Subthreshold swing characteristics of nanowire tunneling FETs with variation in gate coverage and channel diameter

In this study, we demonstrate the simulated subthreshold swing (SS) of silicon nanowire tunneling field-effect transistors (NWTFETs) by varying both the channel diameter from 10 nm to 40 nm and the gate coverage ratio from 30% to 100%. Our simulation work reveals that both a decrease in the channel diameter and an increase in the gate coverage ratio contribute to a reduction in the SS. Additionally, our work shows that the magnitude of the on-current depends linearly on the gate coverage ratio and that the drain current increases with a decrease in the channel diameter. Thus, an NWTFET with a channel diameter of 10 nm and a gate coverage ratio of 100% exhibits superior electrical characteristics over other silicon NWTFETs in that the NWTFET shows a point SS of 22.7 mV/dec, an average SS of 56.3 mV/dec, an on/off current ratio of ∼10¹³, and an on-current of ∼10⁻⁵ A/μm.     

UWB LOS/NLOS identification in multiple indoor environments using deep learning methods

Indoor location-aware service is booming in daily life and business activities, making the demand for precise indoor positioning systems thrive. The identification between line-of-sight (LOS) and non-line-of-sight (NLOS) is critical for wireless indoor time-of-arrival-based localization methods. Ultra-Wide-Band (UWB) is considered low cost among the many wireless positioning systems. It can resolve multi-path and have high penetration ability. This contribution addresses UWB NLOS/LOS identification problem in multiple environments. We propose a LOS/NLOS identification method using Convolutional Neural Network parallel with Gate Recurrent Unit, named Indoor NLOS/LOS identification Neural Network. The Convolutional Neural Network extracts spatial features of UWB channel impulse response data. While the Gate Recurrent Unit is an effective approach for designing deep recurrent neural networks which can extract temporal features. By integrating squeeze-and-extraction blocks into these architectures we can assign weights on channel-wise features. We simulated UWB channel impulse response signals in residential, office, and industrial scenarios based on the IEEE 802.15.4a channel model report. The presented network was tested in simulation scenarios and an open-source real-time measured dataset. Our method can solve NLOS identification problems for multiple indoor environments. Thus more versatile compare with networks only working in one scenario. Popular machine learning methods and deep learning methods are compared against our method. The test results show that the proposed network outperforms benchmark methods in simulation datasets and real-time measured datasets.     

Fast convergence in language games induced by majority rule

We propose a simple model to investigate the evolutionary dynamics of a naming game on well-mixed populations. We assume that each individual has an inherent propensity to maintain his own word about an object whereas other individuals would affect his decision when they communicate. On the one hand, individuals learn the word of another one with a probability pertaining to their propensities. On the other hand, the focal individual would adopt the word held by the majority in a randomly selected group. We have numerically explored how dynamical behavior evolves as a result of combination of these two competing update patterns. A parameter governs the time scale ratio at which the two update patterns separately progress. We find that an increasing tendency to adopt the word held by the majority results in a rapid extinction of most words, thus more easily induces the system to a global consensus. Large initial probabilities denoting propensity are found to be unfavorable for the achievement of the consensus. Interestingly, simulation results indicate that the convergence time is negligibly affected by the number of initial distinct words when this number exceeds a certain value. Results from our model may offer an insight into better understanding the intricate dynamics of naming games.     

Passive Gate-Tunable Kinetic Photovoltage along Semiconductor-Water Interfaces

Moving boundaries of electric double layer at solid–liquid interface enables unprecedented persistent energy conversion and provokes a kinetic photovoltaic effect by moving an illumination region along the semiconductor-water interface. Here, we report a transistor-inspired gate modulation of kinetic photovoltage by applying a bias at the semiconductor-water interface. The kinetic photovoltage of both p-type and n-type silicon samples can be facilely switched on/off, stemming from the electrical-field-modulated surface band bending. In contrast to the function of solid-state transistors relying on external sources, passive gate modulation of the kinetic photovoltage is achieved simply by introducing a counter electrode with materials of desired electrochemical potential. This architecture provides the ability to modulate the kinetic photovoltage over three orders of magnitude and opens up a new way for self-powered optoelectronic logic devices.