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The increase of IC design complexity and the continuous reduction of process feature size bring new severe challenges to static timing analysis (STA) and chip design cycle. In order to improve the efficiency of STA and shorten the chip design cycle, this paper fully considers the FinFET process characteristics and the principle of STA, and predicts the timing characteristics of another part of corners by introducing machine learning methods based on the timing characteristics of some corners. The experiment is based on an industrial design, and the results show that the proposed method uses 5 corners to predict the timing of other 31 corners, which can achieve an average absolute error of less than 2 ps, far better than the 21 process angles required by traditional methods. Thus, the proposed method significantly improves the prediction accuracy and significantly reduces the workload of static time series analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of integrated circuit technology, the size of devices continues to shrink. When the channel of the field effect transistor is shortened to 22 nm, the traditional planar field effect transistor no longer meets the development needs. FinFET is a new type of three-dimensional device, which is currently widely researched and applied due to its good performance. It mainly introduces the basic structure and basic process flow of FinFET devices, as well as some improved FinFET device structures developed on the basic structure. Finally, combined with the reality, the future development of FinFET device structure is expected.