2022 IEEE VLSI Device Circuit and System (VLSI DCS)最新文献

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VLSI Routing Optimization Using Hybrid PSO Based on Reinforcement Learning 基于强化学习的混合粒子群算法的VLSI路由优化
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811434
Pradyut Nath, Sumagna Dey, Subhrapratim Nath, A. Shankar, J. Sing, Subir Kumar Sarkar
{"title":"VLSI Routing Optimization Using Hybrid PSO Based on Reinforcement Learning","authors":"Pradyut Nath, Sumagna Dey, Subhrapratim Nath, A. Shankar, J. Sing, Subir Kumar Sarkar","doi":"10.1109/VLSIDCS53788.2022.9811434","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811434","url":null,"abstract":"Rapid advances in Very Large-Scale Integration (VLSI) technology demand wire length minimization of the circuits in VLSI physical layer design to ensure routing optimization. With the growing dimension of the circuits and increased complexity, only transformation of VLSI routing problem into Non-Polynomial (NP) complete Rectilinear Minimal Spanning Tree (RMST) problem and solving it with traditional approaches results in non-optimal solutions. This brings the need for metaheuristic algorithms. Using metaheuristic algorithms, finding the optimal placement of Steiner points by approximation became easier to optimize the routing path, but sometime with major deviation. In this proposed work, A hybrid Particle swarm optimization (PSO) is used which optimizes and estimates using a value Iteration matrix, obtained using Reinforcement Learning (RL). This RL guided PSO generates much better solutions safely and with more consistency when compared with existing metaheuristic-based routing algorithms. The collected findings demonstrate that the proposed methodology has a lot of potential in VLSI routing optimization.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115692901","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}
引用次数: 0
Silicide on Oxide Based Carrier Selective Front Contact for 24% Efficient PERC Solar Cell 二氧化硅基载流子选择性前接触的24%高效PERC太阳能电池
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811447
Savita Kashyap, R. Pandey, Jaya Madan, Rajnish Sharma
{"title":"Silicide on Oxide Based Carrier Selective Front Contact for 24% Efficient PERC Solar Cell","authors":"Savita Kashyap, R. Pandey, Jaya Madan, Rajnish Sharma","doi":"10.1109/VLSIDCS53788.2022.9811447","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811447","url":null,"abstract":"Passivated emitter rear contact (PERC) based solar cells are currently regarded as a strong contender for large production in the photovoltaic (PV) industry owing to its superior light absorption properties. However, feasible fabrication of PERC devices with minimum contact recombination loss is a challenging task. Therefore, to diminish this factor, poly-silicon on oxide (POLO) as a carrier selective contact is employed in PERC device and processed through Silvaco-TCAD tool. In this proposed work, the concept of silicide electrostatically doped (ED) has been adopted to avoid the need for actual physical doping in polysilicon (poly-Si) layer on the front surface. Also, the impact of three different metal silicides such as ErSi0.25 (3 eV), ErSi0.82 (3.75 eV) and YbSi2 (3.95 eV) to induce the n-type ED region has been studied and analyzed. The overall performance of the PERC device is investigated with the help of PV parameters, EBD, current-density (J-V) curve and EQE. ED-POLO PERC solar cell reflects higher PV parameters such as short circuit current density (JSC) of 40.86 mA/cm2, open-circuit voltage (VOC) of 0.728 V, fill-factor (FF) of 80.76% and power conversion efficiency (PCE) of 24.02% at optimized silicide WF of 3 eV. The reported work of silicide in carrier selective contact-based PERC device may open a path to enhance the device performance.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127231736","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}
引用次数: 7
Multimodal Medical Imaging Using Modern Deep Learning Approaches 使用现代深度学习方法的多模态医学成像
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811498
Rahul Chanumolu, Likhita Alla, Pavankumar Chirala, Naveen Chand Chennampalli, B. Kolla
{"title":"Multimodal Medical Imaging Using Modern Deep Learning Approaches","authors":"Rahul Chanumolu, Likhita Alla, Pavankumar Chirala, Naveen Chand Chennampalli, B. Kolla","doi":"10.1109/VLSIDCS53788.2022.9811498","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811498","url":null,"abstract":"Multimodal medical imaging is gaining prominence in clinical practice as well as in research studies. Multimodal image analysis (MIA) in conjunction with ensemble learning strategies gave rise to explosion in popularity and adding special benefits for medical-related applications. Inspired by recent successes of deep learning techniques in medical imaging, we design an algorithmic structure that enables supervised MIA with Cross-Modality Fusion at preprocessing stage, the classifier level as well as the decision-making step. Using deep convolutional neural networks, we proposed an algorithm for image segmentation to determine the lesions caused by soft tissue tumors. This is done using multi-modal images by MRI tomography as well as PET. The NN built with multimodal images performs better than networks built with single-modal images. In the case of tumor segmentation, an image that is fused within the neural network (i.e., fused within the convolutional layer or totally joined layers) is more effective as compared to using pictures that fuse the network's output. This work offers specific recommendations for the development and application of MIA.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123540576","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}
引用次数: 1
A Deep CNN Framework for Distress Detection Using Facial Expression 基于面部表情的深度CNN遇险检测框架
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811487
Bikramjit Das, Debanjan Ghosh, A. Choudhuri, Ankan Goswami, Avinandan Bhakta, Mahamuda Sultana, Suman Bhattacharya
{"title":"A Deep CNN Framework for Distress Detection Using Facial Expression","authors":"Bikramjit Das, Debanjan Ghosh, A. Choudhuri, Ankan Goswami, Avinandan Bhakta, Mahamuda Sultana, Suman Bhattacharya","doi":"10.1109/VLSIDCS53788.2022.9811487","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811487","url":null,"abstract":"Recent medical developments have projected panic attacks as a forerunner in high-stress job environments. This medical cause has emerged to a considerable extent due to the masses’ lifestyle and food consumption habits. Assessment and prevention of such attacks have become imperative to arrest the situation. In this regard, the present study attempts to detect human distress from facial expressions. Convolutional Neural Networks (CNN) serves as one of the best feature extractors. AlexNet being one of the primitive CNN models, has been employed to study the stress content in a facial expression.AlexNet can perform multi-GPU training, which widely reduces the training time for larger models. Training other models comparatively require higher computations that result in escalated time and energy, which might cause consequent lesser efficiency. We have achieved a training accuracy of 93.4%, and validation accuracy of 92.5% —the image set comprised 35340 images generated from 593 video sequences from 123 people at 30fps. Although AlexNet being one of the primitive CNN models, the results of this study are motivating.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123375062","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}
引用次数: 1
Early Alzheimer’s Disease Detection Using Semi-Supervised GAN Based on Deep Learning 基于深度学习的半监督GAN早期阿尔茨海默病检测
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811458
S. Saravanakumar, T.M.Saravanan
{"title":"Early Alzheimer’s Disease Detection Using Semi-Supervised GAN Based on Deep Learning","authors":"S. Saravanakumar, T.M.Saravanan","doi":"10.1109/VLSIDCS53788.2022.9811458","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811458","url":null,"abstract":"Alzheimer’s disease (AD) prediction accuracy is crucial for minimising memory loss and enhancing Alzheimer’s disease patients’ quality of life. Neuroimaging has been explored as a possible method for diagnosing Alzheimer’s disease for the past decade. The goal of this study is to create a deep learning- an alzheimer’s disease assessment from beginning to finish ahead of schedule on. The semi-supervised generative adversarial network is designed to detect the presence of Alzheimer’s disease in magnetic resonance imaging data automatically. A model mapping is established on the original picture and Before the semi-supervised Generative Adversarial Network classifier predicts the AD, the segmented result is used to efficiently partition the left and right side hippocampal volume, and The deep feature from the segmented area is derived with convolution computational intelligence morphological operations. The current study uses the alzheimer’s disease neuroimaging Initiative dataset to conduct the experiment. This method presents a revolutionary deep learning framework for detecting alzheimer’s disease that can be used to patient data from the adult situation to improve medicine and standard of living.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965685","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}
引用次数: 5
Study of Adsorption Behaviour of Nucleobases on Si and P doped WSe2: DFT Approach 核碱基在Si和P掺杂WSe2上的吸附行为研究:DFT方法
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811470
K. Timsina, Somsher Lepcha, Bibek Chettri, P. Chettri, B. Sharma
{"title":"Study of Adsorption Behaviour of Nucleobases on Si and P doped WSe2: DFT Approach","authors":"K. Timsina, Somsher Lepcha, Bibek Chettri, P. Chettri, B. Sharma","doi":"10.1109/VLSIDCS53788.2022.9811470","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811470","url":null,"abstract":"Our study is based on the study of interaction between the nucleobase and Si and P doped WSe2 using the first principle calculation. The nucleobases used to study the adsorption properties are Adenine (A) and Cytosine (C). The adsorption energy (E<inf>ab</inf>) for different nucleobases was larger for P-WSe<inf>2</inf> than Si-WSe<inf>2</inf>. The adsorption energy of stable P-WSe<inf>2</inf> was -4.16eV and -3.79eV for A and C, respectively. This suggested the strong interaction between them. The band gap and density of state (DOS) were calculated to understand the electrical behaviour during the interaction. The band gap also decreased after interaction with C and A. Recovery time of Si-WSe<inf>2</inf> was longer as compared to P-WSe<inf>2</inf>. P-WSe<inf>2</inf> has the shortest recovery time of 2.5×10<sup>-65</sup> sec for A molecule. All these results suggested that Si-WSe<inf>2</inf> could be used as a sensing material to detect biomolecules. Also, potential of Si-WSe<inf>2</inf> and P-WSe<inf>2</inf> in distinguishing nucleobase types could be explored further for their use as a biosensing substrate in DNA sequencing.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132215094","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}
引用次数: 1
State of the Art and Future Perspectives in III-V Nanometer-Scale MOSFETs III-V纳米mosfet的研究现状与展望
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811490
S. Dey, Kalyan Biswas, A. Sarkar
{"title":"State of the Art and Future Perspectives in III-V Nanometer-Scale MOSFETs","authors":"S. Dey, Kalyan Biswas, A. Sarkar","doi":"10.1109/VLSIDCS53788.2022.9811490","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811490","url":null,"abstract":"The downscaling of transistors being the biggest challenge in terms of gate length in nanometer scale faces hindrances due to short channel effects and gate leakage current and thus becomes a threat against continuation of Moor’s law making further shrinking of silicon transistors is of no use. Here in this paper some of the recent advances and challenges to overcome have been reviewed of InGaAs MOSFETs. Analog, RF responses of III-V MOSFETs along with possible future of MOSFET architectures using InGaAs as channel material are also being demonstrated. Evolution of III-V FinFETs also reviewed. Scaling should be done to maximize ION at an acceptable level of IOFF. From analyzing the published results it can be easily acknowledged that InGaAs can significantly surpass silicon channel of equal gate length by making a small tradeoff between device performance and short channel effects.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130789396","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}
引用次数: 0
Linearity Performance of Double Metal Negative Capacitance Field-Effect Transistors: A Numerical Study 双金属负电容场效应晶体管线性性能的数值研究
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811468
Yash Pathak, B. Malhotra, R. Chaujar
{"title":"Linearity Performance of Double Metal Negative Capacitance Field-Effect Transistors: A Numerical Study","authors":"Yash Pathak, B. Malhotra, R. Chaujar","doi":"10.1109/VLSIDCS53788.2022.9811468","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811468","url":null,"abstract":"In that work, we examined DM NCFETs (double metal negative capacitance field effect transistors) at better Linearity application in differentiate to the conventional device NCFETs through a recent thought of ferroelectric thick sheet (HfO2FE). Simulator of Visual TCAD is utilised to model NCFETs and DM NCFETs. The outcomes of DM NCFETs have been examined in the shape of transconductance (Gm) enhanced by 9.0%, Ioff (leakage current) decreased by 47.0% over NCFETs, and enhanced execution of Gd (output conductance), switching ratio Ion / Ioff, drain barrier induced lowering (DIBL) lowered by 6.0% over NCFETs, subthreshold swing (SS) declined by 5.0% over NCFETs. We examined also the linearity parameters such as intrinsic delay (Ti), second and third order of voltage intercept point (VIP2 and VIP3), third order inter modulation distortion (IMD3), third-order input intercept point (IIP3). Therefore, DM NCFETs could be applicable in digital and analog, linearity parameter circuit applications.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131008611","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}
引用次数: 1
Smart ECG Monitoring and Analysis System Using Machine Learning 基于机器学习的智能心电监测与分析系统
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/vlsidcs53788.2022.9811433
SarabTej Singh, Megha Bhushan
{"title":"Smart ECG Monitoring and Analysis System Using Machine Learning","authors":"SarabTej Singh, Megha Bhushan","doi":"10.1109/vlsidcs53788.2022.9811433","DOIUrl":"https://doi.org/10.1109/vlsidcs53788.2022.9811433","url":null,"abstract":"In critical situations, a patient quarantines himself from others which makes it difficult for a doctor to monitor the acute health symptoms of a patient. This work aims to create a Smart Electrocardiogram monitoring and analysis system. It works intelligently using machine learning for the detection of heart disease. It enables doctors to monitor the patient remotely from a developed Django Web application. The patient will be able to record and transfer the Electrocardiogram (ECG) data to the doctor or any family members remotely which can detect heart disease. Also, it will help the people living in remote areas, lacking a proper infrastructure but can be monitored at low cost by a doctor easily. Therefore, in case of emergency situations patients can be saved by real time monitoring.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664092","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}
引用次数: 13
Machine Learning-Based Intrusion Detection System For Healthcare Data 基于机器学习的医疗数据入侵检测系统
2022 IEEE VLSI Device Circuit and System (VLSI DCS) Pub Date : 2022-02-26 DOI: 10.1109/VLSIDCS53788.2022.9811465
Amit Kumar Balyan, S. Ahuja, S. K. Sharma, U. Lilhore
{"title":"Machine Learning-Based Intrusion Detection System For Healthcare Data","authors":"Amit Kumar Balyan, S. Ahuja, S. K. Sharma, U. Lilhore","doi":"10.1109/VLSIDCS53788.2022.9811465","DOIUrl":"https://doi.org/10.1109/VLSIDCS53788.2022.9811465","url":null,"abstract":"The rising advancement of intrusion strategies has given the desperate imperative for designing and developing IDS with excellent efficiency. The existing IDS have been developed to utilize obsolete threat datasets, concentrating too much on accuracy rate and less on prediction. Machine learning has the potential to deliver an efficient approach when it arrives at intrusion detection due to the high dimensionality and eminent dynamic nature of the available data in such mechanisms. However, plenty of the existing health care IDS either uses dynamic network performance measures or clients' biometric information to establish their database. This research introduced a NIDS for health care data using the Hybrid Feature Selection algorithm (Least Squares and Support Vector Machine), which minimizes forecast latency without influencing attack prediction efficiency by reducing the IDS complexity. The experimental results demonstrate the performance of the proposed hybrid method over the existing method in terms of precision, accuracy, recall, and F-measures.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"451 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125783616","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}
引用次数: 6
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