Machine Learning Strategy for Optimizing Multiple Electrical Characteristics in Dual-Layer Oxide Thin Film Transistors

A machine learning (ML) strategy is suggested to optimize dual-layer oxide thin film transistor (TFTs) performance. In this study, Bayesian optimization (BO), an algorithm recognized for its efficiency in optimizing material design, is applied to guide the design of a channel layer composed of IZO and IGZO. The sputtering fabrication process, which has attracted attention as an oxide semiconductor channel layer deposition method, is fine-tuned using ML to enhance multiple electrical characteristics of transistors: field-effect mobility, threshold voltage, and subthreshold swing. Using BO, the sputtering conditions─plasma power, pressure, and gas ratio, which intricately influence device performance─were modified using 19 data sets of 84 scenarios. It reveals that the modulated process conditions improve field-effect mobility up to 46.7 cm 2 V –1 s –1, achieving more than double the perfo…