Modeling And Simulation Of Single And Double Gates Ion Sensitive Field Effect Transistor For Biomedical Applications

The modeling of Ion Sensitive Field Effect Transistor (ISFET) generally starts with its analogy to MOS devices and its threshold dependence on pH. Massobrio et al. proposed a macro-model plug in for SPICE. It was later modified to fit general SPICE based simulators without the need for a plug-in software. Then, different works followed the first modeling and simulation of ISFET by using widely available commercial CAD simulations. Unfortunately, the commercial TCAD is not supplied with model, material, and electrochemical packages to effectively manage the ISFET process and its operations. The main objective of this research is a comprehensive, accurate modeling and simulation of SG and DG ISFET devices. First, the adaptation of the Gouy-Chapman-Stern model mathematically and using TCAD to compensate for the roll-off non-ideality have been proposed. Performance analysis of conventional ISFET for six high-k materials as a Stern layer sensing membrane was also implemented. Moreover, a design and characterization of double-gate (DG) ISFET for SiO2 and Six high-k sensing membrane toward beyond Nernst limit sensitivity was done. Finally, a model for the geometrical parameter's impact on DG ISFET sensitivity was proposed. To achieve these objectives, the parameters of the silicon semiconductor material (that is, energy bandgap, permittivity, affinity, and density of states) are reconstructed in the electrolyte solution utilizing user-defined statement offered by Silvaco ATLAS. The electrostatic solution of the electrolyte area can also be investigated by constructing a numerical solution for the semiconductor equation in this area. The devices were virtually fabricated using ATHENA module of TCAD software. The materials used as a sensing membrane in devices were normal silicon dioxide (SiO2) and six high-k material (TiO2, Ta2O5, ZrO2, Al2O3, HfO2, and Si3N4). Then, the developed TCAD is used with the design of experiments (DOE) to investigate the effect of geometrical parameters on the performance of DG ISFETs and enhance the classical model. Three and five geometrical parameters, namely, buried oxide, silicon body, top oxide, channel length, and electrolyte thickness, are considered as independent factors in the DOE. Validation results revealed that the developed TCAD model has an acceptable agreement with experimental results and theoretical models in SG and DG ISFET in terms of sensitivity and ideal amplification ratio. On the other hand, silicon body thickness does not only affect the sensitivity toward the ultra-thin body but also can achieve an ultra-thin-body-buried oxide (Box). Channel length and electrolyte thickness as new investigated parameters also showed a clear impact on ISFET sensing properties. Furthermore, the developed TCAD and RSM mathematical models agreed with real experimental results in terms of average sensitivity and amplification ratio. The final design that depends on the control model resulted in a sensitivity ~1250 mV/pH that is ~21 times higher than the Nernst limit. To sum up, this study can open new directions for further analysis and optimization. Besides, the small sensing area and the FDSOI ISFET-based technology of the device can make the sensors ideal for the biomedical and IoT devices market

Independence And Fairness Analysis Of 5G mmWave Operators Utilizing Spectrum Sharing Approach

-e spectrum sharing approach (SSA) has emerged as a cost-efficient solution for the enhancement of spectrum utilization to meet the stringent requirements of 5G systems. However, the realization of SSA in 5G mmWave cellular networks from technical and regulatory perspectives could be challenging. -erefore, in this paper, an analytical framework involving a flexible hybrid mmWave SSA is presented to assess the effectiveness of SSA and investigate its influence on network functionality in terms of independence and fairness among operators. Two mmWave frequencies (28 GHz and 73 GHz) are used with different spectrum bandwidths. Various access models have been presented for adoption by four independent mobile network operators that incorporate three types of spectrum allocation (exclusive, semipooled, and fully pooled access). Furthermore, an adaptive multi-state mmWave cell selection scheme is proposed to associate typical users with the tagged mmWave base stations that provide a great signal-to-interference plus noise ratio, thereby maintaining reliable connections and enriching user experience. Numerical results show that the proposed strategy achieves considerable improvement in terms of fairness and independence among operators, which paves the way for further research activities that would provide better insight and encourage mobile network operators to rely on SSA

Hybrid Multi-Independent Mmwave MNOs Assessment Utilising Spectrum Sharing Paradigm For 5G Networks

Spectrum sharing paradigm (SSP) has recently emerged as an attractive solution to provide capital expenditure (CapEx) and operating expenditure (OpEx) savings and to enhance spectrum utilization (SU). However, practical issues concerning the implementation of such paradigm are rarely addressed (e.g., mutual interference, fairness, and mmWave base station density). Therefore, in this paper, we proposed ultra-reliable and proportionally fair hybrid spectrum sharing access strategy that aims to address the aforementioned aspects as a function of coverage probability (CP), average rate distributions (ARD), and the number of mmWave base stations (mBSs). In this strategy, the spectrum is sliced into three parts (exclusive, semi-pooled, and fully pooled). A typical user that belongs to certain operator has the right to occupy a part of the spectrum available in the high and low frequencies (28 and 73 GHz) based on an adaptive multi-state mmWave cell selection scheme (AMMC-S) which associates the user with the tagged mBS that offers a highest SINR to maintain more reliable connection and enrich the user experience. Numerical results show that significant improvement in terms of ARD, CP, fairness among operators, and maintain an acceptable level of mBSs densit

Novel Design Of Triple-Band EBG

This paper presents a novel design for a triple band electromagnetic band gap (EBG) structures that provides three band gaps, with operating frequency of below 10 GHz, while the ordinary mushroom like EBG structure gives only one band gap. Complexity reduction (reduce the number of unit cells and Vias) was achieved by replacing each four cells of the Mushroom like EBG by the one of double slotted type EBG (DSTEBG) or triple side slotted EBG (TSSEBG). The Mushroom like EBG was further modified by increasing its size and inserting the slots to gain more capacitance and inductance which resulted into triple band stop. The new designs wer compared with bandwidths expressed by other EBGs and-20 dB cut-off frequencies. The size of EBG element and the gap between EBG elements, and slot width were investigated to analyse their effect on the transmission response. The structures were designed from 2.54 mm Rogers RT/Duroid 6010 substrate with relative permittivity of 10.2 and loss tangent of 0.0023. Among the investigated EBGs, the single band mushroom like EBG and the triple band of the TSSEBG demonstrated better bandwidth and lower resonance frequency performance, whereas the DSTEBG showed larger bandwidth for the first and third band. The proposed EBGs could be useful in the antenna design and other microwave circuits

Smart healthcare system for severity prediction and critical tasks management of COVID-19 patients in IoT-fog computing environments

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.Web of Science2022art. no. 501296

Joint QoE-Based User Association And Efficient Cell–Carrier Distribution For Enabling Fully Hybrid Spectrum Sharing Approach In 5G mmWave Cellular Networks

Densifying the network by adding more minicell towers or relays throughout a hot spot area while extensively reusing the available spectrum is an essential choice to improve QoS. Unfortunately, this approach can be prohibitively costly. One possible solution to reduce the capital and operating expenditure in such overdensified networks is the adoption of the spectrum-sharing approach. However, both approaches would complicate the interference phenomenon either among interor intraoperators, which may cause serious performance degradation. In this paper, a fully hybrid spectrum-sharing (FHSS) approach aided by an efficient cell–carrier distribution was proposed with consideration to the interference dilemma. Moreover, an adaptive hybrid QoE-based mmWave user association (mUA) scheme was presented to assign a typical user to the serving mmWave base station (mBS), which offers the highest achievable data rate. The proposed FHSS approach (with the presented QoE-based mUA) was compared with recent works and with both FHSS approach using the conventional max-SINR-based mUA, which assigns a typical user to the tagged mBS carrying the highest signal-to-interference-plus noise ratio and the baseline scenario (licensed spectrum access). In particular, three spectrum access methods (licensed, semipooled, and fully pooled) were integrated in a hybrid manner to engage improved data rates to users. Numerical results show that the joint cell–carrier distribution and FHSS approach with QoE-based mUA outperform both baselines FHSS with the max-SINR mUA scheme and the licensed spectrum access. Furthermore, results demonstrate the effectiveness of the proposed approach in terms of both operators’ independence and fairness