Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing

Gender differences is a phenomenon around the world actively researched by social scientists. Traditionally, the data used to support such studies is manually obtained, often through surveys with volunteers. However, due to their inherent high costs because of manual steps, such traditional methods do not quickly scale to large-size studies. We here investigate a particular aspect of gender differences: preferences for venues. To that end we explore the use of check-in data collected from Foursquare to estimate cultural gender preferences for venues in the physical world. For that, we first demonstrate that by analyzing the check-in data in various regions of the world we can find significant differences in preferences for specific venues between gender groups. Some of these significant differences reflect well-known cultural patterns. Moreover, we also gathered evidence that our methodology offers useful information about gender preference for venues in a given region in the real world. This suggests that gender and venue preferences observed may not be independent. Our results suggests that our proposed methodology could be a promising tool to support studies on gender preferences for venues at different spatial granularities around the world, being faster and cheaper than traditional methods, besides quickly capturing changes in the real world

Spatial Sensitivity of Silicon GAA Nanowire FETs under Line Edge Roughness Variations

Standard analysis of variability sources in nanodevices lacks information about the spatial influence of the variability. However this spatial information is paramount for the industry and academia to improve the design of variability-resistant architectures. A recently developed technique, the Fluctuation Sensitivity Map (FSM) is used to analyse the spatial effect of the Line Edge Roughness (LER) variability in key figures-of-merit (FoM) in silicon Gate-All-Around (GAA) nanowire (NW) FETs. This technique gives insight about the local sensitivity identifying the regions inducing the strongest variability into the FoM. We analyse both 22 nm and 10 nm gate length GAA NW FETs affected by the LER with different amplitudes (0.6, 0.7, 0.85 nm) and correlation lengths (10, 20 nm) using in-house 3D quantum-corrected drift-diffusion simulation tool calibrated against experimental or Monte Carlo data. The FSM finds that the gate is the most sensitive region to LER deformations. We demonstrate that the specific location of the deformation inside the gate plays an important role in the performance and that the effect of the location is also dependent on the FoM analysed. Moreover, there is a negligible impact on the device performance if the LER deformation occurs in the source or drain region

FinFET Versus Gate-All-Around Nanowire FET: Performance, Scaling, and Variability

Performance, scalability and resilience to variability of Si SOI FinFETs and gate-all-around (GAA) nanowires (NWs) are studied using in-house-built 3D simulation tools. Two experimentally based devices, a 25 nm gate length FinFET and a 22 nm GAA NW are modelled and then scaled down to 10.7 and 10 nm gate lengths, respectively. A TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability affecting OFF and ON characteristics are investigated and compared. In the OFF-region, the FinFETs have over an order of magnitude larger OFF-current that those of the equivalent GAA NWs. In the ON-region, the 25/10.7 nm gate length FinFETs deliver 20/58% larger ON-current than the 22/10 nm gate length GAA NWs. The FinFETs are more resilient to the MGG and LER variability in the sub-threshold compared to the GAA NWs. However, the MGG ON-current variability is larger for the 10.7 nm FinFET than that for the 10 nm GAA NW. The LER ON-current variability depends largely on the RMS height; whereas a 0.6 nm RMS height yields a similar variability for both FinFETs and GAA NWs. Finally, the industry preferred 110 channel orientation is more resilient to the MGG and LER variability in both architectures

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

Comparison of fin-edge roughness and metal grain work function variability in InGaAs and Si FinFETs

The fin-edge roughness (FER) and the TiN metal grain work function (MGW)-induced variability affecting OFF and ON device characteristics are studied and compared between a 10.4 nm gate length In0.53Ga0.47As FinFET and a 10.7 nm gate length Si FinFET. We have analyzed the impact of variability by assessing five figures of merit (threshold voltage, subthreshold slope, OFF-current, drain-induced-barrier-lowering, and ON-current) using the two state-of-the-art in-house-build 3-D simulation tools based on the finite-element method. Quantum-corrected 3-D drift-diffusion simulations are employed for variability studies in the subthreshold region while, in the ON-region, we use quantum-corrected 3-D ensemble Monte Carlo simulations. The In0.53Ga0.47As FinFET is more resilient to the FER and MGW variability in the subthreshold compared with the Si FinFET due to a stronger quantum carrier confinement present in the In0.53Ga0.47As channel. However, the ON-current variability is between 1.1 and 2.2 times larger for the In0.53Ga0.47As FinFET than for the Si counterpart, respectively