Selenium-alloyed tellurium oxide for amorphous p-channel transistors.

Compared to polycrystalline semiconductors, amorphous semiconductors offer inherent cost-effective, simple and uniform manufacturing. Traditional amorphous hydrogenated Si falls short in electrical properties, necessitating the exploration of new materials. The creation of high-mobility amorphous n-type metal oxides, such as a-InGaZnO (ref. 1), and their integration into thin-film transistors (TFTs) have propelled advancements in modern large-area electronics and new-generation displays2-8. However, finding comparable p-type counterparts poses notable challenges, impeding the progress of complementary metal-oxide-semiconductor technology and integrated circuits9-11. Here we introduce a pioneering design strategy for amorphous p-type semiconductors, incorporating high-mobility tellurium within an amorphous tellurium suboxide matrix, and demonstrate its use in high-performance, stable p-channel TFTs and complementary circuits. Theoretical analysis unveils a delocalized valence band from tellurium 5p bands with shallow acceptor states, enabling excess hole doping and transport. Selenium alloying suppresses hole concentrations and facilitates the p-orbital connectivity, realizing high-performance p-channel TFTs with an average field-effect hole mobility of around 15 cm2 V-1 s-1 and on/off current ratios of 106-107, along with wafer-scale uniformity and long-term stabilities under bias stress and ambient ageing. This study represents a crucial stride towards establishing commercially viable amorphous p-channel TFT technology and complementary electronics in a low-cost and industry-compatible manner.

Physics-Informed, Data-Driven Model for Atmospheric Corrosion of Carbon Steel Using Bayesian Network.

Atmospheric corrosion is a significant challenge faced by the aviation industry as it considerably affects the structural integrity of an aircraft operated for long periods. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, practical application of the deterioration model is challenging owing to the limited data available for the parameter estimation. Thus, a high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and the monitoring data on a Bayesian network (BN) is presented herein. Atmospheric corrosion is modeled using the simulation method, and a BN is constructed using GeNie. Moreover, model calibration is performed using the monitoring data collected from aircraft parking areas. The calibration approach is an improvement over existing models as it incorporates actual environmental data, making it more accurate and applicable to real-world scenarios. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion on carbon steel. The study results open new avenues for future research, such as the incorporation of additional data sources to further improve the accuracy of corrosion models.