Pub Date : 2023-05-04DOI: 10.1109/ESDC56251.2023.10149857
Ravindra Kumar Maurya, Vivek Kumar, R. Saha, B. Bhowmick
In this paper, the temperature effect on Metal-Ferroelectric-Insulator-Semiconductor (MFIS) structured negative capacitance fin field effect transistor (NC-FinFET) Silicon doped HfO2 (Si:HfO2) is analyzed. The simulation is carried out in Sentaurus TCAD and various characteristics are extracted. With the incorporation of FE layer, the ION is increased by 1.5 times compared to baseline FinFET and SS is achieved as 53 mV/dec. The device provides a high transconductance (gm) of 5 mS at Vgs = 0.95 V. These parameters viz. SS, ION and gm etc. has been analyzed with varying temperature (250 K - 350 K with step 50 K).
{"title":"Effects of Ferro-thickness and Temperature on Electrical Performance of Si:HfO2 based NC-FinFET","authors":"Ravindra Kumar Maurya, Vivek Kumar, R. Saha, B. Bhowmick","doi":"10.1109/ESDC56251.2023.10149857","DOIUrl":"https://doi.org/10.1109/ESDC56251.2023.10149857","url":null,"abstract":"In this paper, the temperature effect on Metal-Ferroelectric-Insulator-Semiconductor (MFIS) structured negative capacitance fin field effect transistor (NC-FinFET) Silicon doped HfO2 (Si:HfO2) is analyzed. The simulation is carried out in Sentaurus TCAD and various characteristics are extracted. With the incorporation of FE layer, the ION is increased by 1.5 times compared to baseline FinFET and SS is achieved as 53 mV/dec. The device provides a high transconductance (gm) of 5 mS at Vgs = 0.95 V. These parameters viz. SS, ION and gm etc. has been analyzed with varying temperature (250 K - 350 K with step 50 K).","PeriodicalId":354855,"journal":{"name":"2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127605422","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 : 2023-05-04DOI: 10.1109/ESDC56251.2023.10149858
Ashwini Tangade, Ashish Kumar Verma, Narayana Darapaneni, Y. Harika, Prasanna, Anwesh Reddy Paduri, Srinath Ram Shankar, Ravi Sadalagi
In this study, we suggest a unique method for text summarization that combines the TextRank algorithm, Kmeans clustering, and neural network classification. To determine which phrases in a given text are most crucial, the basic model uses TextRank, a graph-based algorithm. In order to group together comparable sentences, these sentences are subsequently clustered using K-means. The best representative statement from each cluster is chosen as the final summary in the last phase of our method using neural network classification. In order to enhance TextRank’s functionality, we also suggest an optimization strategy called cosine similarity with TextRank (Cosim-TextRank). In order to further improve the model’s accuracy, we also suggest using weighted cosine similarity. Overall, our method successfully creates a summary of the text by choosing significant and illustrative phrases while maintaining the context and content of the original text. The experimental findings demonstrate that, in terms of ROUGE scores and human evaluation, our suggested strategy performs better than the current state-of-the-art methods.
{"title":"The Power of Pre-trained Transformers for Extractive Text Summarization: An Innovative Approach","authors":"Ashwini Tangade, Ashish Kumar Verma, Narayana Darapaneni, Y. Harika, Prasanna, Anwesh Reddy Paduri, Srinath Ram Shankar, Ravi Sadalagi","doi":"10.1109/ESDC56251.2023.10149858","DOIUrl":"https://doi.org/10.1109/ESDC56251.2023.10149858","url":null,"abstract":"In this study, we suggest a unique method for text summarization that combines the TextRank algorithm, Kmeans clustering, and neural network classification. To determine which phrases in a given text are most crucial, the basic model uses TextRank, a graph-based algorithm. In order to group together comparable sentences, these sentences are subsequently clustered using K-means. The best representative statement from each cluster is chosen as the final summary in the last phase of our method using neural network classification. In order to enhance TextRank’s functionality, we also suggest an optimization strategy called cosine similarity with TextRank (Cosim-TextRank). In order to further improve the model’s accuracy, we also suggest using weighted cosine similarity. Overall, our method successfully creates a summary of the text by choosing significant and illustrative phrases while maintaining the context and content of the original text. The experimental findings demonstrate that, in terms of ROUGE scores and human evaluation, our suggested strategy performs better than the current state-of-the-art methods.","PeriodicalId":354855,"journal":{"name":"2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133093796","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}
In this article, we have studied the effects of gamma and heavy ion radiation on 5nm stacked nanosheet FET and Fork-sheet FET and analyzed the impact of radiation on circuitlevel characteristics. These analyses are carried out by using Three-Dimensional Technology Computer-Aided Design (3-D TCAD) simulations. By exposing gamma rays and heavy ion, the performance in terms of charge generation rate of nanosheet FET and Fork-sheet are investigated. Gamma particle and heavy-ion impacts are studied at the device and circuit levels. The results of Fork-sheet FET are compared with the results of gate-all-around nanosheet FET. After comparison, we found that radiation has a stronger influence on Fork-sheet than Nanosheet.
{"title":"TCAD-Based Analysis of Nanosheet and Forksheet FET Electrical Characteristics in the Presence of Gamma and Heavy Ion Radiation","authors":"Nischal Anand, Rohit Rai, Yashvi Verma, Amit Kumar Singh Chauhan, Deepak Kumar Sharma, Vivek Kumar","doi":"10.1109/ESDC56251.2023.10149855","DOIUrl":"https://doi.org/10.1109/ESDC56251.2023.10149855","url":null,"abstract":"In this article, we have studied the effects of gamma and heavy ion radiation on 5nm stacked nanosheet FET and Fork-sheet FET and analyzed the impact of radiation on circuitlevel characteristics. These analyses are carried out by using Three-Dimensional Technology Computer-Aided Design (3-D TCAD) simulations. By exposing gamma rays and heavy ion, the performance in terms of charge generation rate of nanosheet FET and Fork-sheet are investigated. Gamma particle and heavy-ion impacts are studied at the device and circuit levels. The results of Fork-sheet FET are compared with the results of gate-all-around nanosheet FET. After comparison, we found that radiation has a stronger influence on Fork-sheet than Nanosheet.","PeriodicalId":354855,"journal":{"name":"2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126446052","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 : 2023-05-04DOI: 10.1109/ESDC56251.2023.10149877
T. Dharani, Medikonda Padma Prasamsa, B. Sirisha, Jorige Bala Vivek, Battina Harsha Vardhan
One of the most common eye diseases in the people aged between 20-74 years is Diabetic Retinopathy (DR). DR is an eye complication where the patient loses his vision due to an increase in glucose levels in the blood. DR is most prominent in the patients who are diagnosed with the diabetes disease. Over one-third of the diabetic mellitus patients are diagnosed with DR. For diagnosing DR, the patient has to visit an ophthalmologist for dilated eye examination. However, everyone cannot have this facility. Hence, there is a need for a simple automated software for diagnosing the five stages of DR efficiently. In this paper, a simple model is developed using the Kaggle APTOS Blindness Detection dataset which is publicly available. In the pre-processing step the images are enhanced and the deep learning model ResNet152 architecture is used for the classification step. After training, the ReseNet152 model yielded a training and validation loss of 0.073 and 0.107 respectively and validation accuracy of 0.97. Further, a simple Graphical User Interface is developed using tkinter framework in python standard library which classifies the given input (a) (b) (c) fundus image as one of the five stages of DR.
{"title":"Diabetic Retinopathy classification through fundus images using Deep Learning","authors":"T. Dharani, Medikonda Padma Prasamsa, B. Sirisha, Jorige Bala Vivek, Battina Harsha Vardhan","doi":"10.1109/ESDC56251.2023.10149877","DOIUrl":"https://doi.org/10.1109/ESDC56251.2023.10149877","url":null,"abstract":"One of the most common eye diseases in the people aged between 20-74 years is Diabetic Retinopathy (DR). DR is an eye complication where the patient loses his vision due to an increase in glucose levels in the blood. DR is most prominent in the patients who are diagnosed with the diabetes disease. Over one-third of the diabetic mellitus patients are diagnosed with DR. For diagnosing DR, the patient has to visit an ophthalmologist for dilated eye examination. However, everyone cannot have this facility. Hence, there is a need for a simple automated software for diagnosing the five stages of DR efficiently. In this paper, a simple model is developed using the Kaggle APTOS Blindness Detection dataset which is publicly available. In the pre-processing step the images are enhanced and the deep learning model ResNet152 architecture is used for the classification step. After training, the ReseNet152 model yielded a training and validation loss of 0.073 and 0.107 respectively and validation accuracy of 0.97. Further, a simple Graphical User Interface is developed using tkinter framework in python standard library which classifies the given input (a) (b) (c) fundus image as one of the five stages of DR.","PeriodicalId":354855,"journal":{"name":"2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125508056","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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