Improving Model Flexibility of Electrical Characteristic Prediction of a-IGZO-TFTs

This paper presents a lightweight and flexible artificial neural network (ANN) model for predicting the transfer characteristics of amorphous Indium Gallium Zinc Oxide (a-IGZO) thin-film transistors (TFTs). Traditional Technology Computer-Aided Design (TCAD) simulations, while accurate, are computationally intensive and inflexible to rapid design variations. Prior efforts using variational autoencoders (VAEs) showed promise but were limited by rigid input formats that required retraining for any changes in curve dimension or voltage range. To overcome this, we propose an ANN architecture that treats gate voltage as a dynamic input, enabling continuous and accurate predictions across a wide voltage spectrum without the need for retraining. Experimental evaluation through 5-fold cross-validation confirms the ANN’s competitive performance, achieving an average ℝ² score of 0.9898, outperforming VAE models in flexibility and robustness. This approach offers a fast, accurate, and hardware-efficient alternative for a-IGZO TFT modeling and optimization, facilitating the monolithic 3D (M3D) integration and the next generation of display.