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access icon free Multiple memristor series–parallel connections with use in synaptic circuit design

  • Author(s): Li Luo 1 ; Xiaofang Hu 2 ; Shukai Duan 1 ; Zhekang Dong 3 ; Lidan Wang 1
    • View affiliations
    • Affiliations: 1: College of Electronics and Information Engineering, Southwest University, Chongqing 400715, People's Republic of China ;
      2: School of Computer and Information Science, Southwest University, Chongqing 400715, People's Republic of China ;
      3: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China
  • Source: Volume 11, Issue 2, March 2017, p. 123 – 134
    DOI:  10.1049/iet-cds.2015.0357 , Print ISSN 1751-858X, Online ISSN 1751-8598
© The Institution of Engineering and Technology
Received 02/12/2015, Accepted 19/04/2016, Revised 18/04/2016, Published 01/03/2017

With the increase of research interest on memristors, various single or multiple memristor configurations have been integrated with advanced complementary metal–oxide–semiconducor technology, which promises efficient implementations of synaptic connections in neuromorphic computing systems, or computing elements in signal processing systems. In this study, multiple memristors, both in series and parallel connections, and their characteristics are further studied including the transient behaviours when asynchronous change happens and the composite electric properties in steady state etc. Particularly, the specific conditions to reach steady state and produce composite memristive effects are presented in detail. Furthermore, several synaptic memristor circuits based on series and parallel connections are also discussed.

Inspec keywords: neural chips; CMOS integrated circuits; memristor circuits

Other keywords: neuromorphic computing systems; memristor series–parallel connections; signal processing systems; synaptic circuit design; complementary metal oxide semiconducor technology; synaptic memristor circuits

Subjects: Neural nets (circuit implementations); Neural net devices; Neural computing techniques; Resistors; CMOS integrated circuits

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