Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

BP neural network based GFET modeling method

A technology of BP neural network and modeling method, which is applied in the field of modeling of graphene field effect transistors, can solve the problems of long operation time, short calculation time, inability to apply circuit simulation tools, etc., and achieve the effect of fast calculation

Inactive Publication Date: 2015-09-16
WUHAN UNIV
View PDF3 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Numerical calculations based on first principles or non-equilibrium Green's functions, numerical models can accurately simulate the characteristics of GFETs, but the calculation time is too long to be applied to circuit simulation tools
The intensive model uses analytical expressions to describe the characteristics of GFETs, and the calculation time is short. It can be integrated into circuit simulation tools for circuit design, but it requires a deep understanding of the working mechanism of GFETs and simplifies them into appropriate analytical expressions. Often difficult to do with newer GFET devices

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • BP neural network based GFET modeling method
  • BP neural network based GFET modeling method
  • BP neural network based GFET modeling method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052]In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0053] A kind of GFET modeling method based on BP neural network provided by the invention comprises the following steps:

[0054] Step 1. Data collection: collect a certain amount of GFET input and output data for BP network model training and testing. The network model obtained after training and testing can be used to predict the performance of GFET under other input parameters. The input data includes the gate-source voltage V gs , drain-source voltage V ds , channel width W, channel length L, the output data is the channel current I d ; After the data...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention discloses a BP neural network based GFET modeling method. The method comprises six steps of: collecting data; preprocessing the data; determining a BP network GFET model structure to be used; building and training a GFET model; performing denormalization on an output electric current predicted by the GFET model to obtain a model output value; and implementing Verilog-A of the GFET mode. The method of the present invention has the advantages of being short in calculation time, high in accuracy, requiring no complete theory knowledge of devices and being applicable to circuit design and simulation.

Description

technical field [0001] The invention belongs to the technical field of device modeling, in particular to a modeling method of a graphene field effect transistor (GFET). Background technique [0002] The device model is a tool to describe the performance of the device. To apply new devices to circuit design on a large scale, an accurate device model is essential. Traditional device models are mainly numerical models and intensive models. [0003] Based on first-principle or non-equilibrium Green's function numerical calculations, the numerical model can accurately simulate the characteristics of GFETs, but the calculation time is too long to be applied to circuit simulation tools. The intensive model uses analytical expressions to describe the characteristics of GFETs, and the calculation time is short. It can be integrated into circuit simulation tools for circuit design, but it requires a deep understanding of the working mechanism of GFETs and simplifies them into appropr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/50G06N3/02
Inventor 常胜张济
Owner WUHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com