Cmos Rf Cituits Sic] Variability And Reliability Resilient Design, Modeling, And Simulation

The work presents a novel voltage biasing design that helps the CMOS RF circuits resilient to variability and reliability. The biasing scheme provides resilience through the threshold voltage (VT) adjustment, and at the mean time it does not degrade the PA performance. Analytical equations are established for sensitivity of the resilient biasing under various scenarios. Power Amplifier (PA) and Low Noise Amplifier (LNA) are investigated case by case through modeling and experiment. PTM 65nm technology is adopted in modeling the transistors within these RF blocks. A traditional class-AB PA with resilient design is compared the same PA without such design in PTM 65nm technology. Analytical equations are established for sensitivity of the resilient biasing under various scenarios. A traditional class-AB PA with resilient design is compared the same PA without such design in PTM 65nm technology. The results show that the biasing design helps improve the robustness of the PA in terms of linear gain, P1dB, Psat, and power added efficiency (PAE). Except for post-fabrication calibration capability, the design reduces the majority performance sensitivity of PA by 50% when subjected to threshold voltage (VT) shift and 25% to electron mobility (μn) degradation. The impact of degradation mismatches is also investigated. It is observed that the accelerated aging of MOS transistor in the biasing circuit will further reduce the sensitivity of PA. In the study of LNA, a 24 GHz narrow band cascade LNA with adaptive biasing scheme under various aging rate is compared to LNA without such biasing scheme. The modeling and simulation results show that the adaptive substrate biasing reduces the sensitivity of noise figure and minimum noise figure subject to process variation and iii device aging such as threshold voltage shift and electron mobility degradation. Simulation of different aging rate also shows that the sensitivity of LNA is further reduced with the accelerated aging of the biasing circuit. Thus, for majority RF transceiver circuits, the adaptive body biasing scheme provides overall performance resilience to the device reliability induced degradation. Also the tuning ability designed in RF PA and LNA provides the circuit post-process calibration capability

Semi-Numerical Simulation of Reionization with Semi-Analytical Modeling of Galaxy Formation

In a semi-numerical model of reionization, the evolution of ionization fraction is simulated approximately by the ionizing photon to baryon ratio criterion. In this paper we incorporate a semi-analytical model of galaxy formation based on the Millennium II N-body simulation into the semi-numerical modeling of reionization. The semi-analytical model is used to predict the production of ionizing photons, then we use the semi-numerical method to model the reionization process. Such an approach allows more detailed modeling of the reionization, and also connects observations of galaxies at low and high redshifts to the reionization history. The galaxy formation model we use was designed to match the low-$z$ observations, and it also fits the high redshift luminosity function reasonably well, but its prediction on the star formation falls below the observed value, and we find that it also underpredicts the stellar ionizing photon production rate, hence the reionization can not be completed at $z \sim 6$ without taking into account some other potential sources of ionization photons. We also considered simple modifications of the model with more top heavy initial mass functions (IMF), with which the reionization can occur at earlier epochs. The incorporation of the semi-analytical model may also affect the topology of the HI regions during the EoR, and the neutral regions produced by our simulations with the semi-analytical model appeared less poriferous than the simple halo-based models.Comment: 13 pages, 8 figures, RAA accepte

Employing a Narrow-Band-Gap Mediator in Ternary Solar Cells for Enhanced Photovoltaic Performance.

Ternary organic solar cells (OSCs) provide a convenient and effective means to further improve the power conversion efficiency (PCE) of binary ones via composition control. However, the role of the third component remains to be explored in specific binary systems. Herein, we report ternary blend solar cells by adding the narrow-band-gap donor PCE10 as the mediator into the PBDB-T:IDTT-T binary blend system. The extended absorption, efficient fluorescence resonance energy transfer, enhanced charge dissociation, and induced tighter molecular packing of the ternary blend films enhance the photovoltaic properties of devices and deliver a champion PCE of 10.73% with an impressively high open-circuit voltage (VOC) of 1.03 V. Good miscibility and similar molecular packing behavior of the components guarantee the desired morphology in the ternary blend films, leading to solar cell devices with over 10% PCEs at a range of compositions. Our results suggest that ternary systems with properly aligned energy levels and overlapping absorption among the components hold great promises to further enhance the performance of corresponding binary ones

Understanding Health Video Engagement: An Interpretable Deep Learning Approach

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Understanding how health misinformation is transmitted is an urgent goal for researchers, social media platforms, health sectors, and policymakers to mitigate those ramifications. Deep learning methods have been deployed to predict the spread of misinformation. While achieving the state-of-the-art predictive performance, deep learning methods lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning approach, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. Improving upon state-of-the-art interpretable methods, GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature when its value varies. We select features according to social exchange theory and evaluate GAN-PiWAD on 4,445 misinformation videos. The proposed approach outperformed strong benchmarks. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning method that is generalizable to understand other human decision factors. Our findings provide direct implications for social media platforms and policymakers to design proactive interventions to identify misinformation, control transmissions, and manage infodemics.Comment: WITS 2021 Best Paper Awar

An Interpretable Deep Learning Approach to Understand Health Misinformation Transmission on YouTube

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Deep learning methods have been deployed to predict the spread of misinformation, but they lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning that is generalizable to understand human decisions. We provide direct implications to design interventions to identify misinformation, control transmissions, and manage infodemics

Study Of Ingaas Ldmos For Power Conversion Applications

In this work an n-channel In0.65Ga0.35As LDMOS with Al2O3 as gate dielectric is investigated. Instead of using traditional Si process for LDMOS, we suggest In0.65Ga0.35As as substitute material due to its higher electron mobility and its promising for power applications. The proposed 0.5-µm channel-length LDMOS cell is studied through device TCAD simulation tools. Due to different gate dielectric, comprehensive comparisons between In0.65Ga0.35As LDMOS and Si LDMOS are made in two ways, structure with the same cross-sectional dimension, and structure with different thickness of gate dielectric to achieve the same gate capacitance. The on-resistance of the new device shows a big improvement with no degradation on breakdown voltage over traditional device. Also it is indicated from these comparisons that the figure of merit(FOM) Ron·Qg of In0.65Ga0.35As LDMOS shows an average of 91.9% improvement to that of Si LDMOS. To further explore the benefit of using In0.65Ga0.35As LDMOS as switch in power applications, DC-DC buck converter is utilized to observe the performance of LDMOS in terms of power efficiency. The LDMOS performance is experimented with operation frequency of the circuit sweeping in the range from 100 KHz to 100 MHz. It turns out InGaAs LDMOS is good candidate for power applications

HMGCS2 is a key ketogenic enzyme potentially involved in type 1 diabetes with high cardiovascular risk.

Diabetes increases the risk of Cardio-vascular disease (CVD). CVD is more prevalent in type 2 diabetes (T2D) than type 1 diabetes (T1D), but the mortality risk is higher in T1D than in T2D. The pathophysiology of CVD in T1D is poorly defined. To learn more about biological pathways that are potentially involved in T1D with cardiac dysfunction, we sought to identify differentially expressed genes in the T1D heart. Our study used T1D mice with severe hyperglycemia along with significant deficits in echocardiographic measurements. Microarray analysis of heart tissue RNA revealed that the T1D mice differentially expressed 10 genes compared to control. Using Ingenuity Pathway Analysis (IPA), we showed that these genes were significantly involved in ketogenesis, cardiovascular disease, apoptosis and other toxicology functions. Of these 10 genes, the 3-Hydroxy-3-Methylglutaryl-CoA Synthase 2 (HMGCS2) was the highest upregulated gene in T1D heart. IPA analysis showed that HMGCS2 was center to many biological networks and pathways. Our data also suggested that apart from heart, the expression of HMGCS2 was also different in kidney and spleen between control and STZ treated mice. In conclusion, The HMGCS2 molecule may potentially be involved in T1D induced cardiac dysfunction