Current - Issue
Review Article
A Comprehensive Review of Reliability Prediction and Enhancement in Transparent Thin-Film Transistors
A. Mani kandan1
Dr. T. Sivakumar2
1 Assistant Professor, Department of Electronics and Communication, Sri Ganesh College of Arts & Science (Autonomous), Salem, Tamilnadu, India. 2 Professor, Department of Electronics, RVS College of Arts & Science (Autonomous), Coimbatore, Tamilnadu, India.
Published Online: July-August 2026
Pages: 233-241
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260704025
References
[1] S. Jeong, J. Kim, and H. J. Kim, “Recent Advances in Oxide Thin-Film Transistors for Flexible and Transparent Electronics,”
Advanced Electronic Materials, vol. 8, no. 3, pp. 1–21, 2022.
[2] T. Kamiya and H. Hosono, “Material Characteristics and Reliability Issues of Amorphous Oxide Semiconductor Thin-Film
Transistors,” NPG Asia Materials, vol. 14, no. 1, pp. 1–15, 2022.
[3] Y. Li, X. Zhang, and J. Wang, “Machine Learning Approaches for Semiconductor Device Reliability Prediction: A Review,”
Microelectronics Reliability, vol. 132, pp. 114–126, 2022.
[4] J. Robertson and R. M. Wallace, “High-k Materials and Reliability Challenges in Advanced Thin Film Devices,” Materials Science
in Semiconductor Processing, vol. 145, pp. 106–118, 2022.
[5] H. Park, S. Lee, and K. Cho, “Deep Learning-Based Degradation Prediction in Oxide Thin Film Transistors Under Bias Stress
Conditions,” Microelectronics Reliability, vol. 140, pp. 114845, 2023.
[6] M. Chen, Y. Liu, and X. Zhao, “LSTM-Based Time-Series Reliability Prediction for Semiconductor Devices,” Engineering
Applications of Artificial Intelligence, vol. 124, pp. 106530, 2023.
[7] R. Singh and P. Sharma, “Artificial Intelligence Techniques for Reliability Assessment of Electronic Devices: A Comprehensive
Review,” Microelectronics Reliability, vol. 143, pp. 114962, 2023.
[8] X. Wang, Y. Huang, and Z. Li, “Physics-Informed Neural Networks for Reliability Modeling of Thin-Film Electronic Devices,”
Applied Soft Computing, vol. 142, pp. 110370, 2024.
[9] S. Kumar, A. Verma, and N. Gupta, “Deep Neural Network Framework for Predicting Electrical Degradation in Flexible Thin
Film Transistors,” Journal of Intelligent Manufacturing, vol. 35, no. 2, pp. 781–796, 2024.
[10] Y. Zhao, J. Chen, and H. Wu, “Data-Driven Reliability Monitoring of Transparent Electronic Devices Using Machine Learning,”
Microelectronics Reliability, vol. 151, pp. 115258, 2024.
[11] A. Ahmed, M. Hassan, and R. Ibrahim, “Explainable Artificial Intelligence for Semiconductor Reliability Prediction,” Expert
Systems with Applications, vol. 252, pp. 124102, 2025.
[12] L. Zhang, P. Li, and H. Zhou, “Digital Twin-Assisted Reliability Prediction for Flexible Electronics Manufacturing,” Robotics
and Computer-Integrated Manufacturing, vol. 89,pp. 102760, 2025.
[13] V. Patel and K. Shah, “Hybrid Deep Learning and Optimization Framework for Lifetime Prediction of Electronic Devices,”
Engineering Applications of Artificial Intelligence, vol. 136, pp. 108970, 2025.
[14] B. Liu, Y. Sun, and J. Wang, “Real-Time Reliability Enhancement of Thin-Film Devices Using Explainable Deep Learning,”
Microelectronics Reliability, vol. 162, pp. 115810, 2026.
[15] H. Chen, X. Wu, and Y. Zhang, “Adaptive Reliability Optimization for Transparent Thin Film Transistors Using Deep Learning
and Evolutionary Algorithms,” Applied Soft Computing, vol. 158, pp. 111642, 2026.
[16] Shen, Y., Zhang, M., He, S., Bian, L., Liu, J., Chen, Z., Xue, S., Zhou, Y., & Yan, Y. (2024). Reliability issues of amorphous
oxide semiconductor-based thin film transistors.Journal of Materials Chemistry C, 12, 13707–13726
[17] Kumar, T. S., Hazarika, A., Hazarika, A., Srinivas, P. S. T. N., Tiwari, P. K., & Kumar, A. (2024). A machine learning approach toaccelerate reliability prediction in nanowire FETs from self-heating perspective. Microelectronics Reliability, 161, 115484
[18] Yuan, C., de Jong, S. D. M., & van Driel, W. D. (2024). AI-assisted Design for Reliability: Review and Perspectives. In Proceedings
of EuroSimE 2024. IEEE.
[19] Choi, S., Jang, Y., Guo, Y., Kim, Y.-S., Song, J.-K., & Yi, J. (2026). Reliability Strategies for a-IGZO Thin-Film Transistors Under
Stress Environments. Transactions on Electrical and Electronic Materials, 27(2), 243–257.
[20] Huzaibi, H. U., Han, S.-T., & Zhang, M. (2026). Model representation in amorphous metal oxide thin-film transistors: a critical
review. npj Flexible Electronics, 10, Article 13.
[21] Rafiee, M., & Saini, P. (2026). Advanced semiconductor packaging design via artificial intelligence and machine learning: A
review. Microelectronics Reliability, 181, 116145.
[22] Rafiee, M., & Saini, P. (2026). Advanced semiconductor packaging design via artificial intelligence and machine learning: A
review. Microelectronics Reliability.
[23] Manoharan, S. (2026). Study on Thin-Film Transistor and Circuit Design Using Artificial Intelligence. Journal of Electronics and
Informatics, 7(4), 298–315.
Advanced Electronic Materials, vol. 8, no. 3, pp. 1–21, 2022.
[2] T. Kamiya and H. Hosono, “Material Characteristics and Reliability Issues of Amorphous Oxide Semiconductor Thin-Film
Transistors,” NPG Asia Materials, vol. 14, no. 1, pp. 1–15, 2022.
[3] Y. Li, X. Zhang, and J. Wang, “Machine Learning Approaches for Semiconductor Device Reliability Prediction: A Review,”
Microelectronics Reliability, vol. 132, pp. 114–126, 2022.
[4] J. Robertson and R. M. Wallace, “High-k Materials and Reliability Challenges in Advanced Thin Film Devices,” Materials Science
in Semiconductor Processing, vol. 145, pp. 106–118, 2022.
[5] H. Park, S. Lee, and K. Cho, “Deep Learning-Based Degradation Prediction in Oxide Thin Film Transistors Under Bias Stress
Conditions,” Microelectronics Reliability, vol. 140, pp. 114845, 2023.
[6] M. Chen, Y. Liu, and X. Zhao, “LSTM-Based Time-Series Reliability Prediction for Semiconductor Devices,” Engineering
Applications of Artificial Intelligence, vol. 124, pp. 106530, 2023.
[7] R. Singh and P. Sharma, “Artificial Intelligence Techniques for Reliability Assessment of Electronic Devices: A Comprehensive
Review,” Microelectronics Reliability, vol. 143, pp. 114962, 2023.
[8] X. Wang, Y. Huang, and Z. Li, “Physics-Informed Neural Networks for Reliability Modeling of Thin-Film Electronic Devices,”
Applied Soft Computing, vol. 142, pp. 110370, 2024.
[9] S. Kumar, A. Verma, and N. Gupta, “Deep Neural Network Framework for Predicting Electrical Degradation in Flexible Thin
Film Transistors,” Journal of Intelligent Manufacturing, vol. 35, no. 2, pp. 781–796, 2024.
[10] Y. Zhao, J. Chen, and H. Wu, “Data-Driven Reliability Monitoring of Transparent Electronic Devices Using Machine Learning,”
Microelectronics Reliability, vol. 151, pp. 115258, 2024.
[11] A. Ahmed, M. Hassan, and R. Ibrahim, “Explainable Artificial Intelligence for Semiconductor Reliability Prediction,” Expert
Systems with Applications, vol. 252, pp. 124102, 2025.
[12] L. Zhang, P. Li, and H. Zhou, “Digital Twin-Assisted Reliability Prediction for Flexible Electronics Manufacturing,” Robotics
and Computer-Integrated Manufacturing, vol. 89,pp. 102760, 2025.
[13] V. Patel and K. Shah, “Hybrid Deep Learning and Optimization Framework for Lifetime Prediction of Electronic Devices,”
Engineering Applications of Artificial Intelligence, vol. 136, pp. 108970, 2025.
[14] B. Liu, Y. Sun, and J. Wang, “Real-Time Reliability Enhancement of Thin-Film Devices Using Explainable Deep Learning,”
Microelectronics Reliability, vol. 162, pp. 115810, 2026.
[15] H. Chen, X. Wu, and Y. Zhang, “Adaptive Reliability Optimization for Transparent Thin Film Transistors Using Deep Learning
and Evolutionary Algorithms,” Applied Soft Computing, vol. 158, pp. 111642, 2026.
[16] Shen, Y., Zhang, M., He, S., Bian, L., Liu, J., Chen, Z., Xue, S., Zhou, Y., & Yan, Y. (2024). Reliability issues of amorphous
oxide semiconductor-based thin film transistors.Journal of Materials Chemistry C, 12, 13707–13726
[17] Kumar, T. S., Hazarika, A., Hazarika, A., Srinivas, P. S. T. N., Tiwari, P. K., & Kumar, A. (2024). A machine learning approach toaccelerate reliability prediction in nanowire FETs from self-heating perspective. Microelectronics Reliability, 161, 115484
[18] Yuan, C., de Jong, S. D. M., & van Driel, W. D. (2024). AI-assisted Design for Reliability: Review and Perspectives. In Proceedings
of EuroSimE 2024. IEEE.
[19] Choi, S., Jang, Y., Guo, Y., Kim, Y.-S., Song, J.-K., & Yi, J. (2026). Reliability Strategies for a-IGZO Thin-Film Transistors Under
Stress Environments. Transactions on Electrical and Electronic Materials, 27(2), 243–257.
[20] Huzaibi, H. U., Han, S.-T., & Zhang, M. (2026). Model representation in amorphous metal oxide thin-film transistors: a critical
review. npj Flexible Electronics, 10, Article 13.
[21] Rafiee, M., & Saini, P. (2026). Advanced semiconductor packaging design via artificial intelligence and machine learning: A
review. Microelectronics Reliability, 181, 116145.
[22] Rafiee, M., & Saini, P. (2026). Advanced semiconductor packaging design via artificial intelligence and machine learning: A
review. Microelectronics Reliability.
[23] Manoharan, S. (2026). Study on Thin-Film Transistor and Circuit Design Using Artificial Intelligence. Journal of Electronics and
Informatics, 7(4), 298–315.
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