A neural compact model based on transfer learning for organic FETs with Gaussian disorder

Abstract

We present an approach to adopt deep neural networks for the development of a compact model for transistors, namely a neural compact model, including transfer learning to enhance accuracy and reduce model development time. We examine the effectiveness of this approach when the electrical data for neural networks is scarce and costly and when the electrical characteristics to be modeled are highly non-linear. By using technology computer-aided design simulations, we constructed a dataset of the electrical characteristics of organic field-effect transistors with Gaussian disorder that exhibit highly non-linear current–voltage curves. Subsequently, we developed neural compact models by modifying conventional deep learning models and validated the effectiveness of transfer learning with testing through various experiments. We showed that the neural compact model with transfer learning provides an equivalent accuracy at a significantly shorter training time.