A uniform raised-salicide technology has been investigated using both uniform selective-epitaxial-growth (SEG) silicon and salicide films, to reduce a junction leakage current of shallow source/drain (S/D) regions for high-performance CMOS devices. The uniform SEG-Si film without pits is formed by using a wet process, which is a carbon-free oxide removal only using a dilute hydrofluoric acid (DHF) dipping, prior to the Si-SEG process. After a titanium-salicide formation using a conventional two-step salicide process, this uniform SEG-Si film achieves good S/D junction characteristics. The uniform titanium-salicide film without bowing into a silicon is formed by a smaller Ti/SEG-Si thickness ratio, which results in a low sheet resistance of 5 Ω/sq. without a narrow-line effect. Furthermore, the drive current is maximized by this raised-salicide film using a Ti/SEG-Si thickness ratio of 1.0.

A method to predict lightning by machine learning analysis of atmospheric electric fields is proposed for the first time. In this study, we calculated an anomaly score with long short-term memory (LSTM), a recurrent neural network analysis method, using electric field data recorded every second on the ground. The threshold value of the anomaly score was defined, and a lightning alarm at the observation point was issued or canceled. Using this method, it was confirmed that 88.9% of lightning occurred while alarming. These results suggest that a lightning prediction system with an electric field sensor and machine learning can be developed in the future.