Gaining an understanding the dynamic behaviors of dopant atoms in silicon nanowires (SiNWs) is the key to achieving low-power and high-speed transistor devices using SiNWs. The segregation behavior of boron (B) and phosphorus (P) atoms in B- and P-doped SiNWs during thermal oxidation was closely observed using B local vibrational peaks and Fano broadening in optical phonon peaks of B-doped SiNWs by micro-Raman scattering. Electron spin resonance (ESR) signals from conduction electrons were used for P-doped SiNWs. Our results showed that B atoms preferentially segregate in the surface oxide layer, whereas P atoms tend to accumulate in the Si region around the interface of SiNWs. The radial distribution of P atoms in SiNWs was also investigated to prove the difference segregation behaviors between of P and B atoms.

Recrystallization of silicon nanowires (SiNWs) after ion implantation strongly depends on the ion doses and species. Full amorphization by high-dose implantation induces polycrystal structures in SiNWs even after high-temperature annealing, with this tendency more pronounced for heavy ions. Hot-implantation techniques dramatically suppress polycrystallization in SiNWs, resulting in reversion to the original single-crystal structures and consequently high reactivation rate of dopant atoms. In this study, the chemical bonding states and electrical activities of implanted boron and phosphorus atoms were evaluated by Raman scattering and electron spin resonance, demonstrating the formation of p- and n-type SiNWs.

In doped Si nanowires (SiNWs) boron (B) atoms segregate to the surface oxide layers during thermal oxidation, while phosphorus (P) atoms preferentially pile up in Si crystalline regions close to the Si/SiO2 interface. Here we report on micro-Raman scattering and electron spin resonance (ESR) measurements showing that B atoms can be stabilized at the crystalline Si core region in codped SiNWs with average diameters of 20–30 nm because of the strong interaction between B and P atoms during thermal oxidation below 800 °C. Theoretical calculation clearly demonstrated the effect of B–P pairing, which can stabilize the B atoms in the Si side. In the B–P pairing configuration, dopant passivationbeyond simple compensationoccurs, making the impurities electrically inactive.

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Background: Computational simulation using mathematical models is a useful method for understanding the complex behavior of a living system. The majority of studies using mathematical models to reveal biological mechanisms use one of the two main approaches: the bottom-up or the top-down approach. When we aim to analyze a large-scale network, such as a comprehensive knowledge-integrated model of a target phenomenon, for example a whole-cell model, the variation of analyses is limited to particular kind of analysis because of the size and complexity of the model. Results: To analyze a large-scale network of neural differentiation, we developed a hybrid method that combines both approaches. To construct a mathematical model, we extracted network motifs, subgraph structures that recur more often in a metabolic network or gene regulatory network than in a random network, from a large-scale network, detected regulatory motifs among them, and combined these motifs. We confirmed that the model reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale network is controlled by a combination of four feedback loops, including a novel large loop discovered in this study. Conclusion: The model extracted by our hybrid method has the potential to reveal the critical mechanisms of neural differentiation. The hybrid method is applicable to other biological events.