پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 687 |
| تعداد مقالات | 9,985 |
| تعداد مشاهده مقاله | 70,957,069 |
| تعداد دریافت فایل اصل مقاله | 62,422,382 |
Modeling and Simulation of Pc-ZnO TFTs using AI/ML Techniques | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 5، شماره 3 - شماره پیاپی 21، دی 2025، صفحه 37-44 اصل مقاله (836.89 K) | ||
| نوع مقاله: Research Article | ||
| 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 | ||
|
[1] N. Kawasaki, “Parametric study of thermal and chemical nonequilibrium nozzle flow,” M.S. thesis, Dept. Electron. Eng., Osaka Univ., Osaka, Japan, 1993.
[2] N. Kumar, V. Rajakumari, R. P. Padhy, S. Routray, and K. P. Pradhan, ‘A machine learning approach for optimizing and accurately predicting of performance parameters for stacked nanosheet transistor’, Physica Scripta, vol. 99, no. 4, p. 046001, Mar. 2024.
[3] C. R. Kagan and P. Andry, Thin-film transistors. CRC Press, 2003.
[4] K. Nomura, H. Ohta, A. Takagi, T. Kamiya, M. Hirano, and H. Hosono, “Room temperature fabrication of transparent flexible thin-film transistors using amorphous oxide semiconductors,” Nature, vol. 432, no. 7016, p. 488, 2004.
[5] N. H. NEvgeniiickel and Terukov, Zinc Oxide—A Material for Micro- and Optoelectronic Applications: Proceedings of the NATO Advanced Research Workshop, St. Petersburg, Russia, 23–25 June 2004, vol. 194. Springer, 2006.
[6] T. Hirao et al., “Bottom-Gate Zinc Oxide Thin-Film Transistors (Pc-ZnO TFTs) for AM-LCDs,” IEEE Trans. Electron Devices, vol. 55, no. 11, pp. 3136–3142, Nov. 2008.
[7] R. A. Street, “Thin-film transistors,” Adv. Mater., vol. 21, no. 20, pp. 2007–2022, 2009.
[8] Y. Liu, Y. Wang, J. Zhang, “New machine learning algorithm: Random Forest,” in Int. Conf. Information Computing and Applications, Springer, Berlin/Heidelberg, Germany, 2012, pp. 246–252.
[9] M. Estrada et al., “Temperature dependence of the electrical characteristics of low temperature processed zinc oxide thin film transistors,” Thin Solid Films, vol. 573, pp. 18–21, 2014.
[10] E. Ozer et al., “A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate,” Nature Electronics, vol. 3, no. 7, pp. 419–425, 2020.
[11] R. Butola, Y. Li, and S. R. Kola, “A comprehensive technique based on machine learning for device and circuit modeling of gate-all-around nanosheet transistors,” IEEE Open Journal of Nanotechnology, vol. 4, pp. 247–255, Nov. 2023.
[12] S. Choi et al., “Automatic Prediction of MOSFET Threshold Voltage Using Machine Learning Algorithm,” Adv. Intell. Syst., vol. 5, no. 1, 2023.
[13] N. Soufyane, Elaboration, characterization and simulation of thin film transistors based on Zinc Oxide, Ph.D. dissertation, Univ. Mohamed Khider–Biskra, Algeria, 2023.
[14] A. P. Singh, R. K. Baghel, and S. Tirkey, “Integration of Machine Learning with Statistical Variation Analysis for Ferroelectric Transistor (FE-MOSFETs),” Mater. Open, vol. 02, p. 2440001, Jan. 2024.
[15] B. B. Kumar, S. Kumar, P. K. Tiwari, “Performance Investigation of Bottom Gate Pc-ZnO Based TFT for High-Speed Digital Display Circuit Applications,” Feb. 2024.
[16] B. S. Sannakashappanavar et al., “Study of Electrical Characteristics with Different Channel Lengths of Bottom Gate Oxide Semiconductor-Based TFT,” IEEE SILCON 2024, pp. 1–6, 2024.
[17] Sumit Vyas, A.D.D. Dwivedi, Rajeev Dhar Dwivedi, “Effect of gate dielectric on the performance of ZnO based thin film transistor, Superlattices and Microstructures Vol. 120, pp. 223–234, (2018).
[18] R. C. Rajan et al., “A hybrid machine learning model to predict reconfigurable field-effect transistor characteristics with limited dataset,” Semicond. Sci. Technol., vol. 40, no. 7, 075011, 2025.
[19] M. Chankla et al., “Demonstration of accurate ID-VG characteristics modeling in SiC MOSFETs using separated artificial neural networks with small training dataset,” Sci. Rep., vol. 15, no. 1, p. 18941, May 2025.
[20] R. Ghoshhajra et al., “Performance evaluation of junctionless accumulation-mode FinFET using TCAD-enabled deep learning approach,” J. Korean Phys. Soc., Aug. 2025.
[21] S. Khani, P. Rezaei, and M. Rahmanimanesh, “Machine learning analysis of a Fano resonance based plasmonic refractive index sensor using U-shaped resonators,” Sci. Rep., vol. 15, p. 23857, 2025. https://doi.org/10.1038/s41598-025-08508-y
[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
| ||
|
آمار تعداد مشاهده مقاله: 127 تعداد دریافت فایل اصل مقاله: 122 |
||