Machine Learning-Assisted Performance Prediction of ZnMSnO-based Thin-Film Transistors

Amorphous oxide semiconductors (AOS) are highly promising for optoelectronic devices due to their exceptional electrical properties and optical transparency. However, a significant barrier to their development is the lack of a comprehensive materials database, which hinders systematic studies and the discovery of novel AOS materials. This study addresses this gap by constructing a dedicated database of zinc-tin-based doped oxide semiconductors (Zn-M-Sn-O) and their thin-film transistor (TFT) performance parameters, compiled from an extensive review of existing literature. Our research aims to perform a systematic analysis of the correlations between key material properties and device performance. We first summarize and analyze existing Zn-M-Sn-O based optoelectronic devices, extracting key features such as material compositions, processing parameters, and performance metrics. These features are then used to construct feature vectors. By applying a machine learning algorithm to this dataset, we establish a performance prediction model for Zn-M-Sn-O TFTs. This machine learning-assisted approach allows us to efficiently screen materials and predict the optimal M element for high-performance devices. This methodology significantly accelerates the discovery and development of advanced AOS materials, paving the way for next-generation optoelectronic technologies.