Simultaneous Improvement of Multiple Electrical Characteristics in Ultrathin a-IGZO TFTs Using Machine Learning Optimization

While IGZO is emerging as a promising channel material to address the scaling limitations of conventional silicon-based DRAM, its application in next-generation 3D DRAM requires further advancements in achieving ultrathin structures and excellent performance tailored to DRAM characteristics. Specifically, optimizing process variables is essential for enhancing mobility in ultrathin structures, where mobility tends to degrade significantly, while maintaining a constant threshold voltage, a task that is both experimentally intensive and resource-demanding. This study employed multi-objective Bayesian optimization (MOBO) machine learning (ML) to simultaneously optimize multiple electrical objectives, aiming to achieve high mobility and a near-zero threshold voltage for ultrathin IGZO thin-film transistors (TFTs) under complex sputtering conditions, involving a wide range of possible combina…