Related papers: Artificial Neural Network based Modelling for Vari…
Tunnel Field Effect Transistors (TFET) have extremely low leakage current, exhibit excellent subthreshold swing and are less susceptible to short channel effects. However, TFETs do face certain special challenges, particularly with respect…
Online training of deep neural networks (DNN) can be significantly accelerated by performing in-situ vector matrix multiplication in a crossbar array of analog memories. However, training accuracies often suffer due to device non-idealities…
In context of the universal presence of defects in additively manufactured (AM) metals, efficient computational tools are required to rapidly screen AM microstructures for mechanical integrity. To this end, a deep learning approach is used…
In this paper, an analytical predictive model of the negative capacitance (NC) effect in symmetric long channel double-gate junctionless transistor is proposed based on a charge-based model. In particular, we have investigated the effect of…
This paper reports a comprehensive study on the impacts of temperature-change, process variation, flicker noise and device aging on the inference accuracy of pre-trained all-ferroelectric (FE) FinFET deep neural networks.…
This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful…
This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained…
Reliability issues stemming from device level non-idealities of non-volatile emerging technologies like ferroelectric field-effect transistors (FeFET), especially at scaled dimensions, cause substantial degradation in the accuracy of…
In this work, we simulate the expected device performance and the scaling perspectives of Carbon nanotube Field Effect Transistors (CNT-FETs), with doped source and drain extensions. The simulations are based on the self-consistent solution…
Carbon nanotube field-effect transistors (CNT FETs) have been proposed as possible building blocks for future nano-electronics. But a challenge with CNT FETs is that they appear to randomly display varying amounts of hysteresis in their…
The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour…
Estimation of threshold voltage V T variability for NWFETs has been compu- tationally expensive due to lack of analytical models. Variability estimation of NWFET is essential to design the next generation logic circuits. Compared to any…
By developing a two-dimensional (2D) full quantum simulation, the attributes of carbon nanotube field-effect transistors (CNTFETs) in different temperatures have been comprehensively investigated. Simulations have been performed by…
This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite…
In this paper we propose a modified structure of TFET incorporating ferroelectric oxide as the complementary gate dielectric operating in negative capacitance zone, called the Negative Capacitance Tunnel FET (NCTFET). The proposed device…
Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect…
The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the…
In this paper we have developed a two dimensional (2D) analytical model for surface potential and drain current for a long channel Dual Material Gate (DMG) Silicon-on-Insulator (SOI) Tunneling Field Effect Transistor (TFET). This model…
In this work, the performance of Tunnel Field-Effect Transistors (TFETs) based on two-dimensional Transition Metal Dichalcogenide (TMD) materials is investigated by atomistic quantum transport simulations. One of the major challenges of…
High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe…