Diffusion and electrical properties of Boron and Arsenic doped poly-Si and poly-GexSi1−x(x 0.3)Ge_xSi_1-x(x~0.3) as gate material for sub-0.25 µm complementary metal oxide semiconductor applications

In this paper the texture, morphology, diffusion and electrical (de‐) activation of dopants in polycrystalline GexSi1-x and Si have been studied in detail. For gate doping B+,BF2+ and As+ were used and thermal budgets were chosen to be compatible with deep submicron CMOS processes. Diffusion of dopants is different for GeSi alloys, B diffuses significantly more slowly and As has a much faster diffusion in GeSi. For B doped samples both electrical activation and mobility are higher compared to poly‐Si. Also for the first time, BF2+ data of doped layers are presented, these show the same trend as the B doped samples but with an overall higher sheet resistance. For arsenic doping, activation and mobility are lower compared to poly‐Si, resulting in a higher sheet resistance. The dopant deactivation due to long low temperature steps after the final activation anneal is also found to be quite different. Boron‐doped GeSi samples show considerable reduced deactivation whereas arsenic shows a higher deactivation rate. The electrical properties are interpreted in terms of different grain size, quality and properties of the grain boundaries, defects, dopant clustering, and segregation, and the solid solubility of the dopants

Doped SbTe phase change material in memory cells

Phase Change Random Access Memory (PCRAM) is investigated as replacement for Flash. The memory concept is based on switching a chalcogenide from the crystalline (low ohmic) to the amorphous (high ohmic) state and vice versa. Basically two memory cell concepts exist: the Ovonic Unified Memory (OUM) and the line cell. Switching to the high ohmic or low ohmic state is done using Joule heating. A relatively short (~ns) electrical pulse with large amplitude is used to heat the crystalline phase to melt and quench into the amorphous state (RESET). A pulse with smaller amplitude heats the amorphous region above its crystallization temperature (lower than the melting temperature) and the material returns into the crystalline state (SET). In the OUM cell this will be at the electrode-phase change material contact, whereas for the line cell this will be at the position where the current density is the highest

Correlative transmission electron microscopy and electrical properties study of switchable phase-change random access memory line cells

Phase-change memory line cells, where the active material has a thickness of 15 nm, were prepared for transmission electron microscopy (TEM) observation such that they still could be switched and characterized electrically after the preparation. The result of these observations in comparison with detailed electrical characterization showed (i) normal behavior for relatively long amorphous marks, resulting in a hyperbolic dependence between SET resistance and SET current, indicating a switching mechanism based on initially long and thin nanoscale crystalline filaments which thicken gradually, and (ii) anomalous behavior, which holds for relatively short amorphous marks, where initially directly a massive crystalline filament is formed that consumes most of the width of the amorphous mark only leaving minor residual amorphous regions at its edges. The present results demonstrate that even in (purposely) thick TEM samples, the TEM sample preparation hampers the probability to observe normal behavior and it can be debated whether it is possible to produce electrically switchable TEM specimen in which the memory cells behave the same as in their original bulk embedded state

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations