Modeling and Simulation of Pc-ZnO TFTs using AI/ML Techniques
نویسندگان
1 Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
2 Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
3 Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
4 Department of Micro and Nano Electronics, School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
doi
10.22075/mseee.2025.39015.1228چکیده
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.
