Mechanism of Random Telegraph Noise in 22-nm FDSOI-Based MOSFET at Cryogenic Temperatures.

In the emerging process-based transistors, random telegraph noise (RTN) has become a critical reliability problem. However, the conventional method to analyze RTN properties may not be suitable for the advanced silicon-on-insulator (SOI)-based transistors, such as the fully depleted SOI (FDSOI)-based transistors. In this paper, the mechanism of RTN in a 22-nm FDSOI-based metal-oxide-semiconductor field-effect transistor (MOSFET) is discussed, and an improved approach to analyzing the relationship between the RTN time constants, the trap energy, and the trap depth of the device at cryogenic temperatures is proposed. The cryogenic measurements of RTN in a 22-nm FDSOI-based MOSFET were carried out and analyzed using the improved approach. In this approach, the quantum mechanical effects and diffuse scattering of electrons at the oxide-silicon interface are considered, and the slope of the trap potential determined by the gate voltage relation is assumed to decrease proportionally with temperature as a result of the electron distribution inside the top silicon, per the technology computer-aided design (TCAD) simulations. The fitted results of the improved approach have good consistency with the measured curves at cryogenic temperatures from 10 K to 100 K. The fitted trap depth was 0.13 nm, and the decrease in the fitted correction coefficient of the electron distribution proportionally with temperature is consistent with the aforementioned assumption.

Optimal Modeling of Anti-Breast Cancer Candidate Drugs Based on Graph Model Feature Selection.

Breast cancer is one of the most widespread and fatal cancers in women. At present, anticancer drug-inhibiting estrogen receptor α subtype (ERα) can greatly improve the cure rate for breast cancer patients, so the research and development of this kind of drugs are very urgent. In this paper, the problem of how to screen excellent anticancer drugs is abstracted as an optimization problem. Firstly, the graph model is used to extract low-dimensional features with strong distinguishing and describing ability according to various attributes of candidate compounds, and then, kernel functions are used to map these features to high-dimensional space. Then, the quantitative analysis model of ERα biological activity and the classification model based on ADMET properties of the support vector machine are constructed. Finally, sequential least square programming (SLSQP) is utilized to solve the ERα biological activity model. The experimental results show that for anticancer data sets, compared with principal component analysis (PCA), the error rate of the graph model constructed in this paper is reduced by 6.4%, 15%, and 7.8% on mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE), respectively. In terms of classification prediction, compared with principal component analysis (PCA), the recall and precision rates of this method are enhanced by 19.5% and 12.41%, respectively. Finally, the optimal biological activity value (IC50_nM) 34.6 and inhibitory biological activity value (pIC50) 7.46 were obtained.