Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497971
Erwin H. T. Shad, Tania Moeinfard, M. Molinas, T. Ytterdal
In this article, a rail-to-rail low-power amplifier is presented based on stacking inverter-based amplifiers. The output voltages of each inverter-based amplifier are converted to a current and then mirrored to the output so that a rail-to-rail output is achieved. Besides, extensive simulations have been carried out to show the effect of drain-source voltage on the intrinsic gain of a transistor. Based on these simulations, a minimum supply voltage is chosen to achieve high open-loop gain and low closed-loop gain error. All the simulations are carried out in a commercially available 0.18 µm CMOS technology. The proposed amplifier achieves 88 dB open-loop gain. It is exploited in a capacitively-coupled amplifier structure. The closed-loop gain is 40 dB in the bandwidth of 0.1 Hz to 10 kHz when the power consumption is 0.54 µW at a 1.2 V supply voltage. The total input-referred noise is 4.7 µVrms in the whole bandwidth. The proposed neural amplifier achieved 0.02 SEF in the bandwidth from 200 Hz to 10 kHz. The proposed amplifier achieved a rail-to-rail output swing while the SEF is among the best reported SEF in the literature. Besides, to show the robustness of the proposed structure in the presence of process and mismatch variation, 500 Monte Carlo simulations are carried out. The PSRR and CMRR mean values are 89 dB and 68 dB, respectively. Finally, the proposed neural amplifier area consumption is 0.03 mm2 without pads.
{"title":"A Low-power High-gain Inverter Stacking Amplifier with Rail-to-Rail Output","authors":"Erwin H. T. Shad, Tania Moeinfard, M. Molinas, T. Ytterdal","doi":"10.1109/DTS52014.2021.9497971","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497971","url":null,"abstract":"In this article, a rail-to-rail low-power amplifier is presented based on stacking inverter-based amplifiers. The output voltages of each inverter-based amplifier are converted to a current and then mirrored to the output so that a rail-to-rail output is achieved. Besides, extensive simulations have been carried out to show the effect of drain-source voltage on the intrinsic gain of a transistor. Based on these simulations, a minimum supply voltage is chosen to achieve high open-loop gain and low closed-loop gain error. All the simulations are carried out in a commercially available 0.18 µm CMOS technology. The proposed amplifier achieves 88 dB open-loop gain. It is exploited in a capacitively-coupled amplifier structure. The closed-loop gain is 40 dB in the bandwidth of 0.1 Hz to 10 kHz when the power consumption is 0.54 µW at a 1.2 V supply voltage. The total input-referred noise is 4.7 µVrms in the whole bandwidth. The proposed neural amplifier achieved 0.02 SEF in the bandwidth from 200 Hz to 10 kHz. The proposed amplifier achieved a rail-to-rail output swing while the SEF is among the best reported SEF in the literature. Besides, to show the robustness of the proposed structure in the presence of process and mismatch variation, 500 Monte Carlo simulations are carried out. The PSRR and CMRR mean values are 89 dB and 68 dB, respectively. Finally, the proposed neural amplifier area consumption is 0.03 mm2 without pads.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127772638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498164
O. Zaim, F. Ajana, Naoual Lagdali, I. Benelbarhdadi, N. El Bari, B. Bouchikhi
Health status monitoring based on non-invasive methodology through exhaled breath analysis has raised great interest, due to its easiness that does not require skilled medical personnel. The aim of the present study is to discriminate between exhaled breath samples of patients' groups with Diabetes Mellitus (DM), Renal Failure (RF), Liver Cirrhosis (LCi), and Healthy Controls (HC), by using an array of metal oxide chemoresistive sensors. Breath samples collected from HC (n=10), DM (n=6), RF (n=11), and LCi (n=11) patients were analyzed by the electronic nose (e-nose), and classification was performed using chemometric techniques namely: Discriminant Function Analysis (DFA) and Support Vector Machines (SVMs). As result, DFA has shown good discrimination between data-points of breath samples related to HC, DM, RF, and LCi patients. The SVMs method reached a 96.49% success rate for the recognition of the analyzed four groups. In the light of these results, we can state that the presented e-nose technology demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis could be considered a reliable screening tool for diseases diagnosis.
{"title":"Exhaled breath-print analysis by using metal oxide chemoresistive sensors for classifying and monitoring patients with different clinical states","authors":"O. Zaim, F. Ajana, Naoual Lagdali, I. Benelbarhdadi, N. El Bari, B. Bouchikhi","doi":"10.1109/DTS52014.2021.9498164","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498164","url":null,"abstract":"Health status monitoring based on non-invasive methodology through exhaled breath analysis has raised great interest, due to its easiness that does not require skilled medical personnel. The aim of the present study is to discriminate between exhaled breath samples of patients' groups with Diabetes Mellitus (DM), Renal Failure (RF), Liver Cirrhosis (LCi), and Healthy Controls (HC), by using an array of metal oxide chemoresistive sensors. Breath samples collected from HC (n=10), DM (n=6), RF (n=11), and LCi (n=11) patients were analyzed by the electronic nose (e-nose), and classification was performed using chemometric techniques namely: Discriminant Function Analysis (DFA) and Support Vector Machines (SVMs). As result, DFA has shown good discrimination between data-points of breath samples related to HC, DM, RF, and LCi patients. The SVMs method reached a 96.49% success rate for the recognition of the analyzed four groups. In the light of these results, we can state that the presented e-nose technology demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis could be considered a reliable screening tool for diseases diagnosis.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122773426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498072
Safa Bouguezzi, H. Faiedh, C. Souani
The Convolutional Neural Network (CNN) dominates the research area of Field Programmable Gate Arrays (FPGAs) and demonstrates its efficiency on computer vision applications. The correct predicted rate of the CNN is highly dependent on the selection of the activation functions. Thus, we intend to deploy a CNN model on Virtex-7 while varying the activation function such as ReLU, PReLU, and Tanh Exponential (TanhExp) activation functions. To this end, we will use a fixed-point representation concerning the arithmetic numbers and the piecewise linear approximation regarding the TanhExp activation function. We present the speed, accuracy and hardware resources of each model of the CNN.
{"title":"Hardware Implementation of Fixed-Point Convolutional Neural Network For Classification","authors":"Safa Bouguezzi, H. Faiedh, C. Souani","doi":"10.1109/DTS52014.2021.9498072","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498072","url":null,"abstract":"The Convolutional Neural Network (CNN) dominates the research area of Field Programmable Gate Arrays (FPGAs) and demonstrates its efficiency on computer vision applications. The correct predicted rate of the CNN is highly dependent on the selection of the activation functions. Thus, we intend to deploy a CNN model on Virtex-7 while varying the activation function such as ReLU, PReLU, and Tanh Exponential (TanhExp) activation functions. To this end, we will use a fixed-point representation concerning the arithmetic numbers and the piecewise linear approximation regarding the TanhExp activation function. We present the speed, accuracy and hardware resources of each model of the CNN.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131646519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498044
Marios Gourdouparis, Vassilis Alimisis, Christos Dimas, P. Sotiriadis
A compact, ultra-low power (4nW), low supply voltage (0.6V) Gaussian-bump circuit architecture for Radial Basis Functions implementation is presented. It consists of only ten transistors, all operating in sub-threshold. The Gaussians center, height and width are independently and electronically controlled. The proposed architecture is used as a building block to construct a 2 – D Gaussian cascaded bump structure, demonstrating its dimensional scalability. Proper operation, sensitivity and accuracy are confirmed via theoretical analysis and simulation. The presented architectures were realized in TSMC 90nm CMOS process and were simulated using the Cadence IC Suite.
提出了一种紧凑、超低功耗(4nW)、低电源电压(0.6V)的径向基函数实现高斯碰撞电路结构。它由十个晶体管组成，全部工作在亚阈值下。高斯中心，高度和宽度是独立的和电子控制的。将该结构作为构建块，构造了一个二维高斯级联凹凸结构，证明了其维度可扩展性。通过理论分析和仿真验证了该方法的正确性、灵敏度和精度。该架构在台积电90nm CMOS工艺中实现，并使用Cadence IC Suite进行了仿真。
{"title":"Ultra-Low Power (4nW), 0.6V Fully-Tunable Bump Circuit operating in Sub-threshold regime","authors":"Marios Gourdouparis, Vassilis Alimisis, Christos Dimas, P. Sotiriadis","doi":"10.1109/DTS52014.2021.9498044","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498044","url":null,"abstract":"A compact, ultra-low power (4nW), low supply voltage (0.6V) Gaussian-bump circuit architecture for Radial Basis Functions implementation is presented. It consists of only ten transistors, all operating in sub-threshold. The Gaussians center, height and width are independently and electronically controlled. The proposed architecture is used as a building block to construct a 2 – D Gaussian cascaded bump structure, demonstrating its dimensional scalability. Proper operation, sensitivity and accuracy are confirmed via theoretical analysis and simulation. The presented architectures were realized in TSMC 90nm CMOS process and were simulated using the Cadence IC Suite.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9497940
Mouna Elhamdaoui, Khaoula Mbarek, Faten Ouaja Rziga, K. Besbes
The memristor, the fourth passive circuit element, has exhibited resistance switching mechanism, which can be used in several applications such as nonvolatile memory, digital logic circuits, and neuromorphic systems. The switching mechanism in a Ta2O5-RRAM device is achieved by conductive filament (CF) modulation that provides a suitable analog switching for the electronic synapses. In this paper, we analyze and discuss four different memristor models to identify which of them can achieve sufficient accuracy compared to the physical Ta2O5-RRAM device, in order to be implemented as a synapse. These examined models are the linear ion drift (HP) model, the Voltage Threshold Adaptive Memristor (VTEAM) model, the Memdiode model and the Enhanced Generalized Memristor (EGM) model. Thus, we present the simulation results of each model and we compare its switching characteristics with the experimental characteristics. This study allows us to select the most appropriate memristor model for emulating the synaptic functions.
{"title":"Memristor models for synapse component","authors":"Mouna Elhamdaoui, Khaoula Mbarek, Faten Ouaja Rziga, K. Besbes","doi":"10.1109/DTS52014.2021.9497940","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9497940","url":null,"abstract":"The memristor, the fourth passive circuit element, has exhibited resistance switching mechanism, which can be used in several applications such as nonvolatile memory, digital logic circuits, and neuromorphic systems. The switching mechanism in a Ta2O5-RRAM device is achieved by conductive filament (CF) modulation that provides a suitable analog switching for the electronic synapses. In this paper, we analyze and discuss four different memristor models to identify which of them can achieve sufficient accuracy compared to the physical Ta2O5-RRAM device, in order to be implemented as a synapse. These examined models are the linear ion drift (HP) model, the Voltage Threshold Adaptive Memristor (VTEAM) model, the Memdiode model and the Enhanced Generalized Memristor (EGM) model. Thus, we present the simulation results of each model and we compare its switching characteristics with the experimental characteristics. This study allows us to select the most appropriate memristor model for emulating the synaptic functions.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123592739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development technology, Artificial Intelligence is the most powerful technique, it has made great progress in many areas, including computer vision and medical imaging. This paper proposes a deep learning-based framework for COVID-19 detection. Deep transfer learning models-based on a pre-trained Deep convolutional Neural Network are proposed. Several pre-trained models, such as DensNet201, InceptionV3, VGG16, and ResNet50 were evaluated for this analysis.The datasets used in this paper for training model are a mix of X-ray and CT images in two distinct categories: Normal and COVID-19. The experimental results proved that the DensNet201 was the most suitable deep transfer model according to the test accuracy measure and that it reached 97% with the other performance metrics such as F1 score, precision, and recall.
{"title":"COVID-19 Recognition based on Deep Transfer Learning","authors":"Soulef Bouaafia, Seifeddine Messaoud, Randa Khemiri, Fatma Sayadi","doi":"10.1109/DTS52014.2021.9498052","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498052","url":null,"abstract":"With the rapid development technology, Artificial Intelligence is the most powerful technique, it has made great progress in many areas, including computer vision and medical imaging. This paper proposes a deep learning-based framework for COVID-19 detection. Deep transfer learning models-based on a pre-trained Deep convolutional Neural Network are proposed. Several pre-trained models, such as DensNet201, InceptionV3, VGG16, and ResNet50 were evaluated for this analysis.The datasets used in this paper for training model are a mix of X-ray and CT images in two distinct categories: Normal and COVID-19. The experimental results proved that the DensNet201 was the most suitable deep transfer model according to the test accuracy measure and that it reached 97% with the other performance metrics such as F1 score, precision, and recall.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130218686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498154
M. Belouda, A. Mami
The electric power supplied by hybrid renewable sources such as Photovoltaic panels and Wind Turbines associated to storage systems as Batteries (PV/WT/BAT systems) depends on environmental factors (wind speed, light intensity and temperature). These factors are characterized by their intermittence and unpredictability; it is therefore essential to effectively control and manage power flows from these sources. This paper presents the implementation of the different energy management scenarios of a PV/WT/BAT system based on various programmable targets (VLSI and Pic microcontroller).
{"title":"Hybrid PV/WT/Batteries system management using VLSI and Pic microcontroller","authors":"M. Belouda, A. Mami","doi":"10.1109/DTS52014.2021.9498154","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498154","url":null,"abstract":"The electric power supplied by hybrid renewable sources such as Photovoltaic panels and Wind Turbines associated to storage systems as Batteries (PV/WT/BAT systems) depends on environmental factors (wind speed, light intensity and temperature). These factors are characterized by their intermittence and unpredictability; it is therefore essential to effectively control and manage power flows from these sources. This paper presents the implementation of the different energy management scenarios of a PV/WT/BAT system based on various programmable targets (VLSI and Pic microcontroller).","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130041429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498223
Nafaa Kacem, A. Bhouri, J. Lazzari, N. Jaba
In this study, we theoretically investigated the electronic properties of resonant tunneling diodes (RTDs) grown along the polar and non-polar orientations by using the self-consistent solution of the coupled Schrödinger and Poisson equations. Based on the transfer matrix formalism, the effects of the geometrical parameters on the current-voltage characteristics of Al0.2Ga0.8N/GaN RTDs we analyzed by varying GaN well width and Al0.2Ga0.8N/GaN barrier thicknesses. The results show that the characteristics of polar and nonpolar Al0.2Ga0.8 N/GaN RTD strongly depend on the barrier and well size; showing a strong decrease in peak and valley current density and a large PVR enhancement when increasing well and barrier thickness. To bring interesting RTD electrical characteristics, a comparison between the polar and non-polar Al0.2 Ga0.8N/GaN RTD was performed. non-polar oriented RTDs show better electronic characteristics, including higher peak current density (Jpeak), smaller peak voltage (Vpeak), and greater pic-to-valley ratio (PVR), than polar ones
{"title":"Quantum well width and barrier Thickness effects on the perpendicular transport in polar and non-polar oriented AlGaN/GaN Resonant Tunneling Diodes","authors":"Nafaa Kacem, A. Bhouri, J. Lazzari, N. Jaba","doi":"10.1109/DTS52014.2021.9498223","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498223","url":null,"abstract":"In this study, we theoretically investigated the electronic properties of resonant tunneling diodes (RTDs) grown along the polar and non-polar orientations by using the self-consistent solution of the coupled Schrödinger and Poisson equations. Based on the transfer matrix formalism, the effects of the geometrical parameters on the current-voltage characteristics of Al0.2Ga0.8N/GaN RTDs we analyzed by varying GaN well width and Al0.2Ga0.8N/GaN barrier thicknesses. The results show that the characteristics of polar and nonpolar Al0.2Ga0.8 N/GaN RTD strongly depend on the barrier and well size; showing a strong decrease in peak and valley current density and a large PVR enhancement when increasing well and barrier thickness. To bring interesting RTD electrical characteristics, a comparison between the polar and non-polar Al0.2 Ga0.8N/GaN RTD was performed. non-polar oriented RTDs show better electronic characteristics, including higher peak current density (Jpeak), smaller peak voltage (Vpeak), and greater pic-to-valley ratio (PVR), than polar ones","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116773309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/dts52014.2021.9497861
{"title":"[DTS 2021 Front cover]","authors":"","doi":"10.1109/dts52014.2021.9497861","DOIUrl":"https://doi.org/10.1109/dts52014.2021.9497861","url":null,"abstract":"","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121932069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-07DOI: 10.1109/DTS52014.2021.9498136
Alaaeddine Rjeb, H. Fathallah, M. Machhout
Orbital angular momentum (OAM) has gained a widespread interest in diverse areas especially in telecommunication due to its capability to elevate the capacity transmission and substantially improving the spectral efficiency of optical communication in optical fibers. In this work, we numerically investigate the bending impacts on OAM channels transmitted through inverse raised cosine few mode fiber (IRC-FMF). The obtained results show that the investigated OAM-Fiber supports 8 OAM data carriers, already presented in straight case, even under tight bend (i.e. at bending radius Rb=4 mm) with maximum loss of 0.0001 dB/m. This confirms the great tolerance of IRC-FMF to bending condition and confirms the capability of such fiber to handle robust OAM channels even under realistic environment.
{"title":"Analysis of Orbital Angular Momentum Channels in Bent Inverse Raised Cosine fiber","authors":"Alaaeddine Rjeb, H. Fathallah, M. Machhout","doi":"10.1109/DTS52014.2021.9498136","DOIUrl":"https://doi.org/10.1109/DTS52014.2021.9498136","url":null,"abstract":"Orbital angular momentum (OAM) has gained a widespread interest in diverse areas especially in telecommunication due to its capability to elevate the capacity transmission and substantially improving the spectral efficiency of optical communication in optical fibers. In this work, we numerically investigate the bending impacts on OAM channels transmitted through inverse raised cosine few mode fiber (IRC-FMF). The obtained results show that the investigated OAM-Fiber supports 8 OAM data carriers, already presented in straight case, even under tight bend (i.e. at bending radius Rb=4 mm) with maximum loss of 0.0001 dB/m. This confirms the great tolerance of IRC-FMF to bending condition and confirms the capability of such fiber to handle robust OAM channels even under realistic environment.","PeriodicalId":158426,"journal":{"name":"2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132109639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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