A CMOS-Compatible Process for ≥3 kV GaN Power HEMTs on 6-inch Sapphire Using In Situ SiN as the Gate Dielectric.

The application of GaN HEMTs on silicon substrates in high-voltage environments is significantly limited due to their complex buffer layer structure and the difficulty in controlling wafer warpage. In this work, we successfully fabricated GaN power HEMTs on 6-inch sapphire substrates using a CMOS-compatible process. A 1.5 µm thin GaN buffer layer with excellent uniformity and a 20 nm in situ SiN gate dielectric ensured uniformly distributed VTH and RON across the entire 6-inch wafer. The fabricated devices with an LGD of 30 µm and WG of 36 mm exhibited an RON of 18.06 Ω·mm and an off-state breakdown voltage of over 3 kV. The electrical mapping visualizes the high uniformity of RON and VTH distributed across the whole 6-inch wafer, which is of great significance in promoting the applications of GaN power HEMTs for medium-voltage power electronics in the future.

Bi and oxygen defects improved visible light photocatalysis with BiOBr nanosheets.

Bi metal attached BiOBr with oxygen defect (BiOBr(3)-Bi(x%, x = 10, 20, 30)) nanosheets was prepared via the hydrothermal process in this study. The different characterization techniques of x-ray diffraction, x-ray photoelectron spectrometer, electron spin resonance (ESR), field emission scanning electron microscope, and high resolution transmission electron microscope were used to distinguish the composition, crystal structure, and morphology of the samples. Under visible light irradiation, the BiOBr(3)-Bi(x%, x = 10, 20, 30) samples exhibited improved photocatalytic activity for the degradation of colored dyes (RhB) and colorless tetracycline hydrochloride. Such an improvement was ascribed to the widened visible light absorption and enhanced separation of the photogenerated electron-hole pairs because of the synergistic effect of oxygen vacancies and Bi metal with plasmon resonance effects. A possible photocatalytic mechanism of the quasi Z-scheme process was proposed on the basis of ESR measurements and radical-trapping experiments.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics.

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.

Physical controllability of complex networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

Energy scaling and reduction in controlling complex networks.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A geometrical approach to control and controllability of nonlinear dynamical networks.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

Failure Analysis in Magnetic Tunnel Junction Nanopillar with Interfacial Perpendicular Magnetic Anisotropy.

Magnetic tunnel junction nanopillar with interfacial perpendicular magnetic anisotropy (PMA-MTJ) becomes a promising candidate to build up spin transfer torque magnetic random access memory (STT-MRAM) for the next generation of non-volatile memory as it features low spin transfer switching current, fast speed, high scalability, and easy integration into conventional complementary metal oxide semiconductor (CMOS) circuits. However, this device suffers from a number of failure issues, such as large process variation and tunneling barrier breakdown. The large process variation is an intrinsic issue for PMA-MTJ as it is based on the interfacial effects between ultra-thin films with few layers of atoms; the tunneling barrier breakdown is due to the requirement of an ultra-thin tunneling barrier (e.g., <1 nm) to reduce the resistance area for the spin transfer torque switching in the nanopillar. These failure issues limit the research and development of STT-MRAM to widely achieve commercial products. In this paper, we give a full analysis of failure mechanisms for PMA-MTJ and present some eventual solutions from device fabrication to system level integration to optimize the failure issues.

Build to understand: synthetic approaches to biology.

In this review we discuss how synthetic biology facilitates the task of investigating genetic circuits that are observed in naturally occurring biological systems. Specifically, we give examples where experimentation with synthetic gene circuits has been used to understand four fundamental mechanisms intrinsic to development and disease: multistability, stochastic gene expression, oscillations, and cell-cell communication. Within each area, we also discuss how mathematical modeling has been employed as an essential tool to guide the design of novel gene circuits and as a theoretical basis for exploring circuit topologies exhibiting robust behaviors in the presence of noise.

Emergence, evolution and scaling of online social networks.

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.