No abstract

Single event effect(SEE) is an important problem in the reliability research of integrated circuits. The study of SEE of traditional MOSFET devices is mainly based on simulation software, which is characterized by slow simulation speed, large computation and time-consuming. In this paper, a single event effect research method based on deep learning (DL) is proposed. The method relies on 28-nm MOSFET. The complete drain transient current pulse, transient current peak value and total collected charge can be obtained in a short time by inputting relevant parameters that affect the SEE. The accuracy of the network for predicting transient current peak and total collected charge is 96.95% and 97.53% respectively, and the mean goodness of fit of the network for predicting the drain transient current pulse curve is 0.985. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.89×103 and 1.50×103 times respectively. This method has good prediction effect and provides a new possibility for the study of single event effect.

Most of the traditional studies based on single event effects (SEEs) favor the analysis of electrical mechanisms of semiconductor devices. Professional simulation software in microelectronics requires researchers to have a solid knowledge of microelectronics theory, and the modeling threshold of the software is relatively high, the simulation speed is slow, and accurate simulation of inter‐particle and particle–material interactions is lacking. SEEs are related to linear energy transfer (LET), in this paper, a method is proposed to obtain LET datas to predict SEEs of particles incident on silicon materials by using the Geant4 Monte Carlo toolkit in combination with a dense convolutional network to accurately and rapidly estimate the energy deposition characteristics of the particles. The proposed network structure has a high prediction accuracy with a mean square error （MSE） of only 1.77 × 10−4. Compared with Geant4, which takes 1 min to compute a set of data, the proposed network structure takes only 0.0817 s. The method explores the feasibility of using Geant4 to model semiconductor devices combined with deep learning algorithms, providing a new research perspective for the prediction of microelectronic devices and making it possible to explore the influence of integrated circuits by SEEs.

Determining the typical area of karst development in China would provide positive reference for theoretical research of engineering geology in karst areas in China. By quantitative or semi-quantitative analyzing on the distribution characteristics, climatic conditions and formation lithology of carbonate rocks in China, regional difference characteristics of karst development in carbonate rock regions in China is evaluated, and finnally the representative karst zone in China is reasonably and theoretically delineated and demonstrated. Results show that: the pure Carbonate rocks in southwest of China in tropical and subtropical climate zone which are very favorable for karst development, distributes continuously and massively, and therefore can be considered as the most typical karst area,. this is also a very explanation that karst morphology develops completely. Especially, karst in the zone including Guangxi Zhuang Autonomous Region, southeast and north-east of Guizhou province, and southeastern of Chongqing Municipality, is the most representative karst region in southwest of china because of its the purest carbonate rocks.

It is common to cause errors in the selection of building foundation in karst terrain. The root reason is that the practical difficulty of karst treatment in foundation cannot be reasonably reflected by the existing evaluation of karst degree. Taking New Terminal of Nanning Wuxu International Airport as an engineering case, this article analyzes the limitation of the existing evaluation indices of karst degree, demonstrates the advanced rationality of intensity dissolution layer’s thickness as the evaluation index of karst degree, and proposes the recommended standard of the evaluation of karst degree. Results show that the intensity dissolution layer, divided by dissolution ratio distribution curve with depth in foundations, comprehensively considers the main depth range of dissolution degree of rock surface and the developing of cavern dissolution, and its distribution characteristics can reflect the most complex depth range of karst foundation. As the evaluation index of karst degree, it is reasonable. Its recommended values are suggested as follows, When the thickness of an intensity dissolution layer is less than 3.0 m, more than or equal to 3.0 m and less than 6.0 m and more than or equal to 6.0 m, the karst degree can be determined as weak development degree, medium development degree and strong development degree.