Journal of Computational Electronics

, Volume 15, Issue 3, pp 911–918 | Cite as

Prior knowledge input neural network method for GFET description

  • Ji Zhang
  • Yawei Lv
  • Sheng Chang
  • Hao Wang
  • Jin He
  • Qijun Huang


For circuit design, various compact models for graphene field-effect transistors (GFETs) have been developed. However, the consistency between them is poor, since study on the mechanism of operation of GFETs is still immature and the models were derived based on different understandings. Herein, we propose another approach for circuit-level description of GFETs based on a prior knowledge input neural network modeling method. By virtue of the neural network’s learning ability, it can accurately describe different GFETs without an exact description of their mechanism of operation. Basic knowledge on GFETs and an adaptive genetic algorithm are employed to improve the precision of the neural network, resulting in performance significantly exceeding that of a traditional, multilayer perceptron network. The universality of the method is verified by detailed tests using two different datasets. Its applicability for circuit design is demonstrated by relevant circuit simulations in a Verilog-A implementation.


Genetic algorithm Graphene field-effect transistors Neural network Prior knowledge Verilog-A 



This work was supported by the National Natural Science Foundation of China (61204096, 61404094, and 61574102), the Fundamental Research Fund for the Central Universities, Wuhan University (2042014kf0238 and 2042015kf0174), the China Postdoctoral Science Foundation (2012T50688), the Natural Science Foundation of Hubei Province, China (2014CFB694), and the Science Foundation of Jiangsu Province, China (BK20141218).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ji Zhang
    • 1
  • Yawei Lv
    • 1
  • Sheng Chang
    • 1
  • Hao Wang
    • 1
  • Jin He
    • 1
  • Qijun Huang
    • 1
  1. 1.Department of Electronics Science and Technology, School of Physics and TechnologyWuhan UniversityWuhanPeople’s Republic of China

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