mukhtar, Aiza AU - Kainat AU - Farid, Saba AU - Fatima, Nikhat AU - Syed, Khaleelullah PY - 2026 DA - 2026/04/22 TI - Electroluminescence Imaging–Driven Software Systems for Solar Cell Defect Detection JO - ICCK Journal of Software Engineering T2 - ICCK Journal of Software Engineering JF - ICCK Journal of Software Engineering VL - 2 IS - 2 SP - 85 EP - 101 DO - 10.62762/JSE.2026.195385 UR - https://www.icck.org/article/abs/JSE.2026.195385 KW - electroluminescence imaging KW - solar cell inspection KW - defect semantics KW - automated inspection systems KW - representation learning KW - decision-making under uncertainty KW - photovoltaic quality assurance KW - system-level analysis AB - Electroluminescence imaging is widely used for detecting defects in solar cells. It reveals electrically active damage that remains invisible under conventional optical inspection. Most existing studies apply machine learning models to classify electroluminescence images and report performance mainly through accuracy scores. Inspection is often treated as an isolated prediction task, while physical defect mechanisms, sensing variability, representation bias, decision risk, and deployment constraints receive limited attention. As a result, strong benchmark results may not translate into reliable inspection outcomes in manufacturing environments. This paper presents a conceptual, non-systematic framework that reframes electroluminescence inspection as a layered system rather than an isolated prediction task. The analysis synthesizes existing research through this structured lens to identify recurring blind spots and system level misalignments. The objective is not to conduct a systematic review or empirical benchmarking study, but to provide a conceptual foundation for positioning automated inspection within a broader physical and operational context. SN - 3069-1834 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Ghulam2026Electrolum,
author = {Rehana Ghulam and Muhammad Fahad Amin Ramay and Aiza Mukhtar and Kainat and Saba Farid and Nikhat Fatima and Khaleelullah Syed},
title = {Electroluminescence Imaging–Driven Software Systems for Solar Cell Defect Detection},
journal = {ICCK Journal of Software Engineering},
year = {2026},
volume = {2},
number = {2},
pages = {85-101},
doi = {10.62762/JSE.2026.195385},
url = {https://www.icck.org/article/abs/JSE.2026.195385},
abstract = {Electroluminescence imaging is widely used for detecting defects in solar cells. It reveals electrically active damage that remains invisible under conventional optical inspection. Most existing studies apply machine learning models to classify electroluminescence images and report performance mainly through accuracy scores. Inspection is often treated as an isolated prediction task, while physical defect mechanisms, sensing variability, representation bias, decision risk, and deployment constraints receive limited attention. As a result, strong benchmark results may not translate into reliable inspection outcomes in manufacturing environments. This paper presents a conceptual, non-systematic framework that reframes electroluminescence inspection as a layered system rather than an isolated prediction task. The analysis synthesizes existing research through this structured lens to identify recurring blind spots and system level misalignments. The objective is not to conduct a systematic review or empirical benchmarking study, but to provide a conceptual foundation for positioning automated inspection within a broader physical and operational context.},
keywords = {electroluminescence imaging, solar cell inspection, defect semantics, automated inspection systems, representation learning, decision-making under uncertainty, photovoltaic quality assurance, system-level analysis},
issn = {3069-1834},
publisher = {Institute of Central Computation and Knowledge}
}
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