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Modeling and Simulation of Pc-ZnO TFTs using AI/ML Techniques | ||
| Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 5، شماره 3 - شماره پیاپی 21، دی 2025، صفحه 37-44 اصل مقاله (1.36 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2025.39015.1228 | ||
| نویسندگان | ||
| Saketh Srikar Reddy؛ Ayush kumar Tiwari؛ Arava Naveen؛ Arun Dev Dhar Dwivedi* | ||
| Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India. | ||
| تاریخ دریافت: 21 شهریور 1404، تاریخ بازنگری: 10 مهر 1404، تاریخ پذیرش: 17 آبان 1404 | ||
| چکیده | ||
| This work develops a machine learning-based model to accurately predict the electrical characteristics of Polycrystalline Zinc Oxide Thin-Film Transistors (Pc-ZnO TFTs). A Random Forest regression model is trained using combined data from multiple drain current versus gate voltage ( ) and drain current versus drain voltage ( ) s1weeps, capturing the complex nonlinear behavior of the device. The model achieves high accuracy, with prediction errors below 1% in most cases, and is validated through comparisons with TCAD-simulated I–V characteristics. The full current–voltage (I–V) curves in forward voltage sweeps are predicted well, with high R-squared values of 0.9938 for and 0.9953 for . This method can replace traditional compact models, which often struggle to capture the variability of Pc-ZnO TFTs, by providing a fast, reliable, and scalable modeling approach. Moreover, the model can be integrated into circuit simulators such as SPICE via Verilog for device- and circuit-level simulations. This study highlights the potential of machine learning techniques to advance compact modeling and support the development of next-generation electronic displays and flexible devices. | ||
| کلیدواژه‌ها | ||
| Pc-ZnO TFTs؛ Characterization؛ Machine Learning (ML)؛ Random Forest Regression | ||
| مراجع | ||
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[22] Shubham Dadhich, D. D. Dwivedi, and A. K. Singh, “Fabrication, Characterization, Numerical Simulation and Compact Modeling of P3HT based Organic Thin Film Transistors (OTFTs)” Journal of Semiconductors, Vol. 42, Issue 7, 074102, (2021).
[23]D.D. Dwivedi, S. K. Jain, Rajeev Dhar Dwivedi, and Shubham Dadhich, "Numerical Simulation and Compact Modeling of Low Voltage Pentacene Based OTFTs" Journal of Science: Advanced Materials and Devices, Vol. 4, Issue 4, pp. 561-567, December 2019.
[24] A.D.D. Dwivedi, S. K. Jain, Rajeev Dhar Dwivedi and Shubham Dadhich "Numerical simulation and compact modeling of thin film transistors for flexible electronics" in INTECK book on Hybrid Nanomaterials: Flexible Electronics Materials, June, (2020), INTECK, UK, ISBN: 978-1-83880-338-4, DOI: 10.5772/intechopen.90301.
[25] K. Singh, Kadiyam Anusha, and A. D. D. Dwivedi, “Enhancing Device Characteristics of Pentacene-Based Organic Transistors through Graphene Integration: A Simulation Study and Performance Analysis,” AIP Advances, Vol.14, 085113 (2024).
[26] Kadiyam Anusha and A. D. D. Dwivedi, “Comparative Study of DNTT-Based Low-Voltage BGBC, BGTC, TGBC and TGTC Configurations of OTFTs in book: Organic Electronics - From Fundamentals to Applications” published by IntechOpen UK (2024). DOI: 10.5772/intechopen.1006308.
[27] Kadiyam Anusha and Arun Dev Dhar Dwivedi “Numerical simulation and performance analysis of amorphous zinc oxynitride thin film transistor (a-ZnON TFT) for large area display application” Results in Engineering, 26, June 2025, 105294, DOI: 10.1016/j.rineng.2025.105294 .
[28] Kadiyam Anusha and Arun Dev Dhar Dwivedi “Review and analysis on numerical simulation and compact modeling of InGaZno thin-film transistor for display SENSOR applications” Measurement Sensors, 36(12):101391, November 2024. DOI: 1016/j.measen.2024.101391
[29] Shubham Dadhich, Arun Dev Dhar Dwivedi, and Ram Babu Pareek “Numerical Simulation and Physics-Based Modelling of Ph-BTBT-C10-Based Organic Thin Film Transistor” In book: Organic Electronics - From Fundamentals to Applications, April 2025, InteckOpen UK, DOI: 5772/intechopen.1009904
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