Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

Akbar, Chandni; Li, Yiming; Thoti, Narasimhulu

doi:10.1109/access.2022.3174685

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…The main application in this design is neural network (as shown in Figure 2). Its basic principle is to imitate, remember and analyze the structure of human cerebral cortex and establish a model combining other relevant knowledge, Then, according to the input signal, a series of theories on individual behavior characteristics are formed by identifying the connection relationship between potential neurons in the sample and the response mechanism of different types of nervous systems to external stimuli, and then a set of research and development work required in the learning and stress disorder training system is established [17][18].…”

Section: Figure 2 Cerebellar Model Arithmetic Computermentioning

confidence: 99%

Machine Learning in the Recognition and Prognosis Prediction of Children's Post-traumatic Stress Disorder

2020

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With the development of society, diseases have also become a topic of concern, and children's post-traumatic stress disorder has attracted more attention. This paper mainly analyzes the current situation of research on nerve protection and human brain injury at home and abroad, and designs a protection system based on machine learning model in combination with the algorithm used in this topic. First, it introduces some related concepts and theoretical foundations of this model, and studies its application in the recognition of children's post-traumatic stress disorder and prognosis prediction. Secondly, the characteristics and advantages of recognition of children's post-traumatic stress defects, problems in use; evaluation difficulties and other aspects are obtained through experimental analysis. Finally, a sample test based on training classification diagram is proposed to solve these problems with Bayesian algorithm, and the results are compared to verify its accuracy. The verification results show that the recognition rate of children's post-traumatic stress disorder in this model is very high. The prediction time and recognition time of stress disorder are very fast, which meets the recognition needs of users.

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Section: Figure 2 Cerebellar Model Arithmetic Computermentioning

confidence: 99%

Machine Learning in the Recognition and Prognosis Prediction of Children's Post-traumatic Stress Disorder

2020

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A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Salah,

Yevick

2024

Advcd Theory and Sims

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This paper presents a Random Forest (RF) machine learning model that relates the DC characteristics and high‐frequency response of a carbon nanotube (CNT) tunnel field‐effect transistor (TFET) with highly doped pockets to the transistor parameters. The analysis of multiple factors for a complex structure as the one studied here becomes expensive with the ordinary simulation techniques and hence machine learning (ML) offers a proficient method to model and enhance the understanding of the key factors that influence the CNT TFET with pockets in considerably reduced time. Numerical simulations are used to generate the data on which the model is trained. This dataset comprises ten input features and four output attributes. The tuned model is capable of predicting the output characteristics of the device with minimal mean squared error (MSE). The RF model is also compared to other ML algorithms to demonstrate its advantage.

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A Perspective View of Silicon Based Classical to Non-Classical MOS Transistors and their Extension in Machine Learning

Singh¹,

Mishra²,

Akhter³

2023

Silicon

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Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

Cited by 4 publications

References 32 publications

Machine Learning in the Recognition and Prognosis Prediction of Children's Post-traumatic Stress Disorder

Machine Learning in the Recognition and Prognosis Prediction of Children's Post-traumatic Stress Disorder

A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

A Perspective View of Silicon Based Classical to Non-Classical MOS Transistors and their Extension in Machine Learning

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